
Recurrence Patterns and Patient Outcomes in Resected Lung Adenocarcinoma Differ according to Ground-Glass Opacity at CT.
Radiology
Background Although lung adenocarcinoma with ground-glass opacity (GGO) is known to have distinct characteristics, limited data exist on whether the recurrence pattern and outcomes in patients with resected lung adenocarcinoma differ according to GGO presence at CT. Purpose To examine recurrence patterns and associations with outcomes in patients with resected lung adenocarcinoma according to GGO at CT. Materials and Methods Patients who underwent CT followed by lobectomy or pneumonectomy for lung adenocarcinoma between July 2010 and December 2017 were retrospectively included. Patients were divided into two groups based on the presence of GGO: GGO adenocarcinoma and solid adenocarcinoma. Recurrence patterns at follow-up CT examinations were investigated and compared between the two groups. The effects of patient grouping on time to recurrence, postrecurrence survival (PRS), and overall survival (OS) were evaluated using Cox regression. Results Of 1019 patients (mean age, 62 years ± 9 [SD]; 520 women), 487 had GGO adenocarcinoma and 532 had solid adenocarcinoma. Recurrences occurred more frequently in patients with solid adenocarcinoma (36.1% [192 of 532 patients]) than in those with GGO adenocarcinoma (16.2% [79 of 487 patients]). Distant metastasis was the most common mode of recurrence in the group with solid adenocarcinoma and all clinical stages. In clinical stage I GGO adenocarcinoma, all regional recurrences appeared as ipsilateral lung metastasis (39.2% [20 of 51]) without regional lymph node metastasis. Brain metastasis was more frequent in patients with clinical stage I solid adenocarcinoma (16.5% [16 of 97 patients]). The presence of GGO was associated with time to recurrence and OS (adjusted hazard ratio [HR], 0.6 [ < .001] for both). Recurrence pattern was an independent risk factor for PRS (adjusted HR, 2.1 for distant metastasis [ < .001] and 3.9 for brain metastasis [ < .001], with local-regional recurrence as the reference). Conclusion Recurrence patterns, time to recurrence, and overall survival differed between patients with and without ground-glass opacity at CT, and recurrence patterns were associated with postrecurrence survival. © RSNA, 2023
10.1148/radiol.222422
Changes in quantitative CT image features of ground-glass nodules in differentiating invasive pulmonary adenocarcinoma from benign and in situ lesions: histopathological comparisons.
Zhang Y P,Heuvelmans M A,Zhang H,Oudkerk M,Zhang G X,Xie X Q
Clinical radiology
AIM:To evaluate progressive changes in quantitative CT features of the non-solid component of ground-glass nodules (GGNs) from baseline to follow-up to differentiate invasive (minimally invasive adenocarcinoma [MIA] and invasive adenocarcinoma [IA]) GGNs from benign or pre-invasive (adenocarcinoma in situ [AIS]) lesions. MATERIALS AND METHODS:Patients with a GGN detected at baseline and follow-up computed tomography (CT), examined by tissue sampling were included in the study. The diameter and mean, maximum, minimum CT density and density deviation from the non-solid component of whole GGNs were measured. Progression of these features over time was analysed by linear regression analysis. Multivariate receiver operating characteristics analyses of combined measures created by a logistic regression model were performed to evaluate diagnostic performance for invasive GGNs. RESULTS:Sixty-one patients (24 males) with 70 GGNs were included. Fifteen GGNs were benign, six AIS, 38 MIA, and 11 IA. The mean diameter of all histological subtypes increased from baseline to follow-up, the largest increase was found in the MIA group (p<0.001). For MIA and IA, the mean, maximum, minimum density, and density deviation had a positive correlation over time, whilst benign and pre-invasive GGNs showed a negative correlation for these features. A diagnostic model based on three GGN features (increasing in diameter, mean density, and density deviation) identified invasive GGNs with a sensitivity, specificity and area under the receiver operating characteristic (ROC) curve (AUC) of 83.7%, 61.9%, and 0.786, respectively (p<0.001). CONCLUSION:In GGN follow-up, the diameter of benign and AIS, and invasive GGNs significantly increased. Additional analysis of mean density and density deviation in the non-solid component may help to identify invasive GGNs.
10.1016/j.crad.2017.12.011
Consolidation volume and integration of computed tomography values on three-dimensional computed tomography may predict pathological invasiveness in early lung adenocarcinoma.
Saeki Yusuke,Kitazawa Shinsuke,Yanagihara Takahiro,Kobayashi Naohiro,Kikuchi Shinji,Goto Yukinobu,Ichimura Hideo,Sato Yukio
Surgery today
PURPOSE:To investigate the relationship between three-dimensional computed tomography (3D-CT) findings and pathological invasiveness in lung adenocarcinoma. METHODS:We retrospectively evaluated 95 patients who underwent surgical resection of lung adenocarcinoma of ≤ 20 mm. The diameters, volumes, and CT values of tumor consolidation were analyzed. We defined the modified CT value by setting air as 0 and water as 1000 and assumed a correlation with pathological invasiveness. Pre-invasive lesions and minimally invasive adenocarcinomas were classified as non-invasive adenocarcinoma. We compared the clinico-radiological features with pathological invasiveness. Receiver operator characteristic (ROC) curves and recurrence-free survival curves were constructed. RESULTS:Twenty-six non-invasive adenocarcinomas and 69 invasive adenocarcinomas were evaluated. The multivariate analysis revealed that the consolidation volume and the integration of modified CT values were the most important predictors of pathological invasion. The area under the ROC curve and the cut-off values of the consolidation volume were 0.868 and 75 mm, respectively. The area under the ROC curve and the cut-off values of the integration of modified CT values were 0.871 and 80,000, respectively. There was no recurrence in cases with values below the cut-off across all parameters. CONCLUSION:The consolidation volume and integration of modified CT values were shown to be highly predictive of pathological invasiveness.
10.1007/s00595-021-02231-7
Contrast analysis of the relationship between the HRCT sign and new pathologic classification in small ground glass nodule-like lung adenocarcinoma.
Meng You,Liu Chen-Lu,Cai Qing,Shen Yu-Ying,Chen Shuang-Qing
La Radiologia medica
PURPOSE:To perform contrast analysis of the relationship between high-resolution computed tomography (HRCT) signs and new pathologic classification of small GGNs-like lung adenocarcinoma. MATERIALS AND METHODS:The HRCT data from 145 pathologically confirmed cases of small GGNs of lung adenocarcinoma were analysed retrospectively. The 145 cases of GGNs were divided into pre-invasive (PI) group (n = 46), micro-invasive adenocarcinoma (MIA) group (n = 48), and invasive adenocarcinoma (IAC) group (n = 51). HRCT imaging sign of GGNs in each group was assessed and compared. RESULTS:Significant differences in GGN size were found among the three groups (P < 0.05). The presence of a tumour-lung interface in the MIA and IAC groups was significantly higher than that in the PI group (P < 0.05), but no significant difference was found between the MIA and IAC groups. The presence of a pleural indentation sign in the IAC group was significantly higher than that in the other two groups (P < 0.05), but no significant difference was noted between the latter two groups. Significant differences were found in the lobulated and spicule signs among the three groups (P < 0.05). The presence of a microvascular sign in the MIA and IAC groups was significantly higher than that in the PI group (P < 0.05). No significant difference was found in the GGN density, vacuole sign, air bronchus sign and notch sign among the three groups. CONCLUSIONS:The HRCT signs of GGNs could be used to differentiate among pre-invasive lesions, micro-invasive lesions and invasive lung adenocarcinoma.
10.1007/s11547-018-0936-x
Preoperative CT-based radiomics combined with intraoperative frozen section is predictive of invasive adenocarcinoma in pulmonary nodules: a multicenter study.
Wu Guangyao,Woodruff Henry C,Sanduleanu Sebastian,Refaee Turkey,Jochems Arthur,Leijenaar Ralph,Gietema Hester,Shen Jing,Wang Rui,Xiong Jingtong,Bian Jie,Wu Jianlin,Lambin Philippe
European radiology
OBJECTIVES:Develop a CT-based radiomics model and combine it with frozen section (FS) and clinical data to distinguish invasive adenocarcinomas (IA) from preinvasive lesions/minimally invasive adenocarcinomas (PM). METHODS:This multicenter study cohort of 623 lung adenocarcinomas was split into training (n = 331), testing (n = 143), and external validation dataset (n = 149). Random forest models were built using selected radiomics features, results from FS, lesion volume, clinical and semantic features, and combinations thereof. The area under the receiver operator characteristic curves (AUC) was used to evaluate model performances. The diagnosis accuracy, calibration, and decision curves of models were tested. RESULTS:The radiomics-based model shows good predictive performance and diagnostic accuracy for distinguishing IA from PM, with AUCs of 0.89, 0.89, and 0.88, in the training, testing, and validation datasets, respectively, and with corresponding accuracies of 0.82, 0.79, and 0.85. Adding lesion volume and FS significantly increases the performance of the model with AUCs of 0.96, 0.97, and 0.96, and with accuracies of 0.91, 0.94, and 0.93 in the three datasets. There is no significant difference in AUC between the FS model enriched with radiomics and volume against an FS model enriched with volume alone, while the former has higher accuracy. The model combining all available information shows minor non-significant improvements in AUC and accuracy compared with an FS model enriched with radiomics and volume. CONCLUSIONS:Radiomics signatures are potential biomarkers for the risk of IA, especially in combination with FS, and could help guide surgical strategy for pulmonary nodules patients. KEY POINTS:• A CT-based radiomics model may be a valuable tool for preoperative prediction of invasive adenocarcinoma for patients with pulmonary nodules. • Radiomics combined with frozen sections could help in guiding surgery strategy for patients with pulmonary nodules.
10.1007/s00330-019-06597-8
Ground-glass opacity nodules: histopathology, imaging evaluation, and clinical implications.
Lee Ho Yun,Lee Kyung Soo
Journal of thoracic imaging
Ground-glass opacity (GGO) nodules noted at thin-section computed tomography (CT) scan have been shown to have a histopathologic relationship with atypical adenomatous hyperplasia, bronchioloalveolar carcinoma (BAC, or adenocarcinoma in situ), and adenocarcinoma with a predominant BAC component (minimally invasive adenocarcinoma). Patients harboring GGO nodules of BAC or adenocarcinoma with a predominant BAC component demonstrate negative results for malignancy at positron emission tomography. In peripheral adenocarcinoma of a part-solid (mixed GGO and solid attenuation) nodular nature, both the degree of disappearance of GGO area, when the lung window is changed to a mediastinal window image at CT scanning, and the maximum standardized uptake value at positron emission tomography correlate well with histopathologic BAC and non-BAC ratios. However, a high non-BAC ratio appears to be the only independent prognosis-determining factor. Epidermal growth factor receptor mutations are positively correlated with the GGO ratio at a thin-section CT scan in lung adenocarcinomas. As patients with a GGO nodule of BAC or adenocarcinoma with a predominant BAC component have a good prognosis, a wedge resection is recommended as a treatment option, in preference to lobectomy. Even for multiple malignant pure GGO nodules, minimally invasive surgery (including multiple resections) with the preservation of lung volume and adequate imaging follow-up studies are the recommended diagnostic and therapeutic measures.
10.1097/RTI.0b013e3181fbaa64
Pathological features and prognostic implications of ground-glass opacity components on computed tomography for clinical stage I lung adenocarcinoma.
Katsumata Shinya,Aokage Keiju,Ishii Genichiro,Hoshino Hironobu,Suzuki Jun,Miyoshi Tomohiro,Tane Kenta,Samejima Joji,Tsuboi Masahiro
Surgery today
PURPOSE:To investigate the prognostic implications and pathological features of clinical stage I lung adenocarcinoma with ground-glass opacity (GGO) on computed tomography (CT). METHODS:The subjects of this retrospective study were 1228 patients with lung adenocarcinoma classified as clinical stage I, who underwent complete resection by lobectomy. The patients were divided into four groups based on the presence and proportion of GGO according to the consolidation-to-tumor ratio (CTR); A, CTR ≤ 0.5; B, 0.5 < CTR ≤ 0.75; C, 0.75 < CTR ≤ 1.0 with GGO; D, without GGO (pure-solid). We compared overall survival, pathological findings (N/ly/v/STAS), and histological subtypes within each clinical stage among the four groups. RESULTS:We found no significant differences among tumors with GGO (groups A, B and C) for prognosis or pathological findings in all the clinical stages. The prognoses of groups A, B and C were significantly better than that of group D for patients with clinical stages IA2-IB disease. Tumors without GGO on CT had a significantly larger number of positive N, ly, v and STAS in almost all stages than tumors with GGO on CT. Tumors without GGO on CT had significantly more solid predominant and less lepidic predominant adenocarcinoma. CONCLUSION:Not the proportion of GGO, but its presence on CT, as well as the size of the solid component, were correlated significantly with pathological characteristics and survival.
10.1007/s00595-021-02235-3
Clinical T categorization in stage IA lung adenocarcinomas: prognostic implications of CT display window settings for solid portion measurement.
Kim Hyungjin,Goo Jin Mo,Kim Young Tae,Park Chang Min
European radiology
OBJECTIVES:Our study aimed at evaluating the prognostic implications of lung and mediastinal CT display window settings for solid portion measurements on the eighth-edition lung cancer staging system's clinical T (cT) categorization. METHODS:We retrospectively analyzed 691 surgically treated patients from 2009 to 2015 for clinical stage IA lung adenocarcinomas. Solid portions were measured at the lung and mediastinal window settings, respectively, and cT categories were determined for each measurement (cT and cT). The prognostic power of the two cT factors for disease-free survival (DFS) was assessed using Cox regression, and concordance indices (C-indices) were compared using the Student t test. Subsequently, the patients were split into training and validation cohorts to calculate optimal cutoffs for the cT categorization of mediastinal window-based solid portions (cT) and validate its prognostic performance. RESULTS:Both cT ((cT1b: adjusted HR, 3.547; p = 0.017), (cT1c: adjusted HR, 9.439; p < 0.001)) and cT ((cT1b: adjusted HR, 4.635; p < 0.001), (cT1c: adjusted HR, 11.235; p < 0.001)) were significantly associated with DFS for each multivariable Cox model. The C-indices were 0.772 (95% CI, 0.702-0.842) for cT and 0.787 (95% CI, 0.726-0.848) for cT (p = 0.789). The optimal cutoffs for cT categorization of the mediastinal window-based solid portions were 0.9 cm and 1.8 cm. However, there were no significant differences in the C-indices among cT, cT, and cT (p > 0.05). CONCLUSIONS:The prognostic performances of the cT categorizations at the lung and mediastinal windows were not significantly different. The current cT categorization based on the lung window measurement is appropriate as it stands. KEY POINTS:• Discriminatory power of the eighth-edition clinical T category was not significantly affected by the CT display window settings. • Given the facts that the lung window setting enables more sensitive detection of the solid portions and higher correlation with the pathological invasive components, our findings may support adherence to the usage of the lung window setting for the solid portion measurement per the current recommendations.
10.1007/s00330-019-06216-6
Acinar-Predominant Pattern Correlates With Poorer Prognosis in Invasive Mucinous Adenocarcinoma of the Lung.
Lin Gengpeng,Li Hui,Kuang Jianyi,Tang Kejing,Guo Yubiao,Han Anjia,Xie Canmao
American journal of clinical pathology
OBJECTIVES:Invasive mucinous adenocarcinoma (IMA) is a variant of lung adenocarcinoma with several growth patterns, such as lepidic acinar and papillary. However, to our knowledge, no study regarding prognostic and clinicopathologic aspects of IMAs with different growth patterns has been reported. METHODS:Of 2,236 patients with primary lung adenocarcinoma, 16 were identified as having lepidic-predominant IMA and 10 as having acinar-predominant IMA. Data regarding the clinicopathologic characteristics, computed tomography (CT) features, and prognosis were collected. RESULTS:No statistically significant difference was noted in sex, age, smoker proportion, and T classification between both groups. The proportion of lymph node metastasis was significantly higher in acinar-predominant IMA (P = .046). Both groups shared many signs in CT findings. Air bronchogram was a relatively specific sign for lepidic-predominant IMA. Survival analysis showed that acinar-predominant IMA had a poorer prognosis (P = .0294). CONCLUSIONS:Lepidic-predominant and acinar-predominant IMA are two different subtypes of IMA. Acinar-predominant IMA is associated with lymph node metastasis and a poorer prognosis.
10.1093/ajcp/aqx170
Lung Adenocarcinoma Invasiveness Risk in Pure Ground-Glass Opacity Lung Nodules Smaller than 2 cm.
Lee Geun Dong,Park Chul Hwan,Park Heae Surng,Byun Min Kwang,Lee Ik Jae,Kim Tae Hoon,Lee Sungsoo
The Thoracic and cardiovascular surgeon
BACKGROUND: We aimed to identify clinicopathologic characteristics and risk of invasiveness of lung adenocarcinoma in surgically resected pure ground-glass opacity lung nodules (GGNs) smaller than 2 cm. METHODS: Among 755 operations for lung cancer or tumors suspicious for lung cancer performed from 2012 to 2016, we retrospectively analyzed 44 surgically resected pure GGNs smaller than 2 cm in diameter on computed tomography (CT). RESULTS: The study group was composed of 36 patients including 11 men and 25 women with a median age of 59.5 years (range, 34-77). Median follow-up duration of pure GGNs was 6 months (range, 0-63). Median maximum diameter of pure GGNs was 8.5 mm (range, 4-19). Pure GGNs were resected by wedge resection, segmentectomy, or lobectomy in 27 (61.4%), 10 (22.7%), and 7 (15.9%) cases, respectively. Pathologic diagnosis was atypical adenomatous hyperplasia, adenocarcinoma in situ, minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IA) in 1 (2.3%), 18 (40.9%), 15 (34.1%), and 10 (22.7%) cases, respectively. The optimal cutoff value for CT-maximal diameter to predict MIA or IA was 9.1 mm. In multivariate analyses, maximal CT-maximal diameter of GGNs ≥10 mm (odds ratio, 24.050; 95% confidence interval, 2.6-221.908; = 0.005) emerged as significant independent predictor for either MIA or IA. Estimated risks of MIA or IA were 37.2, 59.3, 78.2, and 89.8% at maximal GGN diameters of 5, 10, 15, and 20 mm, respectively. CONCLUSION: Pure GGNs were highly associated with lung adenocarcinoma in surgically resected cases, while estimated risk of GGNs invasiveness gradually increased as maximal diameter increased.
10.1055/s-0037-1612615
Computed Tomography-Based Score Indicative of Lung Cancer Aggression (SILA) Predicts the Degree of Histologic Tissue Invasion and Patient Survival in Lung Adenocarcinoma Spectrum.
Varghese Cyril,Rajagopalan Srinivasan,Karwoski Ronald A,Bartholmai Brian J,Maldonado Fabien,Boland Jennifer M,Peikert Tobias
Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer
OBJECTIVE:Most computed tomography (CT)-detected lung cancers are adenocarcinomas (ACs), representing lesions with variable tissue invasion, aggressiveness, and clinical outcome. Visual radiologic characterization of AC pulmonary nodules is both inconsistent and inadequate to confidently predict histopathologic classification or prognosis. Comprehensive pathologic interpretation requires full nodule resection. We have described a computerized scoring system for AC detected on CT scans that can noninvasively estimate the degree of histologic invasion and simultaneously predict patient survival. METHODS:The Computer-Aided Nodule Assessment and Risk Yield has been validated to characterize CT-detected nodules across the spectrum of AC. With the use of unsupervised clustering, nine natural exemplars were identified as basic radiographic features of AC nodules. We now introduce the Score Indicative of Lung Cancer Aggression (SILA), which is a cumulative aggregate of normalized distributions of ordered Computer-Aided Nodule Assessment and Risk Yield exemplars. The SILA values for each of 237 unique nodules in AC were compared with the histopathologically defined maximum linear extent of tumor invasion. With use of the SILA, Kaplan-Meier survival and Cox proportionality analysis were performed on patients with stage I AC, who comprised a subset of our cohort. RESULTS:The SILA discriminated between indolent and invasive AC (p < 0.0001). In addition, prediction of linear extent of histopathologic tumor invasion was possible. In stage I AC, three separate SILA prognosis groups were identified: indolent, intermediate, and poor, with 5-year survival rates of 100%, 79%, 58%, respectively. Cox proportionality hazard modeling predicted a 50% increase in mortality, for a 0.1 unit increase in the SILA over a median follow-up time of 3.6 years (p < 0.0002). CONCLUSIONS:The SILA is a computer-based analytic measure allowing noninvasive approximation of histologic invasion and prediction of patient survival in CT-detected AC nodules.
10.1016/j.jtho.2019.04.022
Central Tumor Location at Chest CT Is an Adverse Prognostic Factor for Disease-Free Survival of Node-Negative Early-Stage Lung Adenocarcinomas.
Choi Hyewon,Kim Hyungjin,Park Chang Min,Kim Young Tae,Goo Jin Mo
Radiology
Background The prognostic value of primary tumor location in the central lung is unclear because of heterogeneity in definitions of central lung cancer (CLC). Purpose To validate the prognostic value of two recently proposed definitions of CLC by using a method designed to offset the shortcomings of existing evidence and investigate the prognostic implications of a quantitative definition of CLC at chest CT. Materials and Methods Patients with pathologic stage T1a-bN0M0 lung adenocarcinomas resected between 2009 and 2015 at a single tertiary care center were retrospectively identified. The primary end point was disease-free survival. The associations of multiple definitions of central tumor location with survival were evaluated by using multivariable Cox regression. Time-dependent discrimination measures and interreader agreement were assessed for each definition. Results A total of 436 patients (median age, 62 years [interquartile range, 55-69 years]; 245 women) were evaluated. Tumor location at CT in the inner one-third of the lung defined by concentric lines arising from the hilum was adversely associated with survival (five events among 34 patients with CLC and 27 events among 402 patients with peripheral lung cancer; adjusted hazard ratio, 2.90 [95% CI: 1.06, 7.96; = .04]) and showed moderate interreader agreement (Cohen κ = 0.52 [95% CI: 0.37, 0.68]). Quantitatively determined location in the inner two-thirds of the lung was also an independent prognostic factor (16 events among 130 patients with CLC and 16 events among 306 patients with peripheral lung cancer; adjusted hazard ratio, 2.77 [95% CI: 1.36, 5.65]; = .005), with higher interreader agreement (Cohen κ = 0.86 [95% CI: 0.80, 0.91]; < .001). The quantification-based definition exhibited higher time-dependent sensitivity (48.2% [14.27/29.61; 95% CI: 28.8, 67.6] vs 15.1% [4.47/29.61; 95% CI: 1.3, 28.9]; < .001). Conclusion Central lung cancer at chest CT, defined qualitatively or quantitatively, is an independent adverse prognostic factor in patients with node-negative, early-stage lung adenocarcinomas. The quantification-based approach has advantages in terms of time-dependent sensitivity and reproducibility. © RSNA, 2021 See also the editorial by Wandtke and Hobbs in this issue.
10.1148/radiol.2021203937
Utility of Maximum CT Value in Predicting the Invasiveness of Pure Ground-Glass Nodules.
Ichinose Junji,Kawaguchi Yohei,Nakao Masayuki,Matsuura Yosuke,Okumura Sakae,Ninomiya Hironori,Oikado Katsunori,Nishio Makoto,Mun Mingyon
Clinical lung cancer
PURPOSE:To predict the histologic invasiveness of pure GGNs using the maximum CT value. PATIENTS AND METHODS:One hundred eighty patients underwent a resection of pure GGNs. On preoperative CT imaging studies, we selected the axial section that showed the densest component of each GGN. The CT value was measured using a DICOM (Digital Imaging and Communication in Medicine) viewer, excluding portions of vessels and bronchi. The correlation between the CT value and GGN histologic diagnosis was analyzed. RESULTS:The numbers of patients with atypical adenomatous hyperplasia, adenocarcinoma-in-situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) were 9, 108, 56, and 7, respectively. One of the IAC tumors exhibited lymphatic invasion, and there were no cases of vascular invasion. In comparison to preinvasive lesions (atypical adenomatous hyperplasia and AIS), invasive lesions (MIA and IAC) were correlated with a higher maximum CT value (-404 ± 113 Hounsfield units [HU] vs. -216 ± 125 HU, P < .01). The cutoff point of maximum CT value was determined at -300 HU using receiver operating characteristic curve analysis, and exhibited sensitivity and specificity of 83% and 88%, respectively. Multivariate analysis revealed that maximum CT value was an independent predictor of histologic invasiveness (odds ratio 39, P < .01). The interobserver reliability was satisfactory (intraclass correlation coefficient, 0.738; unweighted kappa-values, 0.722). CONCLUSION:IAC and MIA accounted for 4% and 31% of the pure GGN lesions, respectively. Higher maximum CT value (≥ -300 HU) was a useful predictor of histologic invasiveness.
10.1016/j.cllc.2020.01.015
The effect that pathologic and radiologic interpretation of invasive and non-invasive areas in lung adenocarcinoma has on T-stage and treatment.
Hart Jesse L,Canepa Mariana,Agarwal Saurabh,Lu Shaolei,Azzoli Christopher,Garcia-Moliner Maria
Annals of diagnostic pathology
Lung adenocarcinoma is currently staged based on invasive tumor size, excluding areas of lepidic (in situ) growth. Invasive tumor size may be determined by pathologic assessment of a surgical specimen or radiographic assessment on computerized tomography (CT) scan. When invasive tumor size is the primary stage determinate, radiographic-pathologic discordance or discordant interpretation among pathologists may alter tumor stage and treatment. We reviewed 40 cases of non-mucinous pulmonary adenocarcinoma in which tumor size was the only stage-determinant. We determined the inter-observer variability when microscopically assessing architectural patterns and its effect on pathologic stage and treatment. Additionally, we correlated pathologic and radiographic assessment of invasive tumor size and its effect on tumor stage and treatment. The intraclass correlation among three pathologists was 0.9879; all three pathologists agreed on T-stage in 75% of cases. Four cases of pathologic disagreement had the potential to alter therapy. Intraclass correlation between the pathologists and invasive tumor size determined by CT scan was 0.8482. In 23 cases (57.5%) the pathologic T-stage differed (it increased >90% of the time) from clinical T-stage (determined by CT scan) based on invasive tumor size. Five of the radiographically-pathologically discrepant cases resulted in a stage change that had the potential to alter adjuvant therapy. Our findings suggest the stage differences in pathologic staging are prognostically relevant, but unlikely to impact routine selection of adjuvant therapy, and the observed variability in clinical stage tends to select against overuse of neoadjuvant therapy when invasive tumor size is the primary stage-determinant.
10.1016/j.anndiagpath.2021.151799
Mathematical prediction model of computed tomography signs is superior to intraoperative frozen section in the diagnosis of ground-glass nodular invasive adenocarcinoma of the lung.
Tang Jizheng,Cui Yong,Li Bowen,Xue Xingxing,Tian Feng
Thoracic cancer
BACKGROUND:At present, lobectomy is still the standard treatment for lung cancer. Judging whether a lesion is invasive adenocarcinoma (IA) has important guiding significance for determining the scope of surgical resection. The commonly used methods are intraoperative frozen sections and computed tomography (CT) signs. There is still controversy about the accuracy of both in judging the invasiveness of ground-glass nodules (GGNs). METHODS:The clinical data of patients with GGNs who underwent surgery were collected. According to the results of univariate analysis, the variables with statistical differences were selected and included in logistic regression multivariate analysis. The predictive variables were determined and the receiver operating characteristic (ROC) curve was drawn in order to achieve the area under the curve (AUC) value. RESULTS:According to the results of logistic regression analysis, the longest diameter and maximum CT value of nodules were independent risk factors for IA. The mathematical prediction model of CT signs was determined, and the ROC curves of CT signs and intraoperative frozen sections (FS) were drawn, respectively. The AUC values under the curves were calculated to be 0.873 and 0.807, respectively. The mathematical prediction model of intraoperative frozen section combined with CT signs was established. A ROC curve was drawn and the AUC was calculated to be 0.925. CONCLUSIONS:The diagnostic accuracy of CT signs in judging whether nonbenign GGNs were IA was higher than that of intraoperative FS. Combined with CT signs and intraoperative FS to establish a mathematical prediction model, the diagnostic accuracy of judging whether nonbenign GGNs are IA is significantly improved.
10.1111/1759-7714.14082
Lung Adenocarcinoma Manifesting as Ground-Glass Opacity Nodules 3 cm or Smaller: Evaluation With Combined High-Resolution CT and PET/CT Modality.
Niu Rong,Shao Xiaonan,Shao Xiaoliang,Wang Jianfeng,Jiang Zhenxing,Wang Yuetao
AJR. American journal of roentgenology
The purpose of this study is to evaluate high-resolution CT (HRCT) combined with PET/CT for preoperative differentiation of invasive adenocarcinoma (IAC) from preinvasive lesions and minimally invasive adenocarcinoma (MIA) (the combination of which is hereafter referred to as preinvasive-MIA) in lung adenocarcinoma manifesting as ground-glass opacity nodules (GGNs) 3 cm or smaller. We retrospectively analyzed the data of patients with lung adenocarcinoma with GGNs that were 3 cm or smaller between November 2011 and November 2018. The HRCT and PET/CT parameters for GGNs were compared to differentiate between IAC and preinvasive-MIA. Qualitative and quantitative parameters were analyzed using univariate and multivariate logistic regression models. The diagnostic performance of different parameters was compared using ROC curves and the McNemar test. The study enrolled 89 patients (24 men and 65 women) with lung adenocarcinoma who had a mean (± SD) age of 60.1 ± 8.1 years (range, 36-78 years). The proportions of mixed GGN type, polygonal or irregular shape, lobulated or spiculated edge, and dilated, distorted, or cutoff bronchial sign were higher for IAC GGNs than for preinvasive-MIA GGNs, and the attenuation value of the ground-glass opacity component on CT (CT), maximum standardized uptake value, and the standardized uptake value (SUV) index (i.e., the ratio of the tumor maximum SUV to the liver mean SUV) for IAC GGNs were also higher ( = 0.001-0.022). Logistic regression analyses showed that the CT and SUV index were independent predictors for IAC GGNs. The accuracy of CT in combination with the SUV index for predicting IAC was 81.4% on a per-GGN basis and 85.4% on a per-patient basis. The combined HRCT and PET/CT modality had higher sensitivity and accuracy than did morphologic features, HRCT, and PET/CT measurement parameters alone ( < 0.001). The combined HRCT and PET/CT modality is an effective method to preoperatively identify IAC in lung adenocarcinoma manifesting as GGNs 3 cm or smaller.
10.2214/AJR.19.21382
CT Radiomics Combined With Clinicopathological Features to Predict Invasive Mucinous Adenocarcinoma in Patients With Lung Adenocarcinoma.
Technology in cancer research & treatment
This study aimed to develop and validate predictive models using clinical parameters, radiomic features, and a combination of both for invasive mucinous adenocarcinoma (IMA) of the lung in patients with lung adenocarcinoma. A total of 173 and 391 patients with IMA and non-IMA, respectively, were retrospectively analyzed from January 2017 to September 2022 in our hospital. Propensity Score Matching was used to match the 2 groups of patients. A total of 1037 radiomic features were extracted from contrast-enhanced computed tomography (CT). The patients were randomly divided into training and test groups at a ratio of 7:3. The least absolute shrinkage and selection operator algorithm was used for radiomic feature selection. Three radiomics prediction models were applied: logistic regression (logistic), support vector machine (SVM), and decision tree. The best-performing model was adopted, and the radiomics score (Radscore) was then computed. A clinical model was developed using logistic regression. Finally, a combined model was established based on a clinical model and a radiomics model. The area under the receiver operating characteristic (ROC) curve (AUC) and decision curve analysis were used to evaluate the predictive value of the developed models. Both clinical and radiomics models established using the logistic method showed the best performance. The Delong test revealed that the combined model was superior to the clinical and radiomics models ( = .018 and .020, respectively). The ROC-AUC (also decision curve analysis) of the combined model was 0.840 and 0.850 in the training and testing groups, respectively, which showed good predictive performance for IMA. The Brier scores for the combined model were 0.161 and 0.154 in the training and testing groups, respectively. The combined model incorporating radiomic CT features and clinical predictors may have the potential to predict IMA in patients with lung cancer.
10.1177/15330338231174306
Predictors of Pathologic Tumor Invasion and Prognosis for Ground Glass Opacity Featured Lung Adenocarcinoma.
Ye Ting,Deng Lin,Xiang Jiaqing,Zhang Yawei,Hu Hong,Sun Yihua,Li Yuan,Shen Lei,Wang Shengping,Xie Li,Chen Haiquan
The Annals of thoracic surgery
BACKGROUND:We make surgical strategies for ground glass opacity (GGO) nodules currently based on thin-section (TS) computed tomography (CT) findings. Whether radiologic measurements could precisely predict tumor invasion and prognosis of GGO-featured lung adenocarcinoma is uncertain. METHODS:We retrospectively evaluated medical records of patients with radiologic GGO nodules undergoing a surgical procedure at Fudan University Shanghai Cancer Center. The study endpoints were the predictive value and prognostic significance of radiologic measurements (consolidation-to-tumor ratio value, consolidation size, and tumor size) for pathologic lung adenocarcinoma. RESULTS:In this study 736 patients and 841 GGO nodules were included. Five-year lung cancer-specific regression-free survival (LCS-RFS) rate was 95.76% (95% confidence interval [CI], 93.01% to 97.44%). The 5-year LCS overall survival (OS) rate was 98.99% (95% CI, 97.69% to 99.57%). Multivariable analysis showed that tumor invasion (invasive adenocarcinoma [IAD] vs adenocarcinoma in situ [AIS]/minimally invasive adenocarcinoma [MIA], p = 0.020) was the only independent predictor for 5-year LCS-RFS. IAD (hazard ratio, 15.98; 95% CI, 1.55 to 164.35) was correlated with a higher risk of recurrence. Kaplan-Meier analysis showed that only tumor invasion status (IAD vs AIS/MIA, p = 0.003) could predict 5-year lung cancer-specific overall survival (LCS-OS), and IAD had a worse LCS-OS than AIS and MIA. A part-solid component (odds ratio [OR], 9.09; 95% CI, 2.71 to 30.47; p = 0.000), large consolidation size (OR, 3.11; 95% CI, 1.03 to 9.40; p = 0.045), and large tumor size (OR, 5.48; 95% CI, 2.68 to 11.19; p = 0.000) were associated with pathologic IAD. For IAD ≤ 20 mm, segmentectomy and lobectomy had better 5-year LCS-RFS than wedge resection, although the difference was statistically insignificant (p = 0.367). The three types of surgeries provided the similar 5-year LCS-OS (p = 0.834). CONCLUSIONS:Radiologic measurements could not precisely predict tumor invasion and prognosis. Making treatment strategies solely according to TS-CT findings for GGO tumor is inappropriate.
10.1016/j.athoracsur.2018.06.058
Predicting lung adenocarcinoma invasiveness by measurement of pure ground-glass nodule roundness by using multiplanar reformation: a retrospective analysis.
Wang Q,Ba W,Yin K,Shen J,Jiang G,Liang Y,Zhu Z,Wu J
Clinical radiology
AIM:To explore the value of roundness measurement based on thin-section axial, coronal, and sagittal section computed tomography (CT) images for predicting pure ground-glass nodule (pGGN) invasiveness. MATERIALS AND METHODS:A total of 168 pGGNs in 155 patients (44 male, 111 females; mean age, 55.74 ± 10.57 years), and confirmed by surgery and histopathology, were analysed retrospectively and divided into pre-invasive (n=72) and invasive (n=96) groups. Photoshop (CS6) software was used to measure pGGN roundness based on conventional axial section, as well as coronal and sagittal sections generated by multiplanar reformation, from thin-section (1-mm-thick) CT lung images. RESULTS:pGGN roundness values, measured in axial, coronal, and sagittal thin-section CT sections from the pre-invasive group were 0.8 ± 0.049, 0.816 ± 0.05, and 0.818 ± 0.043, respectively, while those in the invasive group were 0.745 ± 0.077, 0.684 ± 0.106, and 0.678 ± 0.106; differences between the two groups were significant (all p<0.001). Binary logistic regression analysis showed that roundness values based on coronal and sagittal sections (p<0.001) were better than those from axial sections (p>0.05) in predicting pGGN invasiveness, with odds ratio (OR) values of 14.858 and 23.315, respectively. ROC analysis showed that evaluation of roundness measured in sagittal sections was better at predicting pGGN invasiveness than when coronal sections were used (AUC 0.870 versus 0.832). CONCLUSION:Roundness is useful for predicting pGGN invasiveness, with measurements from coronal and sagittal sections better than those from conventional axial sections, with sagittal section images having the best predictive value.
10.1016/j.crad.2021.10.007
Diagnostic performance for pulmonary adenocarcinoma on CT: comparison of radiologists with and without three-dimensional convolutional neural network.
Yanagawa Masahiro,Niioka Hirohiko,Kusumoto Masahiko,Awai Kazuo,Tsubamoto Mitsuko,Satoh Yukihisa,Miyata Tomo,Yoshida Yuriko,Kikuchi Noriko,Hata Akinori,Yamasaki Shohei,Kido Shoji,Nagahara Hajime,Miyake Jun,Tomiyama Noriyuki
European radiology
OBJECTIVES:To compare diagnostic performance for pulmonary invasive adenocarcinoma among radiologists with and without three-dimensional convolutional neural network (3D-CNN). METHODS:Enrolled were 285 patients with adenocarcinoma in situ (AIS, n = 75), minimally invasive adenocarcinoma (MIA, n = 58), and invasive adenocarcinoma (IVA, n = 152). A 3D-CNN model was constructed with seven convolution-pooling and two max-pooling layers and fully connected layers, in which batch normalization, residual connection, and global average pooling were used. Only the flipping process was performed for augmentation. The output layer comprised two nodes for two conditions (AIS/MIA and IVA) according to prognosis. Diagnostic performance of the 3D-CNN model in 285 patients was calculated using nested 10-fold cross-validation. In 90 of 285 patients, results from each radiologist (R1, R2, and R3; with 9, 14, and 26 years of experience, respectively) with and without the 3D-CNN model were statistically compared. RESULTS:Without the 3D-CNN model, accuracy, sensitivity, and specificity of the radiologists were as follows: R1, 70.0%, 52.1%, and 90.5%; R2, 72.2%, 75%, and 69%; and R3, 74.4%, 89.6%, and 57.1%, respectively. With the 3D-CNN model, accuracy, sensitivity, and specificity of the radiologists were as follows: R1, 72.2%, 77.1%, and 66.7%; R2, 74.4%, 85.4%, and 61.9%; and R3, 74.4%, 93.8%, and 52.4%, respectively. Diagnostic performance of each radiologist with and without the 3D-CNN model had no significant difference (p > 0.88), but the accuracy of R1 and R2 was significantly higher with than without the 3D-CNN model (p < 0.01). CONCLUSIONS:The 3D-CNN model can support a less-experienced radiologist to improve diagnostic accuracy for pulmonary invasive adenocarcinoma without deteriorating any diagnostic performances. KEY POINTS:• The 3D-CNN model is a non-invasive method for predicting pulmonary invasive adenocarcinoma in CT images with high sensitivity. • Diagnostic accuracy by a less-experienced radiologist was better with the 3D-CNN model than without the model.
10.1007/s00330-020-07339-x
Joint use of the radiomics method and frozen sections should be considered in the prediction of the final classification of peripheral lung adenocarcinoma manifesting as ground-glass nodules.
Wang Bin,Tang Yuhong,Chen Yinan,Hamal Preeti,Zhu Yajing,Wang TingTing,Sun Yangyang,Lu Yang,Bhuva Maheshkumar Satishkumar,Meng Xue,Yang Yang,Ai Zisheng,Wu Chunyan,Sun Xiwen
Lung cancer (Amsterdam, Netherlands)
OBJECTIVES:To evaluate the diagnostic accuracy of radiomics method and frozen sections (FS) for the pathological classification of peripheral lung adenocarcinoma manifesting as ground-glass nodules (GGNs) in computer tomography (CT). MATERIALS AND METHODS:A dataset of 831 peripheral lung adenocarcinoma manifesting as GGNs in CT were divided into two cohorts: training cohort (n = 581) and validation cohort (n = 250). Combined with clinical features, the radiomics classifier was trained and validated to distinguish the pathological classification of these nodules. FS diagnoses in the validation cohort were collected. Diagnostic performance of the FS and radiomics methods was compared in the validation cohort. The predictive factors for the misdiagnosis of FS were determined via univariate and multivariate analyses. RESULTS:The accuracy of radiomics method in the training and validation cohorts was 72.5 % and 68.8 % respectively. The overall accuracy of FS in the validation cohort was 70.0 %. The concordance rate between FS and final pathology when FS had a different diagnosis from radiomics classifier was significantly lower than when FS had the same diagnosis as radiomics classifier (46 vs. 87 %, P < 0.001). Univariate and Multivariate analyses showed that different diagnosis between FS and radiomics classifier was the independent predictive factor for the misdiagnosis of FS (OR: 7.46; 95%CI: 4.00-13.91; P < 0.001). CONCLUSIONS:Radiomics classifier predictions may be a reliable reference for the classification of peripheral lung adenocarcinoma manifesting as GGNs when FS cannot provide a timely diagnosis. Intraoperative diagnoses of the cases where FS had a different diagnosis from radiomics method should be considered cautiously due to the higher misdiagnosis rate.
10.1016/j.lungcan.2019.10.031
Radiomics-guided deep neural networks stratify lung adenocarcinoma prognosis from CT scans.
Communications biology
Deep learning (DL) is a breakthrough technology for medical imaging with high sample size requirements and interpretability issues. Using a pretrained DL model through a radiomics-guided approach, we propose a methodology for stratifying the prognosis of lung adenocarcinomas based on pretreatment CT. Our approach allows us to apply DL with smaller sample size requirements and enhanced interpretability. Baseline radiomics and DL models for the prognosis of lung adenocarcinomas were developed and tested using local (n = 617) cohort. The DL models were further tested in an external validation (n = 70) cohort. The local cohort was divided into training and test cohorts. A radiomics risk score (RRS) was developed using Cox-LASSO. Three pretrained DL networks derived from natural images were used to extract the DL features. The features were further guided using radiomics by retaining those DL features whose correlations with the radiomics features were high and Bonferroni-corrected p-values were low. The retained DL features were subject to a Cox-LASSO when constructing DL risk scores (DRS). The risk groups stratified by the RRS and DRS showed a significant difference in training, testing, and validation cohorts. The DL features were interpreted using existing radiomics features, and the texture features explained the DL features well.
10.1038/s42003-021-02814-7
Correlation with Spectral CT Imaging Parameters and Occult Lymph Nodes Metastases in Sufferers with Isolated Lung Adenocarcinoma.
Contrast media & molecular imaging
For investigating the correlation with spectral CT imaging parameters and occult lymph nodes metastasis in sufferers with isolated lung adenocarcinoma. The clinic cases data of 352 sufferers with isolated lung adenocarcinoma from January 2019 to January 2022 were assembled. In line with whether the sufferers had occult lymph nodes metastasis, they were taken as a part in the metastasis group ( = 172) and the nonmetastasis group. All sufferers were scanned by spectral CT with a dual-phase contrast-enhanced method, and the recording of spectral CT imaging parameters in arteriovenous phase, iodine concentration (IC), water concentration (WC), the slope rate of the spectral HU curve (HU), the normalized iodine concentration(NIC), the normalized water concentration(NWC), the normalized effective atomic number (Neff-Z)], and receiver operating characteristic (ROC) were employed to analyze the spectral CT imaging parameters of the arteriovenous phase. Evaluation of occult lymph nodes metastases in sufferers with isolated lung adenocarcinoma. The IC, NIC, HU, and Neff-Z in the arteriovenous phase spectral CT imaging parameters of the metastasis group were obviously smaller than that of the nonmetastasis group, and the discrepancies were statistically obvious ( < 0.05). The results of ROC curve analysis manifested that the area under the curve (AUC) of HU, IC, NIC, and Neff-Z in the CT parameters of the arterial phase were 0. 840 (95%CI : 0. 796-0.883), 0.763 (95% CI : 0.708-0.818), 0.918 (95% CI : 0.888-0.948), 0.778 (95% CI : 0.731-0.826). The AUCs of HU, IC, NIC, and Neff-Z in the venous phase spectral CT parameters were 0.909 (95% CI : 0.877-0.941), 0.837 (95% CI : 0.792-0.881), and 0.980 (95% CI : 0.968-0.968), respectively. 0.993), 0.792 (95% CI : 0.742∼0.842). Spectral CT imaging parameters have a certain value in evaluating occult lymph nodes metastasis in sufferers with isolated lung adenocarcinoma, which is helpful for doctors to judge the lymph nodes metastasis in sufferers with this disease before surgery.
10.1155/2022/5472446
Prognostic role of positron emission tomography and computed tomography parameters in stage I lung adenocarcinoma.
Carretta Angelo,Bandiera Alessandro,Muriana Piergiorgio,Viscardi Stefano,Ciriaco Paola,Gajate Ana Maria Samanes,Arrigoni Gianluigi,Lazzari Chiara,Gregorc Vanesa,Negri Giampiero
Radiology and oncology
Background According to the current pathological classification, lung adenocarcinoma includes histological subtypes with significantly different prognoses, which may require specific surgical approaches. The aim of the study was to assess the role of CT and PET parameters in stratifying patients with stage I adenocarcinoma according to prognosis. Patients and methods Fifty-eight patients with pathological stage I lung adenocarcinoma who underwent surgical treatment were retrospectively reviewed. Adenocarcinoma in situ and minimally-invasive adenocarcinoma were grouped as non-invasive adenocarcinoma. Other histotypes were referred as invasive adenocarcinoma. CT scan assessed parameters were: ground glass opacity (GGO) ratio, tumour disappearance rate (TDR) and consolidation diameter. The prognostic role of the following PET parameters was also assessed: standardized uptake value (SUV) max, SUVindex (SUVmax to liver SUVratio), metabolic tumour volume (MTV), total lesion glycolysis (TLG). Results Seven patients had a non-invasive adenocarcinoma and 51 an invasive adenocarcinoma. Five-year disease-free survival (DFS) and cancer-specific survival (CSS) for non-invasive and invasive adenocarcinoma were 100% and 100%, 70% and 91%, respectively. Univariate analysis showed a significant difference in SUVmax, SUVindex, GGO ratio and TDR ratio values between non-invasive and invasive adenocarcinoma groups. Optimal SUVmax, SUVindex, GGO ratio and TDR cut-off ratios to predict invasive tumours were 2.6, 0.9, 40% and 56%, respectively. TLG, SUVmax, SUVindex significantly correlated with cancer specific survival. Conclusions CT and PET scan parameters may differentiate between non-invasive and invasive stage I adenocarcinomas. If these data are confirmed in larger series, surgical strategy may be selected on the basis of preoperative imaging.
10.2478/raon-2020-0034
Lung Adenocarcinoma Presenting as Multiple Thromboembolic Events: A Case Report and Review of the Literature.
Galarza Fortuna Gliceida M,Singh Anita,Jacobs Adam,Ugalde Israel
Journal of investigative medicine high impact case reports
Patients with malignancy may present with significant thromboembolic complications including deep vein thrombosis (DVT), pulmonary embolism, arterial thrombosis, nonbacterial thrombotic endocarditis, and stroke due to abnormal coagulation cascades. Although these events are typically recognized later in the disease process, complications of a hypercoagulable state can rarely present as the first manifestation of an occult malignancy. We report a case of a young male who was ultimately found to have an aggressive form of lung adenocarcinoma after the initial presentation of multiple thromboembolic events. DVT and stroke as an initial presentation of an active lung adenocarcinoma in a young patient is extremely rare as patients presenting in a hypercoagulable state usually are older. Though testing for a hypercoagulable state is not recommended for the first unprovoked DVT, clinicians should be prompted to screen for malignancy in the setting of cryptogenic strokes, especially in younger patients with no prior risk factors.
10.1177/2324709620969482
Utility of Core Biopsy Specimen to Identify Histologic Subtype and Predict Outcome for Lung Adenocarcinoma.
Kim Tae Hee,Buonocore Darren,Petre Elena Nadia,Durack Jeremy C,Maybody Majid,Johnston Rocio P,Travis William D,Adusumilli Prasad S,Solomon Stephen B,Ziv Etay
The Annals of thoracic surgery
BACKGROUND:Lung adenocarcinoma histologic subtype is an important indicator of patient outcomes, so preoperative knowledge of subtype may be helpful to guide surgical planning. We evaluated the sensitivity and prognostic efficacy of specimens from computed tomography-guided core needle biopsies to predict histologic subtype and patient outcome after surgery. METHODS:We retrospectively identified 221 patients with lung adenocarcinoma who underwent computed tomography-guided lung biopsy and subsequent surgical resection. Concordance, accuracy, specificity, and sensitivity of histologic subtypes from core biopsy specimens were compared with surgically resected specimens. Tumor characteristics and biopsy procedural factors were analyzed to determine impact on diagnostic sensitivity. Histologic subtype based on biopsy specimen, clinical, tumor, and treatment variables were also examined in relation to time to progression. RESULTS:Overall concordance of biopsy samples with the predominant subtype from surgical specimens was 77%. Specificity (sensitivity) of detecting a nonaggressive and aggressive subtype were 86% (93%) and 95% (48%), respectively. Length of core specimen and percentage subtype composition in the surgically resected specimen were correlated with improved sensitivity but to a lesser extent with aggressive subtypes. Presence of an aggressive subtype in biopsy specimens was an independent predictor of progression after surgery (subdistribution hazard ratio, 2.51; 95% confidence interval, 1.28-4.94; p = 0.0075). CONCLUSIONS:Specimens from computed tomography-guided core biopsies can predict lung adenocarcinoma progression after surgical resection. Future prospective studies should address the role of core biopsy in preoperative planning.
10.1016/j.athoracsur.2019.03.043
Incremental benefits of size-zone matrix-based radiomics features for the prognosis of lung adenocarcinoma: advantage of spatial partitioning on tumor evaluation.
European radiology
OBJECTIVES:Prognostic models of lung adenocarcinoma (ADC) can be built using radiomics features from various categories. The size-zone matrix (SZM) features have a strong biological basis related to tumor partitioning, but their incremental benefits have not been fully explored. In our study, we aimed to evaluate the incremental benefits of SZM features for the prognosis of lung ADC. METHODS:A total of 298 patients were included and their pretreatment computed tomography images were analyzed in fivefold cross-validation. We built a risk model of overall survival using SZM features and compared it with a conventional radiomics risk model and a clinical variable-based risk model. We also compared it with other models incorporating various combinations of SZM features, other radiomics features, and clinical variables. A total of seven risk models were compared and evaluated using the hazard ratio (HR) on the left-out test fold. RESULTS:As a baseline, the clinical variable risk model showed an HR of 2.739. Combining the radiomics signature with SZM feature was better (HR 4.034) than using radiomics signature alone (HR 3.439). Combining radiomics signature, SZM feature, and clinical variable (HR 6.524) fared better than just combining radiomics signature and clinical variables (HR 4.202). These results confirmed the added benefits of SZM features for prognosis in lung ADC. CONCLUSION:Combining SZM feature with the radiomics signature was better than using the radiomics signature alone and the benefits of SZM features were maintained when clinical variables were added confirming the incremental benefits of SZM features for lung ADC prognosis. KEY POINTS:• Size-zone matrix (SZM) features provide incremental benefits for the prognosis of lung adenocarcinoma. • Combining the radiomics signature with SZM features performed better than using a radiomics signature alone.
10.1007/s00330-022-08818-z
Disparate genomic characteristics of patients with early-stage lung adenocarcinoma manifesting as radiological subsolid or solid lesions.
Lung cancer (Amsterdam, Netherlands)
INTRODUCTION:Early-stage lung adenocarcinoma (LUAD) manifesting as subsolid nodules (SSNs) exhibit more favorable prognosis than solid nodules (SNs). However, the genomic underpinnings behind their indolent tumor behavior remain largely unexplained. METHODS:We identified patients with stage I invasive LUAD who underwent complete surgical resection and broad-panel next-generation sequencing (NGS). Comparative genomic profiling was then performed by radiological subtype (SSNs vs. SNs) regarding the general genomic features, driver genes, oncogenic pathways, therapeutic actionability, and evolutionary trajectory. RESULTS:In total, 177 SSN-LUADs and 133 SN-LUADs were included. Compared with SNs, SSN-LUADs possessed lower somatic mutation count (P < 0.001), genomic alteration count (P = 0.002), and intra-tumor heterogeneity (P = 0.006). In terms of driver genes, SSNs harbored more EGFR mutation (77% vs. 62%), but had lower frequencies of genes such as TP53, ARID1A, PIK3CA, CDKN2A, and BRAF (FDR q < 0.1). Besides, RBM10 mutation was independently associated with SSN-LUADs in multivariate analysis (P = 0.033). Three oncogenic pathways (p53, cell cycle, PI3K) were altered with statistical significance in SNs, while only RNA splicing/processing pathway was significantly altered in SSNs (FDR q < 0.1). Also, SSNs had significantly lower number of pathway alterations (P < 0.001). Finally, SSNs and SNs showed distinct evolutionary trajectories regarding somatic mutations during early-stage LUAD progression. CONCLUSIONS:This study performed the first direct comparative genomic profiling in pathologic stage I invasive LUAD by radiological subtype, highlighting a less complex genomic architecture of SSNs, which might be the molecular interpretation of their indolent tumor behavior.
10.1016/j.lungcan.2022.02.012
The value of CT attenuation in distinguishing atypical adenomatous hyperplasia from adenocarcinoma in situ.
Jiang Binghu,Wang Jichen,Jia Peng,Le Meizhao
Zhongguo fei ai za zhi = Chinese journal of lung cancer
BACKGROUND AND OBJECTIVE:Advances in high-resolution computed tomography (CT) scanning have increased the detection of small ground-glass opacity (GGO) nodules and also allowed such images to be investigated in detail. However, it is difficult to differentiate atypical adenomatous hyperplasia (AAH) from adenocarcinoma in situ (AIS) with CT, even at follow-up, because they share many similar CT manifestations. While AAH is thought to be a precursor or even an early-stage lesion of lung adenocarcinoma, and the stepwise progression from AAH to AIS is thought to be reasonable. Therefore, the hypothesis that the attenuation of GGO is increased gradually from AAH to AIS is proposed. The aim of this study was to distinguish AAH from AIS with CT attenuation in patients with pure GGO nodules. METHODS:Between January 2010 and December 2012, the CT findings in terms of the greatest diameter and mean CT attenuation (HU) were reviewed and correlated with pathology in 56 patients with AAH (n=21) and non-mucinous AIS (n=38) by two independent observers. All the 59 lesions were pure GGO nodules with size of 2 cm or smaller. To determine variability of measuring CT attenuation, we calculated the 95% confidence interval (CI) for the limits of agreement by using Bland-Altman analysis. Student t test was used to compare AAH and AIS in terms of diameter and CT attenuation. And receiver operating characteristic (ROC) curve was used to determine the optimal cut-off value of mean CT attenuation for differentiating AAH from AIS and obtain the diagnostic value. Two-tailed P value of less than 0.05 was considered to be significant. RESULTS:For the manually measured CT attenuation, the 95%CI for the limits of agreement was -40 HU, 50 HU for inter-observer variability. Although there was significant difference in nodule diameter between AAH and AIS (P=0.046), the overlap was considerable. The mean CT attenuation was (-718 ± 53) HU (95%CI: -822, -604) for AAH, which was significantly smaller than (-600 ± 35) HU (95%CI: -669, -531) for AIS (P=0.013). The area under curve (AUC) from ROC was 0.903 for differentiating AAH from AIS, and the cut-off value of -632 HU was optimal for differentiation between AAH and AIS, with sensitivity of 0.79, specificity of 0.95, and accuracy of 0.85. CONCLUSIONS:The mean CT attenuation can help the radiological differentiation between AAH and AIS.
10.3779/j.issn.1009-3419.2013.11.03
Quantitative image variables reflect the intratumoral pathologic heterogeneity of lung adenocarcinoma.
Choi E-Ryung,Lee Ho Yun,Jeong Ji Yun,Choi Yoon-La,Kim Jhingook,Bae Jungmin,Lee Kyung Soo,Shim Young Mog
Oncotarget
We aimed to compare quantitative radiomic parameters from dual-energy computed tomography (DECT) of lung adenocarcinoma and pathologic complexity.A total 89 tumors with clinical stage I/II lung adenocarcinoma were prospectively included. Fifty one radiomic features were assessed both from iodine images and non-contrast images of DECT datasets. Comprehensive histologic subtyping was evaluated with all surgically resected tumors. The degree of pathologic heterogeneity was assessed using pathologic index and the number of mixture histologic subtypes in a tumor. Radiomic parameters were correlated with pathologic index. Tumors were classified as three groups according to the number of mixture histologic subtypes and radiomic parameters were compared between the three groups.Tumor density and 50th through 97.5th percentile Hounsfield units (HU) of histogram on non-contrast images showed strong correlation with the pathologic heterogeneity. Radiomic parameters including 75th and 97.5th percentile HU of histogram, entropy, and inertia on 1-, 2- and 3 voxel distance on non-contrast images showed incremental changes while homogeneity showed detrimental change according to the number of mixture histologic subtypes (all Ps < 0.05).Radiomic variables from DECT of lung adenocarcinoma reflect pathologic intratumoral heterogeneity, which may help in the prediction of intratumoral heterogeneity of the whole tumor.
10.18632/oncotarget.11693
A CT-based radiomics nomogram for prediction of lung adenocarcinomas and granulomatous lesions in patient with solitary sub-centimeter solid nodules.
Cancer imaging : the official publication of the International Cancer Imaging Society
PURPOSE:To develop a radiomics nomogram based on computed tomography (CT) images that can help differentiate lung adenocarcinomas and granulomatous lesions appearing as sub-centimeter solid nodules (SCSNs). MATERIALS AND METHODS:The records of 214 consecutive patients with SCSNs that were surgically resected and histologically confirmed as lung adenocarcinomas (n = 112) and granulomatous lesions (n = 102) from 2 medical institutions between October 2011 and June 2019 were retrospectively analyzed. Patients from center 1 ware enrolled as training cohort (n = 150) and patients from center 2 were included as external validation cohort (n = 64), respectively. Radiomics features were extracted from non-contrast chest CT images preoperatively. The least absolute shrinkage and selection operator (LASSO) regression model was used for radiomics feature extraction and radiomics signature construction. Clinical characteristics, subjective CT findings, and radiomics signature were used to develop a predictive radiomics nomogram. The performance was examined by assessment of the area under the receiver operating characteristic curve (AUC). RESULTS:Lung adenocarcinoma was significantly associated with an irregular margin and lobulated shape in the training set (p = 0.001, < 0.001) and external validation set (p = 0.016, = 0.018), respectively. The radiomics signature consisting of 22 features was significantly associated with lung adenocarcinomas of SCSNs (p < 0.001). The radiomics nomogram incorporated the radiomics signature, gender and lobulated shape. The AUCs of combined model in the training and external validation dataset were 0.885 (95% confidence interval [CI]: 0.823-0.931), 0.808 (95% CI: 0.690-0.896), respectively. Decision curve analysis (DCA) demonstrated that the radiomics nomogram was clinically useful. CONCLUSION:A radiomics signature based on non-enhanced CT has the potential to differentiate between lung adenocarcinomas and granulomatous lesions. The radiomics nomogram incorporating the radiomics signature and subjective findings may facilitate the individualized, preoperative treatment in patients with SCSNs.
10.1186/s40644-020-00320-3
[Prediction of Pathological Subtypes of Lung Adenocarcinoma with Pure Ground Glass Nodules by Deep Learning Model].
Tao Xue-Min,Fang Rui,Wu Chong-Chong,Zhang Chi,Zhang Rong-Guo,Yu Peng-Xin,Zhao Shao-Hong
Zhongguo yi xue ke xue yuan xue bao. Acta Academiae Medicinae Sinicae
To make a preliminary pathological classification of lung adenocarcinoma with pure ground glass nodules(pGGN)on CT by using a deep learning model. CT images and pathological data of 219 patients(240 lesions in total)with pGGN on CT and pathologically confirmed adenocarcinoma were collected.According to pathological subtypes,the lesions were divided into non-invasive lung adenocarcinoma group(which included atypical adenomatous hyperplasia and adenocarcinoma in situ and micro-invasive adenocarcinoma)and invasive lung adenocarcinoma group.First,the lesions were outlined and labeled by two young radiologists,and then the labeled data were randomly divided into two datasets:the training set(80%)and the test set(20%).The prediction Results of deep learning were compared with those of two experienced radiologists by using the test dataset. The deep learning model achieved high performance in predicting the pathological types(non-invasive and invasive)of pGGN lung adenocarcinoma.The accuracy rate in pGGN diagnosis was 0.8330(95% =0.7016-0.9157)for of deep learning model,0.5000(95% =0.3639-0.6361)for expert 1,0.5625(95% =0.4227-0.6931)for expert 2,and 0.5417(95% =0.4029-0.6743)for both two experts.Thus,the accuracy of the deep learning model was significantly higher than those of the experienced radiologists(=0.002).The intra-observer agreements were good(Kappa values:0.939 and 0.799,respectively).The inter-observer agreement was general(Kappa value:0.667)(=0.000). The deep learning model showed better performance in predicting the pathological types of pGGN lung adenocarcinoma compared with experienced radiologists.
10.3881/j.issn.1000-503X.11693
Correlation of computed tomography quantitative parameters with tumor invasion and Ki-67 expression in early lung adenocarcinoma.
Medicine
ABSTRACT:The purpose of the study is to investigate the correlation of computed tomography (CT) quantitative parameters with tumor invasion and Ki-67 expression in early lung adenocarcinoma.The study involved 141 lesions in 141 patients with early lung adenocarcinoma. According to the degree of tumor invasion, the lesions were assigned into (adenocarcinoma in situ + minimally invasive adenocarcinoma) group and invasive adenocarcinoma (IAC) group. Artificial intelligence-assisted diagnostic software was used to automatically outline the lesions and extract corresponding quantitative parameters on CT images. Statistical analysis was performed to explore the correlation of these parameters with tumor invasion and Ki-67 expression.The results of logistic regression analysis showed that the short diameter of the lesion and the average CT value were independent predictors of IAC. Receiver operating characteristic curve analysis identified the average CT value as an independent predictor of IAC with the best performance, with the area under the receiver operating characteristic curve of 0.893 (P < .001), and the threshold of -450 HU. Besides, the predicted probability of logistic regression analysis model was detected to have the area under the curve of 0.931 (P < .001). The results of Spearman correlation analysis showed that the expression level of Ki-67 had the highest correlation with the average CT value of the lesion (r = 0.403, P < .001).The short diameter of the lesion and the average CT value are independent predictors of IAC, and the average CT value is significantly positively correlated with the expression of tumor Ki-67.
10.1097/MD.0000000000029373
A computerized tomography-based radiomic model for assessing the invasiveness of lung adenocarcinoma manifesting as ground-glass opacity nodules.
Respiratory research
BACKGROUND:Clinically differentiating preinvasive lesions (atypical adenomatous hyperplasia, AAH and adenocarcinoma in situ, AIS) from invasive lesions (minimally invasive adenocarcinomas, MIA and invasive adenocarcinoma, IA) manifesting as ground-glass opacity nodules (GGOs) is difficult due to overlap of morphological features. Hence, the current study was performed to explore the diagnostic efficiency of radiomics in assessing the invasiveness of lung adenocarcinoma manifesting as GGOs. METHODS:A total of 1018 GGOs pathologically confirmed as lung adenocarcinoma were enrolled in this retrospective study and were randomly divided into a training set (n = 712) and validation set (n = 306). The nodules were delineated manually and 2446 intra-nodular and peri-nodular radiomic features were extracted. Univariate analysis and least absolute shrinkage and selection operator (LASSO) were used for feature selection. Clinical and semantic computerized tomography (CT) feature model, radiomic model and a combined nomogram were constructed and compared. Decision curve analysis (DCA) was used to evaluate the clinical value of the established nomogram. RESULTS:16 radiomic features were selected and used for model construction. The radiomic model exhibited significantly better performance (AUC = 0.828) comparing to the clinical-semantic model (AUC = 0.746). Further analysis revealed that peri-nodular radiomic features were useful in differentiating between preinvasive and invasive lung adenocarcinomas appearing as GGOs with an AUC of 0.808. A nomogram based on lobulation sign and radiomic features showed the best performance (AUC = 0.835), and was found to have potential clinical value in assessing nodule invasiveness. CONCLUSIONS:Radiomic model based on both intra-nodular and peri-nodular features showed good performance in differentiating between preinvasive lung adenocarcinoma lesions and invasive ones appearing as GGOs, and a nomogram based on clinical, semantic and radiomic features could provide clinicians with added information in nodule management and preoperative evaluation.
10.1186/s12931-022-02016-7
Prediction of micropapillary and solid pattern in lung adenocarcinoma using radiomic values extracted from near-pure histopathological subtypes.
Chen Li-Wei,Yang Shun-Mao,Wang Hao-Jen,Chen Yi-Chang,Lin Mong-Wei,Hsieh Min-Shu,Song Hsiang-Lin,Ko Huan-Jang,Chen Chung-Ming,Chang Yeun-Chung
European radiology
OBJECTIVES:Near-pure lung adenocarcinoma (ADC) subtypes demonstrate strong stratification of radiomic values, providing basic information for pathological subtyping. We sought to predict the presence of high-grade (micropapillary and solid) components in lung ADCs using quantitative image analysis with near-pure radiomic values. METHODS:Overall, 103 patients with lung ADCs of various histological subtypes were enrolled for 10-repetition, 3-fold cross-validation (cohort 1); 55 were enrolled for testing (cohort 2). Histogram and textural features on computed tomography (CT) images were assessed based on the "near-pure" pathological subtype data. Patch-wise high-grade likelihood prediction was performed for each voxel within the tumour region. The presence of high-grade components was then determined based on a volume percentage threshold of the high-grade likelihood area. To compare with quantitative approaches, consolidation/tumour (C/T) ratio was evaluated on CT images; we applied radiological invasiveness (C/T ratio > 0.5) for the prediction. RESULTS:In cohort 1, patch-wise prediction, combined model (C/T ratio and patch-wise prediction), whole-lesion-based prediction (using only the "near-pure"-based prediction model), and radiological invasiveness achieved a sensitivity and specificity of 88.00 ± 2.33% and 75.75 ± 2.82%, 90.00 ± 0.00%, and 77.12 ± 2.67%, 66.67% and 90.41%, and 90.00% and 45.21%, respectively. The sensitivity and specificity, respectively, for cohort 2 were 100.0% and 95.35% using patch-wise prediction, 100.0% and 95.35% using combined model, 75.00% and 95.35% using whole-lesion-based prediction, and 100.0% and 69.77% using radiological invasiveness. CONCLUSION:Using near-pure radiomic features and patch-wise image analysis demonstrated high levels of sensitivity and moderate levels of specificity for high-grade ADC subtype-detecting. KEY POINTS:• The radiomic values extracted from lung adenocarcinoma with "near-pure" histological subtypes provide useful information for high-grade (micropapillary and solid) components detection. • Using near-pure radiomic features and patch-wise image analysis, high-grade components of lung adenocarcinoma can be predicted with high sensitivity and moderate specificity. • Using near-pure radiomic features and patch-wise image analysis has potential role in facilitating the prediction of the presence of high-grade components in lung adenocarcinoma prior to surgical resection.
10.1007/s00330-020-07570-6
Association between Histological Types and Enhancement of Dynamic CT for Primary Lung Cancer.
Fukuma Shogo,Shinya Takayoshi,Soh Junichi,Fukuhara Ryuichiro,Ogawa Nanako,Higaki Fumiyo,Tanaka Takehiro,Ichihara Eiki,Hiraki Takao,Toyooka Shinichi,Kanazawa Susumu
Acta medica Okayama
The aim of this study was to explore enhancement patterns of different types of primary lung cancers on 2-phase dynamic computed tomography (CT). This study included 217 primary lung cancer patients (141 adenocarcinomas [ADs], 48 squamous cell carcinomas [SCCs], 20 small cell lung carcinomas [SCLCs], and 8 others) who were examined using a 2-phase dynamic scan. Regions of interest were identified and mean enhancement values were calculated. After excluding the 20 SCLCs because these lesions had different clinical stages from the other cancer types, the mean attenuation values and subtractions between phases were compared between types of non-small cell lung carcinomas (NSCLCs) using the Kruskal-Wallis test. Late phase attenuation and attenuation of the late minus unenhanced phase (LMU) of SCCs were significantly higher than those of ADs (p<0.05). To differentiate SCC and AD in the late phase, a threshold of 80.21 Hounsfield units (HU) gave 52.9% accuracy. In LMU, a threshold of 52.16 HU gave 59.3% accuracy. Dynamic lung CT has the potential to aid in differentiating among NSCLC types.
10.18926/AMO/58271
CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma.
Jiang Changsi,Luo Yan,Yuan Jialin,You Shuyuan,Chen Zhiqiang,Wu Mingxiang,Wang Guangsuo,Gong Jingshan
European radiology
PURPOSE:Spread through air space (STAS) is a novel invasive pattern of lung adenocarcinoma and is also a risk factor for recurrence and worse prognosis of lung adenocarcinoma. The aims of this study are to develop and validate a computed tomography (CT)‑based radiomics model for preoperative prediction of STAS in lung adenocarcinoma. METHODS AND MATERIALS:This retrospective study was approved by an institutional review board and included 462 (mean age, 58.06 years) patients with pathologically confirmed lung adenocarcinoma. STAS was identified in 90 patients (19.5%). Two experienced radiologists segmented and extracted radiomics features on preoperative thin-slice CT images with radiomics extension independently. Intraclass correlation coefficients (ICC) and Pearson's correlation were used to rule out those low reliable (ICC < 0.75) and redundant (r > 0.9) features. Univariate logistic regression was applied to select radiomics features which were associated with STAS. A radiomics-based machine learning predictive model using a random forest (RF) was developed and calibrated with fivefold cross-validation. The diagnostic performance of the model was measured by the area under the curve (AUC) of receiver operating characteristic (ROC). RESULTS:With univariate analysis, 12 radiomics features and age were found to be associated with STAS significantly. The RF model achieved an AUC of 0.754 (a sensitivity of 0.880 and a specificity of 0.588) for predicting STAS. CONCLUSION:CT-based radiomics model can preoperatively predict STAS in lung adenocarcinoma with good diagnosis performance. KEY POINTS:• CT-based radiomics and machine learning model can predict spread through air space (STAS) in lung adenocarcinoma with high accuracy. • The random forest (RF) model achieved an AUC of 0.754 (a sensitivity of 0.880 and a specificity of 0.588) for predicting STAS.
10.1007/s00330-020-06694-z
Role of intratumoral and peritumoral CT radiomics for the prediction of EGFR gene mutation in primary lung cancer.
The British journal of radiology
OBJECTIVES:To determine the added value of combining intratumoral and peritumoral CT radiomics for the prediction of epidermal growth factor receptor (EGFR) gene mutations in primary lung cancer (PLC). METHODS:This study included 478 patients with PLC (348 adenocarcinomas and 130 other histological types) who underwent surgical resection and EGFR gene testing. Two radiologists performed segmentation of tumors and peritumoral regions using precontrast high-resolution CT images, and 398 radiomic features (212 intra- and 186 peritumoral features) were extracted. The peritumoral region was defined as the lung parenchyma within a distance of 3 mm from the tumor border. Model performance was estimated using Random Forest, a machine-learning algorithm. RESULTS:EGFR mutations were found in 162 tumors; 161 adenocarcinomas, and one pleomorphic carcinoma. After exclusion of poorly reproducible and redundant features, 32 radiomic features remained (14 intra- and 18 peritumoral features) and were included in the model building. For predicting EGFR mutations, combining intra- and peritumoral radiomics significantly improved the performance compared to intratumoral radiomics alone (AUC [area under the receiver operating characteristic curve], 0.774 0.730; < 0.001). Even in adenocarcinomas only, adding peritumoral radiomics significantly increased performance (AUC, 0.687 0.630; < 0.001). The predictive performance using radiomics and clinical features was significantly higher than that of clinical features alone (AUC, 0.826 0.777; = 0.005). CONCLUSIONS:Combining intra- and peritumoral radiomics improves the predictive accuracy of EGFR mutations and could be used to aid in decision-making of whether to perform biopsy for gene tests. ADVANCES IN KNOWLEDGE:Adding peritumoral to intratumoral radiomics yields greater accuracy than intratumoral radiomics alone in predicting EGFR mutations and may serve as a non-invasive method of predicting of the gene status in PLC.
10.1259/bjr.20220374
Prognostic factors of lung adenocarcinoma manifesting as ground glass nodules larger than 3 cm.
Ding Hongdou,Wang Haifeng,Zhang Peng,Song Nan,Chen Linsong,Jiang Gening
European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery
OBJECTIVES:The aim of the study was to investigate prognostic factors of lung adenocarcinomas manifesting as ground glass nodules larger than 3 cm on thin-section computed tomography scans, especially comparing the prognostic role of the whole size and the solid size. METHODS:We included 195 patients with lung adenocarcinomas manifesting as ground glass nodules larger than 3 cm who underwent surgical resection. We identified clinical factors associated with lymph node metastases by binary logistics regression analysis. Kaplan-Meier analysis was performed to determine the association between the whole size or the solid size and overall survival (OS). Multivariable Cox regression analysis was used to identify prognostic factors of OS. RESULTS:The median follow-up time was 62 months. The median values of the whole size and the solid size were 3.5 cm and 2.3 cm, respectively. The 3-year and 5-year OS rates were 95.5% and 86.2%, respectively. Patients with lesions <2.3 cm had markedly better OS than those with lesions ≥2.3 cm. No significant differences existed between the survival of patients with lesions <3.5 cm and ≥3.5 cm. Multivariable analysis showed that bigger solid size was significantly associated with the presence of lymph node metastases and inferior OS, whereas larger whole size was not. Adjuvant chemotherapy improved the OS of patients with stage Ib and II-IIIa disease, but not that of patients with stage Ia disease. CONCLUSIONS:Solid size was a better predictor of lymph node metastases and prognosis than whole size in ground glass nodules larger than 3 cm. Clinical T staging should be based on the solid size rather than on the whole size of these lesions.
10.1093/ejcts/ezy422
Invasive mucinous adenocarcinoma of the lung in a 19-year-old female.
Narahari Narendra Kumar,Uppin Shantveer G,Kapoor Anu,Stalin Bala Joseph,Paramjyothi Gongati K
Asian cardiovascular & thoracic annals
Lung cancers commonly occur in the sixth to eighth decades of life. They are extremely uncommon in first two decades of life. We describe the clinical, radiological, and pathological findings in a 19-year-old female diagnosed with an invasive mucinous adenocarcinoma that was initially mistaken and treated as tuberculosis. This case is being presented to emphasize inclusion of this entity in the differential diagnosis of multifocal consolidations and nodules that do not resolve or persist after treatment, and also to create awareness of the occurrence of lung cancers in young patients.
10.1177/0218492318804951
A new classification of adenocarcinoma: what the radiologists need to know.
Lee Sang Min,Goo Jin Mo,Park Chang Min,Lee Hyun-Ju,Im Jung-Gi
Diagnostic and interventional radiology (Ankara, Turkey)
The International Association for the Study of Lung Cancer, American Thoracic Society, and European Respiratory Society recently introduced a new classification of lung adenocarcinoma addressing the latest advances in oncology, molecular biology, pathology, radiology, and surgery of lung adenocarcinoma. In this classification, new uniform terminology and diagnostic criteria are described, including the introduction of adenocarcinoma in situ as a second preinvasive lesion, as well as the concept of minimally-invasive adenocarcinoma and new subtyping of invasive adenocarcinomas stratified according to predominant patterns. In addition, the previously widely-used term bronchioloalveolar carcinoma is no longer considered valid and has been recategorized. This classification also provides, for the first time, guidance for small biopsies and cytology specimens. This new classification has profound implications for radiology, as much investigation will be needed to correlate these newly introduced concepts (such as histologic subtypes) with radiologic features. Understanding the newly described concept of minimally-invasive adenocarcinoma will be essential in determining sublobar resection for adenocarcinomas. In this manuscript, we briefly review the new classification of lung adenocarcinoma and discuss its radiologic relevance to the reporting, biopsy, and future studies of adenocarcinoma.
10.4261/1305-3825.DIR.5778-12.1
CT-texture analysis of subsolid nodules for differentiating invasive from in-situ and minimally invasive lung adenocarcinoma subtypes.
Cohen J G,Reymond E,Medici M,Lederlin M,Lantuejoul S,Laurent F,Toffart A C,Moreau-Gaudry A,Jankowski A,Ferretti G R
Diagnostic and interventional imaging
PURPOSE:The purpose of this study was to evaluate the usefulness of computed tomography-texture analysis (CTTA) in differentiating between in-situ and minimally-invasive from invasive adenocarcinomas in subsolid lung nodules (SSLNs). MATERIAL AND METHODS:Two radiologists retrospectively reviewed 49 SSLNs in 44 patients. There were 27 men and 17 women with a mean age of 63±7 (SD) years (range: 47-78years). For each SSLN, type (pure ground-glass or part-solid) was assessed by consensus and CTTA was conducted independently by each observer using a filtration-histogram technique. Different filters were used before histogram quantification: no filtration, fine, medium and coarse, followed by histogram quantification using mean intensity, standard deviation (SD), entropy, mean positive pixels (MPP), skewness and kurtosis. RESULTS:We analyzed 13 pure ground-glass and 36 part-solid nodules corresponding to 16 adenocarcinomas in-situ (AIS), 5 minimally invasive adenocarcinomas (MIA) and 28 invasive adenocarcinomas (IVA). At uni- and multivariate analysis CTTA allowed discriminating between IVAs and AIS/MIA (P<0.05 and P=0.025, respectively) with the following histogram parameters: skewness using fine textures and kurtosis using coarse filtration for pure ground-glass nodules, and SD without filtration for part-solid nodules. CONCLUSION:CTTA has the potential to differentiate AIS and MIA from IVA among SSLNs. However, our results require further validation on a larger cohort.
10.1016/j.diii.2017.12.013
Computed tomography versus frozen sections for distinguishing lung adenocarcinoma: A cohort study of concordance rate.
Asian journal of surgery
BACKGROUND:Computed tomography (CT) imaging can help to predict the pathological invasiveness of early-stage lung adenocarcinoma and guide surgical resection. This retrospective study investigated whether CT imaging could distinguish pre-invasive lung adenocarcinoma from IAC. It also compared final pathology prediction accuracy between CT imaging and intraoperative frozen section analysis. METHODS:This study included 2093 patients with early-stage peripheral lung adenocarcinoma who underwent CT imaging and intraoperative frozen section analysis between March 2013 and November 2014. Nodules were classified as ground-glass (GGNs), part-solid (PSNs), and solid nodules according to CT findings; they were classified as pre-IAC and IAC according to final pathology. Univariate, multivariate, and receiver operating characteristic (ROC) curve analyses were performed to evaluate whether CT imaging could distinguish pre-IAC from IAC. The concordance rates of CT imaging and intraoperative frozen section analyses with final pathology were also compared to determine their accuracies. RESULTS:Multivariate analysis identified tumor size as an independent distinguishing factor. ROC curve analyses showed that the optimal cut-off sizes for distinguishing pre-IAC from IAC for GGNs, PSNs, and solid nodules were 10.79, 11.48, and 11.45 mm, respectively. The concordance rate of CT imaging with final pathology was significantly greater than the concordance rate of intraoperative frozen section analysis with final pathology (P = 0.041). CONCLUSION:CT imaging could distinguish pre-IAC from IAC in patients with early-stage lung adenocarcinoma. Because of its accuracy in predicting final pathology, CT imaging could contribute to decisions associated with surgical extent. Multicenter standardized trials are needed to confirm the findings in this study.
10.1016/j.asjsur.2022.03.001
The CT delta-radiomics based machine learning approach in evaluating multiple primary lung adenocarcinoma.
BMC cancer
OBJECT:To evaluate the difference between multiple primary lung adenocarcinoma (MPLA) and solitary primary lung adenocarcinoma (SPLA) by delta-radiomics based machine learning algorithms in CT images. METHODS:A total of 1094 patients containing 268 MPLAs and 826 SPLAs were recruited for this retrospective study between 2014 to 2020. After the segmentation of volume of interest, the radiomic features were automatically calculated. The patients were categorized into the training set and testing set by a random proportion of 7:3. After feature selection, the relevant classifiers were constructed by the machine learning algorithms of Bayes, forest, k-nearest neighbor, logistic regression, support vector machine, and decision tree. The relative standard deviation (RSD) was calculated and the classification model with minimal RSD was chosen for delta-radiomics analysis to explore the variation of tumor during follow-up surveillance in the cohort of 225 MPLAs and 320 SPLAs. According to the different follow-up duration, it was divided into group A (3-12 months), group B (13-24 months), and group C (25-48 months). Then the corresponding delta-radiomics classifiers were developed to predict MPLAs. The area under the receiver operator characteristic curve (AUC) with 95% confidence interval (CI) was quantified to evaluate the efficiency of the model. RESULTS:To radiomics analysis, the forest classifier (FC-radio) with the minimal RSD showed the better stability with AUCs of 0.840 (95%CI, 0.810-0.867) and 0.670 (95%CI, 0.611-0.724) in the training and testing set. The AUCs of the forest classifier based on delta-radiomics (FC-delta) were higher than those of FC-radio. In addition, with the extension of follow-up duration, the performance of FC-delta in Group C were the best with AUCs of 0.998 (95%CI, 0.993-1.000) in the training set and 0.853 (95%CI, 0.752-0.940) in the testing set. CONCLUSIONS:The machine-learning approach based on radiomics and delta-radiomics helped to differentiate SPLAs from MPLAs. The FC-delta with a longer follow-up duration could better distinguish between SPLAs and MPLAs.
10.1186/s12885-022-10036-1
Efficacy of measuring the invasive diameter of lung adenocarcinoma using mediastinal window settings: A retrospective study.
Medicine
The recently published 8th edition of the tumor node and metastasis Classification of Lung Cancer proposes using the maximum dimension of the solid component of a ground glass nodule (GGN) for the T categorization. However, few studies have investigated the collection of this information when using mediastinal window settings. In this study, we evaluated tumor measurement data obtained from computed tomography (CT) scans when using mediastinal window settings.This study included 202 selected patients with persistent, partly solid GGNs detected on thin-slice CT after surgical treatment between 2004 and 2013. We compared the differences in tumor diameters measured by 2 different radiologists using a repeated-measures analysis of variance. We divided the patients into 2 groups based on the clinical T stage (T1a+T1b vs T1c) and estimated the probability of overall survival (OS) and disease-free survival (DFS) using Kaplan-Meier curves.The study included 94 male and 108 female patients. The inter-reviewer differences between tumor diameters were significantly smaller when the consolidation to maximum tumor diameter ratio was ≤0.5. The 2 clinical groups classified by clinical T stage differed significantly with respect to DFS when using the mediastinal window settings. However, no significant differences in OS or DFS were observed when using the lung window setting.Our study yielded 2 major findings. First, the diameters of GGNs could be measured more accurately using the mediastinal window setting. Second, measurements obtained using the mediastinal window setting more clearly depicted the effect of clinical T stage on DFS.
10.1097/MD.0000000000020594
The Diagnostic Value of Quantitative CT Analysis of Ground-Glass Volume Percentage in Differentiating Epidermal Growth Factor Receptor Mutation and Subtypes in Lung Adenocarcinoma.
BioMed research international
OBJECTIVE:To retrospectively investigate computed tomographic (CT) quantitative analysis of ground-glass opacity (GGO) volume percentage and morphologic features of resected lung adenocarcinomas according to epidermal growth factor receptor () mutation status and subtypes. METHODS:Amplification refractory mutation system was used to detect mutations in the EGFR gene. Distribution of demographics and GGO volume percentage were performed according to EGFR mutation status and subtypes. RESULTS:EGFR mutations were significantly more frequent in women (55.2% vs. 37.0%, =0.001) and in never-smokers (59.5% vs. 38.4%, p < 0.001) than those without EGFR mutation. GGO volume percentage was significantly higher in tumors with EGFR mutation than in tumors without EGFR mutation (52.8±25.7% vs. 29.0±20.7%, < 0.001). The GGO volume percentages in tumors with exon 21 mutation and EGFR mutation showed a significant difference compared with those without EGFR mutation ( < 0.001, area under the curve=0.871, sensitivity=94.6%, specificity=73.8%, and p < 0.001, area under the curve=0.783, sensitivity=69.9%, specificity=75.4%, resp.), with cut-off values of 37.7% and 34.3% in receiver operating characteristic curve analysis. CONCLUSION:GGO volume percentage in adenocarcinomas with EGFR mutation was significantly higher than that in tumors without EGFR mutation, and adenocarcinomas with exon 21 mutation showed significantly higher GGO volume percentage than in tumors with exon 19 mutation and those without EGFR mutation. Our results indicate that GGO volume percentage cut-off values of more than 37.7% and 34.3% were predictors of positive exon 21 mutation and EGFR mutation, respectively.
10.1155/2019/9643836
Predicting Pathological Invasiveness of Lung Adenocarcinoma Manifesting as GGO-Predominant Nodules: A Combined Prediction Model Generated From DECT.
Wang Siqi,Liu Guoqiang,Fu Zehui,Jiang Zhenxing,Qiu Jianguo
Academic radiology
RATIONALE AND OBJECTIVES:To evaluate qualitative and quantitative indicators generated from Dual-energy computed tomography (DECT) for preoperatively differentiating between invasive adenocarcinoma (IAC) and preinvasive or minimally invasive adenocarcinoma (MIA) lesions manifesting as ground-glass opacity-predominant (GGO-predominant) nodules. MATERIALS AND METHODS:We retrospectively enrolled 143 cases of completely resected GGO-predominant lung adenocarcinoma with DECT examinations between December 2017 and July 2019. Qualitative and quantitative parameters of GGO-predominant nodules were compared after grouping nodules into IAC and preinvasive-MIA groups. A multivariate logistic regression models were used for analyzing these parameters. The diagnostic performance of different parameters was compared by receiver operating characteristic (ROC) curves and Z tests. RESULTS:This study included 137 patients (58 years ± 11; male: female = 52:91) with 143 GGO-predominant nodules. The proportion of margins, internal dilated/distorted/cut-off bronchi, internal thickened/stiff/distorted vasculature, pleural indentation, and vascular convergence were higher in the IAC group than in the preinvasive-MIA group, as were the maximum diameter (D), the diameter of the solid component (D) and the enhanced monochromatic CT value at 40 keV-190 keV (CT) (p range: 0.001-0.019). Logistic regression analyses revealed that margin, D, and CT values were independent predictors of the IAC group. The area under the curve (AUC) for the combination of margin, D, and CT was 0.896 (90.2% sensitivity, 70.7% specificity, 84.6% accuracy), which was significantly higher than that for each two of them (all p < 0.05). CONCLUSION:The combined prediction model generated from DECT allows for effective preoperative differentiation between IAC and preinvasive-MIA in GGO-predominant lung adenocarcinomas.
10.1016/j.acra.2020.03.007
Solid component ratio influences prognosis of GGO-featured IA stage invasive lung adenocarcinoma.
Sun Fenghao,Huang Yiwei,Yang Xiaodong,Zhan Cheng,Xi Junjie,Lin Zongwu,Shi Yu,Jiang Wei,Wang Qun
Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND:The computed tomography (CT) characteristic of ground glass opacity (GGO) were shown to be associated with clinical significance in lung adenocarcinoma. We evaluated the prognostic value of the solid component ratio of GGO IA invasive lung adenocarcinoma. METHODS:We retrospectively analyzed the records of GGO IA patients who received surgical resection from April 2012 to December 2015. The solid component ratio was calculated based on thin-slice CT scans. Baseline features were compared stratified by the ratio. Cox proportional hazard models and survival analyses were adopted to explore potential prognostic value regarding overall survival (OS) and disease-free survival (DFS). RESULTS:Four hundred fifteen patients were included. The higher ratio was significantly associated with larger tumor diameter, pathological subtypes and choice of surgical type. There was a significantly worse DFS with a > 50% ratio. The subgroups of 0% and ≤ 50% ratio showed close survival curves of DFS. Similar trends were observed in OS. Multivariate analyses revealed that the ratio was a significant predictor for DFS, but not for OS. No significant prognostic difference was observed between lobectomy and limited resections. CONCLUSION:A higher solid component ratio may help to predict a significantly worse prognosis of GGO IA lung adenocarcinoma.
10.1186/s40644-020-00363-6
Radiomics Approach to Prediction of Occult Mediastinal Lymph Node Metastasis of Lung Adenocarcinoma.
Zhong Yan,Yuan Mei,Zhang Teng,Zhang Yu-Dong,Li Hai,Yu Tong-Fu
AJR. American journal of roentgenology
OBJECTIVE:The purpose of this study was to evaluate the prognostic impact of radiomic features from CT scans in predicting occult mediastinal lymph node (LN) metastasis of lung adenocarcinoma. MATERIALS AND METHODS:A total of 492 patients with lung adenocarcinoma who underwent preoperative unenhanced chest CT were enrolled in the study. A total of 300 radiomics features quantifying tumor intensity, texture, and wavelet were extracted from the segmented entire-tumor volume of interest of the primary tumor. A radiomics signature was generated by use of the relief-based feature method and the support vector machine classification method. A ROC regression curve was drawn for the predictive performance of radiomics features. Multivariate logistic regression models based on clinicopathologic and radiomics features were compared for discriminating mediastinal LN metastasis. RESULTS:Clinical variables (sex, tumor diameter, tumor location) and predominant subtype were risk factors for pathologic mediastinal LN metastasis. The accuracy of radiomics signature for predicting mediastinal LN metastasis was 91.1% in ROC analysis (AUC, 0.972; sensitivity, 94.8%; specificity, 92%). Radiomics signature (Akaike information criterion [AIC] value, 80.9%) showed model fit superior to that of the clinicohistopathologic model (AIC value, 61.1%) for predicting mediastinal LN metastasis. CONCLUSION:The radiomics signature of a primary tumor based on CT scans can be used for quantitative and noninvasive prediction of occult mediastinal LN metastasis of lung adenocarcinoma.
10.2214/AJR.17.19074
Intratumoral and peritumoral CT-based radiomics strategy reveals distinct subtypes of non-small-cell lung cancer.
Journal of cancer research and clinical oncology
PURPOSE:To evaluate a new radiomics strategy that incorporates intratumoral and peritumoral features extracted from lung CT images with ensemble learning for pretreatment prediction of lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD). METHODS:A total of 105 patients (47 LUSC and 58 LUAD) with pretherapy CT scans were involved in this retrospective study, and were divided into training (n = 73) and testing (n = 32) cohorts. Seven categories of radiomics features involving 3078 metrics in total were extracted from the intra- and peritumoral regions of each patient's CT data. Student's t tests in combination with three feature selection methods were adopted for optimal features selection. An ensemble classifier was developed using five common machine learning classifiers with these optimal features. The performance was assessed using both training and testing cohorts, and further compared with that of Visual Geometry Group-16 (VGG-16) deep network for this predictive task. RESULTS:The classification models developed using optimal feature subsets determined from intratumoral region and peritumoral region with the ensemble classifier achieved mean area under the curve (AUC) of 0.87, 0.83 in the training cohort and 0.66, 0.60 in the testing cohort, respectively. The model developed by using the optimal feature subset selected from both intra- and peritumoral regions with the ensemble classifier achieved great performance improvement, with AUC of 0.87 and 0.78 in both cohorts, respectively, which are also superior to that of VGG-16 (AUC of 0.68 in the testing cohort). CONCLUSIONS:The proposed new radiomics strategy that extracts image features from the intra- and peritumoral regions with ensemble learning could greatly improve the diagnostic performance for the histological subtype stratification in patients with NSCLC.
10.1007/s00432-022-04015-z
The development and validation of a radiomic nomogram for the preoperative prediction of lung adenocarcinoma.
Liu Qin,Huang Yan,Chen Huai,Liu Yanwen,Liang Ruihong,Zeng Qingsi
BMC cancer
BACKGROUND:Accurate diagnosis of early lung cancer from small pulmonary nodules (SPN) is challenging in clinical setting. We aimed to develop a radiomic nomogram to differentiate lung adenocarcinoma from benign SPN. METHODS:This retrospective study included a total of 210 pathologically confirmed SPN (≤ 10 mm) from 197 patients, which were randomly divided into a training dataset (n = 147; malignant nodules, n = 94) and a validation dataset (n = 63; malignant nodules, n = 39). Radiomic features were extracted from the cancerous volumes of interest on contrast-enhanced CT images. The least absolute shrinkage and selection operator (LASSO) regression was used for data dimension reduction, feature selection, and radiomic signature building. Using multivariable logistic regression analysis, a radiomic nomogram was developed incorporating the radiomic signature and the conventional CT signs observed by radiologists. Discrimination and calibration of the radiomic nomogram were evaluated. RESULTS:The radiomic signature consisting of five radiomic features achieved an AUC of 0.853 (95% confidence interval [CI]: 0.735-0.970), accuracy of 81.0%, sensitivity of 82.9%, and specificity of 77.3%. The two conventional CT signs achieved an AUC of 0.833 (95% CI: 0.707-0.958), accuracy of 65.1%, sensitivity of 53.7%, and specificity of 86.4%. The radiomic nomogram incorporating the radiomic signature and conventional CT signs showed an improved AUC of 0.857 (95% CI: 0.723-0.991), accuracy of 84.1%, sensitivity of 85.4%, and specificity of 81.8%. The radiomic nomogram had good calibration power. CONCLUSION:The radiomic nomogram might has the potential to be used as a non-invasive tool for individual prediction of SPN preoperatively. It might facilitate decision-making and improve the management of SPN in the clinical setting.
10.1186/s12885-020-07017-7
Correlation between expression of Ki-67 and MSCT signs in different types of lung adenocarcinoma.
Medicine
To investigate the correlation between the proliferating cell nuclear antigen Ki-67 and the multislice computed tomography (MSCT) signs in different subtypes of lung adenocarcinoma.Ninety-five patients with lung adenocarcinoma confirmed by surgical pathology and treated between January 2017 and December 2017 were included. MSCT was performed before the operation, and the characteristics of the high-resolution CT (HRCT) signs of the lesions were compared with the Ki-67 immunohistochemistry results.The levels of Ki-67 in the 95 lung adenocarcinoma specimens were positively correlated with the malignancy of lung adenocarcinoma. Spearman correlation coefficient was 0.615. The expression of Ki-67 was positively correlated with the nodules' diameter, density, and lobulated sign, with Spearman correlation coefficients of 0.58, 0.554, and 0.436. There was no significant correlation with spiculation and pleural retraction, with correlation coefficients of 0.319/0.381.These findings suggest that the MSCT signs of different types of lung adenocarcinoma might be associated with the expression of Ki-67. Without replacing biopsy, the imaging features of pulmonary nodules could be comprehensively analyzed to evaluate the proliferation potential of preoperative nodules, but additional studies are needed for confirmation.
10.1097/MD.0000000000018678
[Clinical value of a differentiation prediction model for invasive lung adenocarcinoma].
Zhonghua zhong liu za zhi [Chinese journal of oncology]
To investigate the value of predicting the degree of differentiation of pulmonary invasive adenocarcinoma (IAC) based on CT image radiomics model and the expression difference of immunohistochemical factors between different degrees of differentiation of lesions. The clinicopathological data of patients with pulmonary IAC confirmed by surgical pathology in the Affiliated Huai'an First People's Hospital to Nanjing Medical University from December 2017 to September 2018 were collected. High-throughput feature acquisition was performed for all outlined regions of interest, and prediction models were constructed after dimensionality reduction by the minimum absolute shrinkage operator. Receiver operating characteristic curve was used to assess the predictive efficacy of clinical characteristic model, radiomics model and individualized prediction model combined with both to identify the degree of pulmonary IAC differentiation, and immunohistochemical expressions of Ki-67, NapsinA and TTF-1 were compared between groups with different degrees of IAC differentiation using rank sum test. A total of 396 high-throughput features were extracted from all IAC lesions, and 10 features with high generalization ability and correlation with the degree of IAC differentiation were screened. The mean radiomics score of poorly differentiated IAC in the training group (1.206) was higher than that of patients with high and medium differentiation (0.969, =0.001), and the mean radiomics score of poorly differentiated IAC in the test group (1.545) was higher than that of patients with high and medium differentiation (-0.815, <0.001). The differences in gender (<0.001), pleural stretch sign (=0.005), and burr sign (=0.033) were statistically significant between patients in the well and poorly differentiated IAC groups. Multifactorial logistic regression analysis showed that gender and pleural stretch sign were related to the degree of IAC differentiation (<0.05). The clinical feature model consisted of age, gender, pleural stretch sign, burr sign, tumor vessel sign, and vacuolar sign, and the individualized prediction model consisted of gender, pleural stretch sign, and radiomic score, and was represented by a nomogram. The Akaike information standard values of the radiomics model, clinical feature model and individualized prediction model were 54.756, 82.214 and 53.282, respectively. The individualized prediction model was most effective in identifying the degree of differentiation of pulmonary IAC, and the area under the curves (AUC) of the individualized prediction model in the training group and the test group were 0.92 (95% 0.86-0.99) and 0.88 (95% 0.74-1.00, respectively). The AUCs of the radiomics group model for predicting the degree of differentiation of pulmonary IAC in the training group and the test group were 0.91 (95% 0.83-0.98) and 0.87 (95% 0.72-1.00), respectively. The AUCs of the clinical characteristics model for predicting the degree of differentiation of pulmonary IACs in the training and test groups were 0.75 (95% 0.63-0.86) and 0.76 (95% 0.59-0.94), respectively. The expression level of Ki-67 in poorly differentiated IAC was higher than that in well-differentiated IAC (<0.001). The expression levels of NapsinA, TTF-1 in poorly differentiated IAC were higher than those in well-differentiated IAC (<0.05). Individualized prediction model consisted of gender, pleural stretch sign and radiomics score can discriminate the differentiation degree of IAC with the best performance in comparison with clinical feature model and radiomics model. Ki-67, NapsinA and TTF-1 express differently in different degrees of differentiation of IAC.
10.3760/cma.j.cn112152-20200102-00002
[Clinical Characteristics and Prognosis of Sub-centimeter Lung Adenocarcinoma].
Mi Jiahui,Wang Shaodong,Li Xiao,Jiang Guanchao
Zhongguo fei ai za zhi = Chinese journal of lung cancer
BACKGROUND:With the increase of lung cancer screening, more and more patients have been diagnosed as sub-centimeter (≤1 cm) lung adenocarcinoma. Sub-centimeter lung adenocarcinoma is mostly early stage lung cancer, but the research on sub-centimeter lung adenocarcinoma is still insufficient. This study analyzed the clinical characteristics and prognosis of patients with sub-centimeter lung adenocarcinoma in order to provide the basis for the diagnosis and treatment of such patients. METHODS:A retrospective study was performed to analyze patients with sub-centimeter lung adenocarcinoma who underwent VATS in Peking University People's Hospital from January 2012 to December 2016. Patients were divided into pure ground-glass nodules (pGGN) group, mixed ground-glass nodules (mGGN) group and solid nodules (SN) group according to the features of nodular imaging. The clinical characteristics of the three groups were compared and the subgroup analysis of nodules in different diameter was performed. We also performed multivariate logistic regression analyses to identify the risk factors for sub-centimeter lung invasive adenocarcinoma. RESULTS:The study included 182 patients (57 men and 125 women) with a median age of 54 (27-75) years. Female sub-centimeter lung adenocarcinoma patients had a significantly lower proportion of non-smoking history than males (P<0.001). All patients with 1 mm-10 mm pGGN, 1 mm-5 mm mGGN and 1 mm-5 mm SN had no other pathologically positive findings except for the primary lesion. Of the 46 patients with 6 mm-10 mm mGGN, 3 had pleural invasion and 1 had vascular tumor thrombus. Of the 39 patients with 6 mm-10 mm SN, 5 had pleural invasion, 2 had vascular tumor thrombus and 2 had lymph node metastasis. The pathological type in each patient with pleural invasion, vascular tumor thrombus or lymph node metastasis was invasive adenocarcinoma. Logistic regression analysis indicated that smoking history (OR=4.727, P=0.009), previous tumor history (OR=3.408, P=0.015), mGGN (OR=3.735, P=0.004), SN (OR=8.921, P<0.001) and tumor diameter >5 mm (OR=4.241, P=0.001) were independent risk factors for sub-centimeter lung invasive adenocarcinoma. The median follow-up time was 44 (22-82) months. The 5-year recurrence-free survival rate was 100.0% and the overall survival rate was 98.9%. CONCLUSIONS:Patients with sub-centimeter lung adenocarcinoma have a relatively earlier onset age. Sub-centimeter lung invasive adenocarcinoma patients with 6 mm-10 mm mGGN and 6 mm-10 mm SN may be involved in pleural invasion or lymph node metastasis. Smoking history, previous tumor history, mGGN, SN and tumor diameter >5 mm are independent risk factors for sub-centimeter lung invasive adenocarcinoma. For patients with sub-centimeter lung adenocarcinoma, early detection and appropriate surgical intervention can lead to a good prognosis.
10.3779/j.issn.1009-3419.2019.08.04
Prediction of the Invasiveness of Ground-Glass Nodules in Lung Adenocarcinoma by Radiomics Analysis Using High-Resolution Computed Tomography Imaging.
Cancer control : journal of the Moffitt Cancer Center
BACKGROUND:Pure ground-glass nodules (pGGNs) have been considered inert tumors due to their biological behavior; however, their prognosis is not completely consistent because of differences in internal pathological component. The aim of this study was to explore whether radiomics can be used to identify the invasiveness of pGGNs. METHODS:The retrospective study received the relevant ethical approval. After postoperative pathological confirmation, sixty-five patients with lung adenocarcinoma pGGNs (≤30 mm) were enrolled in this study from January 2015 to October 2018. All the cases were randomly divided into training and test groups in a 7:3 ratio. In total, 385 radiomics features were obtained from HRCT images, and then least absolute shrinkage and selection operator (LASSO) logistic regression was applied to the training group to obtain optimal features to distinguish the invasion degree of lesions. The diagnostic efficiency of the radiomics model was estimated by the area under the curve (AUC) of the receiver operating curve (ROC), and verified by the test group. RESULTS:The optimal features ("GLCMEntropy_angle135_offset1" and "Sphericity") were selected after applying the LASSO regression to develop the proposed radiomics model. This prediction model exhibited good differentiation between pre-invasive and invasive lesions. The AUC for the test group was 0.824 (95%CI: 0.599-1.000), indicating that the radiomics model has some prediction ability. CONCLUSION:The HRCT radiomics features can discriminate pre-invasive from invasive lung adenocarcinoma pGGNs. This non-invasive method can provide more information for surgeons before operation, and can also predict the prognosis of patients to some extent.
10.1177/10732748221089408
CT-based radiomics signature for differentiating solitary granulomatous nodules from solid lung adenocarcinoma.
Yang Xinguan,He Jianxing,Wang Jiao,Li Weiwei,Liu Chunbo,Gao Dashan,Guan Yubao
Lung cancer (Amsterdam, Netherlands)
OBJECTIVES:Pulmonary granulomatous nodule (GN) with spiculated or lobulated appearance are indistinguishable from solid lung adenocarcinoma (SADC) based on CT morphological features, and partial false-positive findings on PET/CT. The objective of this study was to investigate the ability of quantitative CT radiomics for preoperatively differentiating solitary atypical GN from SADC. METHODS:302 eligible patients (SADC = 209, GN = 93) were evaluated in this retrospective study and were divided into training (n = 211) and validation cohorts (n = 91). Radiomics features were extracted from plain and vein-phase CT images. The L1 regularized logistic regression model was used to identify the optimal radiomics features for construction of a radiomics model in differentiate solitary GN from SADC. The performance of the constructed radiomics model was evaluated using the area under curve (AUC) of receiver operating characteristic curve (ROC). RESULTS:16.7% (35/209) of SADC were misdiagnosed as GN and 24.7% (23/93) of GN were misdiagnosed as lung cancer before surgery. The AUCs of combined radiomics and clinical risk factors were 0.935, 0.902, and 0.923 in the training cohort of plain radiomics(PR), vein radiomics, and plain and vein radiomics, and were 0.817, 0835, and 0.841 in the validation cohort of three models, respectively. PR combined with clinical risk factors (PRC) performed better than simple radiomics models (p < 0.05). The diagnostic accuracy of PRC in the total cohorts was similar to our radiologists (p ≥ 0.05). CONCLUSIONS:As a noninvasive method, PRC has the ability to identify SADC and GN with spiculation or lobulation.
10.1016/j.lungcan.2018.09.013
[Adenocarcinoma of the lung against the background of usual interstitial pneumonia].
Arkhiv patologii
Lung adenocarcinoma against the background of idiopathic pulmonary fibrosis according to the world literature ranges from 2.7% to 48%, the incidence increases every year after the diagnosis of idiopathic pulmonary fibrosis. We present a clinical and morphological analysis of an autopsy observation of lung adenocarcinoma that developed against the background of corticosteroid-treated usual interstitial pneumonia in a 78-year-old woman. According to the results of histological and immunohistochemical studies, the diagnosis was formulated as: multicentric non-mucinous invasive adenocarcinoma of the right and left lungs with a lepidic growth pattern with background of usual interstitial pneumonia.
10.17116/patol20228405135
Correlation Analysis of Computed Tomography Features and Pathological Types of Multifocal Ground-Glass Nodular Lung Adenocarcinoma.
Computational and mathematical methods in medicine
To investigate the correlation between computed tomography (CT) image characteristics of multiple lung ground-glass nodules (GGNs) and pathological classification, the CT image data of multiple lung GGN patients confirmed by pathology ( = 132) in our hospital were collected. The imaging features of GGNs were analyzed by qualified physicians, including lesion size (diameter, volume, and mass), location, CT values (mean and relative CT values), lesion morphology (round and irregular), marginal structure (pagination and burr), internal structure (bronchial inflation sign), and adjacent structure (pleural depression). CT imaging analysis was performed for the subtype of infiltrating adenocarcinoma (IAC). In CT findings, GGNs were greatly different from adenomatous hyperplasia (AAH), pure GGN adenocarcinoma in situ (AIS), and microinvasive adenocarcinoma (MIA) in terms of marginal structure, lesion morphology, internal structure, adjacent structure, and size ( < 0.05). The mean and relative CT values of mural adenocarcinoma, acinar adenocarcinoma, and papillary adenocarcinoma of IAC subtypes were greatly different from those of AAH/AIS/MIA ( < 0.05). In summary, the CT images of GGNs can be used as the basis for the differentiation of AAH, AIS, and MIA early noninvasive types and IAC invasive types, and the CT value of the IAC subtype can be used as the basis for the classification and differentiation of IAC pathological subtypes.
10.1155/2022/7267036
Profiles of Lung Adenocarcinoma With Multiple Ground-Glass Opacities and the Fate of Residual Lesions.
Shimada Yoshihisa,Maehara Sachio,Kudo Yujin,Masuno Ryuhei,Yamada Takafumi,Hagiwara Masaru,Kakihana Masatoshi,Kajiwara Naohiro,Ohira Tatsuo,Ikeda Norihiko
The Annals of thoracic surgery
BACKGROUND:We aimed to clarify clinical profiles of patients with adenocarcinoma presenting as multifocal ground-glass opacities (MGGOs) to assess their prognosis and the optimal management method for residual satellite lesions. METHODS:We identified 190 patients with cN0 MGGOs (MGGO cohort) and 1426 patients with solitary lung adenocarcinoma (control cohort) who underwent complete resection between 2004 and 2016. Propensity score matching was performed to adjust for differences in baseline characteristics of both cohorts for survival analyses. MGGOs consist of a main tumor and satellite lesions and were subdivided into 3 groups: the PG group, with multifocal pure GGOs; the GD group, in which the main tumor presented as GGO dominant; and the SD group, where the main tumor presented as solid dominant. RESULTS:No significant differences in recurrence-free survival were observed between the 2 cohorts before and after the propensity score matching. For patients with MGGOs, 22 were in the PG group, 47 in the GD group, and 121 in the SD group. Type of MGGOs was a significant factor for recurrence-free survival recurrence-free survival both in the entire population (SD vs PG-GD, P = .008) and in p-stage I cohorts (P = .004) on multivariable analysis. Among 116 patients (61.1%) with residual satellite lesions, 38 patients had progressed lesions and 69 stable lesions. Although the emergence of new lesions during the follow-up period was an independent predictor for satellite lesion progression, neither progressed lesions nor the emergence of new lesions influenced survival. CONCLUSIONS:Patients with MGGOs and solitary adenocarcinoma had a similar prognosis. The biologic behavior of main tumors dominates clinical outcomes in patients with MGGOs.
10.1016/j.athoracsur.2019.12.062
Performance of lung cancer screening with low-dose CT in Gejiu, Yunnan: A population-based, screening cohort study.
Wei Meng-Na,Su Zheng,Wang Jian-Ning,Gonzalez Mendez Maria J,Yu Xiao-Yun,Liang Hao,Zhou Qing-Hua,Fan Ya-Guang,Qiao You-Lin
Thoracic cancer
BACKGROUND:The performance of lung cancer screening with low-dose computed tomography (CT) (LDCT) in China is uncertain. This study aimed to evaluate the performance of LDCT lung cancer screening in the Chinese setting. METHODS:In 2014, a screening cohort of lung cancer with LDCT was established in Gejiu, Yunnan Province, a screening center of the Lung Cancer Screening Program in Rural China (LungSPRC). Participants received a baseline screening and four rounds of annual screening with LDCT in two local hospitals until June 2019. We analyzed the rates of participation, detection, early detection, and the clinical characteristics of lung cancer. RESULTS:A total of 2006 participants had complete baseline screening results with a compliance rate of 98.4%. Of these, 1411 were high-risk and 558 were nonhigh-risk participants. During this period, 40 lung cancer cases were confirmed, of these, 35 were screen-detected, four were post-screening and one was an interval case. The positive rate of baseline and annual screening was 9.7% and 9.0%, while the lung cancer detection rate was 0.4% and 0.6%, respectively. The proportion of early lung cancer increased from 37.5% in T0 to 75.0% in T4. Adenocarcinoma was the most common histological subtype. Lung cancer incidence according to the criteria of LungSPRC and National Lung Cancer Screening Trial (NLST) was 513.31 and 877.41 per 100 000 person-years, respectively. CONCLUSIONS:The program of lung cancer screening with LDCT showed a successful performance in Gejiu, Yunnan. However, further studies are warranted to refine a high-risk population who will benefit most from LDCT screening and reduce the high false positive results. KEY POINTS:This study reports the results of lung cancer screening with LDCT in Gejiu, Yunnan, a high-risk area of lung cancer, and it demonstrates that lung cancer screening with LDCT is effective in detecting early-stage lung cancer. Our program provides an opportunity to explore the performance of LDCT lung cancer screening in the Chinese context.
10.1111/1759-7714.13379
Predictability and Utility of Contrast-Enhanced CT on Occult Lymph Node Metastasis for Patients with Clinical Stage IA-IIA Lung Adenocarcinoma: A Double-Center Study.
Academic radiology
RATIONALE AND OBJECTIVES:With the advantage of minimizing damage and preserving more functional lung tissue, limited surgery is considered depend on the lymph node (LN) involvement situation. However, occult lymph node metastasis (OLM) may be ignored by limited surgery and become a risk factor for local recurrence after surgical resection. The aim of this study was to assess the risk factors for OLM based on computed tomography enhanced image in patients with clinical lung adenocarcinoma (ADC). MATERIALS AND METHODS:From January 2016 to July 2022, 707 patients with clinical stage IA-IIA ADC underwent lobectomy with systematic LN dissection and were divided into training and validation group based on different institution. Univariate analysis followed by multivariable logistic regression were performed to estimate different risk factors of OLM. A predictive model was established with visual nomogram and external validation, and evaluated in terms of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). RESULTS:Fifty-nine patients were diagnosed with OLM (11.9%), and four independent predictors of LN involvement were identified: larger consolidation diameter (odds ratio [OR], 2.35, 95% confidence interval [CI]: 1.06, 5.22, p = 0.013), bronchovascular bundle thickening (OR, 1.99, 95% CI: 1.00, 3.95, p = 0.049), lobulation (OR, 2.92, 95% CI: 1.22, 6.99, p = 0.016) and obstructive change (OR, 1.69, 95% CI: 1.17, 6.16, p = 0.020). The model showed good calibration (Hosmer-Lemeshow goodness-of-fit, p = 0.816) with an AUC of 0.821 (95% CI: 0.775, 0.853). For the validation group, the AUC was 0.788 (95% CI: 0.732, 0.806). CONCLUSION:Our predictive model can non-invasively assess the risk of OLM in patients with clinical stage IA-IIA ADC, enable surgeons perform an individualized prediction preoperatively, and assist the clinical decision-making procedure.
10.1016/j.acra.2023.03.002
Lung Adenocarcinoma at CT with 0.25-mm Section Thickness and a 2048 Matrix: High-Spatial-Resolution Imaging for Predicting Invasiveness.
Yanagawa Masahiro,Tsubamoto Mitsuko,Satoh Yukihisa,Hata Akinori,Miyata Tomo,Yoshida Yuriko,Kikuchi Noriko,Kurakami Hiroyuki,Tomiyama Noriyuki
Radiology
Background High-spatial-resolution (HSR) CT provides detailed information and clear delineation of lung anatomy and disease states. HSR CT may have high diagnostic performance for predicting invasiveness of lung adenocarcinoma. Purpose To examine the diagnostic performance of HSR CT in predicting the invasiveness of lung adenocarcinoma. Materials and Methods In this retrospective study, 89 consecutive patients with adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IVA) were included who underwent surgery for lung cancer between January 2018 and December 2019. All patients underwent HSR CT with 0.25-mm section thickness and a 2048 matrix. Two independent observers evaluated the images for the presence or absence of the following HSR CT findings: lobulation, spiculation, pleural indentation, vessel convergence, homogeneity of ground-glass opacity, reticulation, irregularity and centrality of solid portion, and air bronchiologram (irregularity, disruption, or dilatation). The total diameter (≤1.6 cm or >1.6 cm) and the longest diameter of the solid portion (≤0.8 cm or >0.8 cm) were evaluated. Logistic regression models were used to identify findings associated with MIA plus IVA. Receiver operating characteristic analysis was performed to determine diagnostic performance. Results Eighty-nine patients (mean, 69 years ± 11 [standard deviation]; 49 men) were evaluated. The size of the nodules with invasion was a mean of 2.5 cm ± 1.2. Univariable analysis revealed lobulation, spiculation, pleural indentation, irregular and central solid portion, air bronchiologram with disruption and/or irregular dilatation, and total and solid portion diameters as associated with MIA plus IVA (all, < .05). After adjustment for age, sex, and pack-years of smoking, disruption of air bronchogram and solid portion diameter greater than 0.8 cm remained as predictors of invasiveness ( = .001 and = .02, respectively). The diagnostic performance of these two findings combined were as follows: sensitivity of 97% (59 of 61 patients; 95% confidence interval: 94%, 100%) and specificity of 86% (19 of 22 patients; 95% confidence interval: 65%, 97%), with an area under the curve of 0.94. Conclusion Using high-spatial-resolution CT, disruption of air bronchiologram and a solid portion greater than 0.8 cm were independently associated with a greater likelihood of invasiveness in lung adenocarcinoma. © RSNA, 2020 See also the editorial by Lynch and Oh in this issue.
10.1148/radiol.2020201911
Differentiation of focal organising pneumonia and peripheral adenocarcinoma in solid lung lesions using thin-section CT-based radiomics.
Zhang T,Yuan M,Zhong Y,Zhang Y-D,Li H,Wu J-F,Yu T-F
Clinical radiology
AIM:To evaluate the predictive role of radiomics based on computed tomography (CT) in discriminating focal organising pneumonia (FOP) from peripheral lung adenocarcinoma (LA). MATERIALS AND METHODS:Institutional research board approval was obtained for this retrospective study. One hundred and seventeen patients with FOP and 109 patients with LA who underwent thin-section CT from January 2011 to August 2017 were reviewed systematically and analysed. The clinical and radiological features were established as model A and multi-feature-based radiomics as model B. The diagnostic performance of model A, model B, and model A+B were evaluated and compared via receiver operating characteristic (ROC) curve analysis and logistic regression analysis. RESULTS:Sex, symptoms, necrosis, and the halo sign were identified as independent predictors of LA. The area under the ROC curve (Az value), accuracy, sensitivity, and specificity of model A were 0.839, 75.7%, 82.6%, and 69.2% respectively. Model B showed significantly higher accuracy than model A (83.6% versus 75.7%, p=0.032). The top four best-performing features, WavEnLH_s-3, WavEnHH_s-3, Teta3, and Volume, performed as independent factors for discriminating LA. Regression analysis indicated that model B had superior model fit than model A with Akaike information criterion (AIC) values of 73.6% versus 59.1%, respectively. Combining model A with model B is useful in achieving better diagnostic performance in discriminating FOP from LA: the Az value, accuracy, sensitivity, and specificity were 0.956, 87.6%, 85.3%, and 89.7% respectively. CONCLUSIONS:Radiomics based on CT exhibited better diagnostic accuracy and model fit than clinical and radiological features in discriminating FOP from LA. Combination of both achieved better diagnostic performance.
10.1016/j.crad.2018.08.014
Prognostic role of standard uptake value according to pathologic features of lung adenocarcinoma.
Tumori
OBJECTIVE:To evaluate the influence of lung adenocarcinoma second predominant pattern on the maximal standard uptake value (SUVmax) and its prognostic effect in different histologic groups. METHODS:We retrospectively collected surgically resected pathologic stage I and II lung adenocarcinoma from nine European institutions. Only patients who underwent preoperative PET-CT and with available information regarding SUVmax of T (SUVmaxT) and N1 (SUVmaxN1) component were included. RESULTS:We enrolled 344 patients with lung adenocarcinoma. SUVmaxT did not show any significant relation according to the second predominant pattern ( = 0.139); this relationship remained nonsignificant in patients with similar predominant pattern. SUVmaxT influenced the disease-free survival in the whole cohort ( = 0.002) and in low- and intermediate-grade predominant pattern groups ( = 0.040 and = 0.008, respectively). In the high-grade predominant pattern cohort and in the pathologic N1 cases, SUVmaxT lost its prognostic power. SUVmaxN1 did not show any significant correlation with predominant and second predominant patterns and did not have any prognostic impact on DFS. CONCLUSIONS:SUVmaxT is influenced only by the adenocarcinoma predominant pattern, but not by second predominant pattern. Concurrently, in high-grade predominant pattern and pN1 group the prognostic power of SUVmaxT becomes nonsignificant.
10.1177/03008916211018515
CT quantitative parameters to predict the invasiveness of lung pure ground-glass nodules (pGGNs).
Han L,Zhang P,Wang Y,Gao Z,Wang H,Li X,Ye Z
Clinical radiology
AIM:To investigate the value of computed tomography (CT) quantitative parameters in predicting the invasiveness of lung pure ground-glass nodules (pGGNs). MATERIALS AND METHODS:Chest CT images and pathological findings of 163 pGGNs in 154 consecutive patients were reviewed. According to the clinical management strategies, cases were divided into pre-invasive and MIA groups (atypical adenomatous hyperplasia [AAH], adenocarcinoma in situ [AIS], and minimally invasive adenocarcinoma [MIA]) and invasive group (invasive adenocarcinoma [IAC]). CT quantitative parameters including maximum diameter, largest diameter perpendicular to the maximum diameter, maximum cross-sectional area, volume, mass, and mean attenuation value were measured and compared between two groups. Their diagnostic performances were evaluated using receiver operating characteristic (ROC) and logistic regression analysis. RESULTS:Significant differences existed for all the CT quantitative parameters in both groups (p<0.01). The values of area under the curve (AUC) were 0.783 of maximum diameter (95% CI: 0.711-0.843), 0.779 of longest diameter perpendicular to maximum diameter (95% CI: 0.707-0.840), 0.796 of largest cross-sectional area (95% CI: 0.726-0.855), 0.781 of volume (95% CI: 0.710-0.842), 0.794 of mass (95% CI: 0.722-0.865) and 0.625 of mean attenuation value (95% CI: 0.546-0.700), respectively. A pairwise-manner comparison showed the AUC of mean attenuation value was the smallest (p<0.01). Logistic regression analysis showed the largest cross-sectional area (OR=2.307, 95% CI: 1.689-3.150) was the independent predictor for IAC with a cut-off value of 2.22 cm. CONCLUSIONS:CT quantitative parameters could predict the invasiveness of lung pGGNs. The largest cross-sectional area is the most valuable independent predictor and the mean attenuation value is less valuable.
10.1016/j.crad.2017.12.021
Relationships between SUVmax of lung adenocarcinoma and different T stages, histological grades and pathological subtypes: a retrospective cohort study in China.
BMJ open
OBJECTIVES:Cancer cell has aberrant metabolism. The purpose of this study aimed to investigate relationships between maximum standard uptake value (SUVmax)of fluoro-2-deoxy-d-glucose and T stages, histological grades and pathological subtypes of lung adenocarcinoma. DESIGN:Retrospective cohort study, employing the Kruskal-Wallis, Bonferroni-Dunn and Mann-Whitney tests to compare SUVmax of different T stages, histological grades and pathological subtypes of lung adenocarcinoma. SETTING:The outpatients who had aberrant positron emission tomography/CT (PET/CT) images in chest were enrolled this study from August 2016 to November 2018 in Shanghai, China. PARTICIPANT:Initial 11 270 patients with suspected lung cancer who underwent PET/CT examinations were surveyed. A total of 1454 patients who were diagnosed as lung adenocarcinoma by pathologist were included in this project. PRIMARY OUTCOME MEASURES:SUVmax value at different tumour-node-metastasis stages of lung adenocarcinoma before surgery. RESULTS:The mean SUVmax of patients with lung adenocarcinoma was significantly elevated with the increase in T stages. There were significant evident differences in SUVmax among T1a-T1c (p<0.05). However, after the staging of patients was more than T1 stage, SUVmax of T2a, T2b, T2 visceral pleural invasion, T3 and T4 had not dramatic changes. SUVmax value of lung adenocarcinoma in the same T stage group was the highest in patients with the high grade of malignancy and solid-predominant invasive adenocarcinoma. CONCLUSIONS:SUVmax value was significantly associated with T stages, grades of malignancy and pathological subtypes of lung adenocarcinoma.
10.1136/bmjopen-2021-056804
A nomogram for predicting invasiveness of lung adenocarcinoma manifesting as pure ground-glass nodules: incorporating subjective CT signs and histogram parameters based on artificial intelligence.
Journal of cancer research and clinical oncology
PURPOSE:To construct a nomogram based on subjective CT signs and artificial intelligence (AI) histogram parameters to identify invasiveness of lung adenocarcinoma presenting as pure ground-glass nodules (pGGNs) and to evaluate its diagnostic performance. METHODS:187 patients with 228 pGGNs confirmed by postoperative pathology were collected retrospectively and divided into pre-invasive group [atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS)] and invasive group [minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC)]. All pGGNs were randomly assigned to training cohort (n = 160) and validation cohort (n = 68). Nomogram was developed using subjective CT signs and AI-based histogram parameters by logistic regression analysis. The diagnostic performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) curve. RESULTS:The nomogram was constructed with nodule shape, 3D mean diameter, maximum CT value, and skewness. It showed better discriminative power in differentiating invasive lesions from pre-invasive lesions with area under curve (AUC) of 0.849 (95% CI 0.790-0.909) in the training cohort and 0.831 (95% CI 0.729-0.934) in the validation cohort, which performed better than nodule shape (AUC 0.675, 95% CI 0.609-0.741), 3D mean diameter (AUC 0.762, 95% CI 0.688-0.835), maximum CT value (AUC 0.794, 95% CI 0.727-0.862), or skewness (AUC 0.594, 95% CI 0.506-0.682) alone in training cohort (for all, P < 0.05). CONCLUSION:For pulmonary pGGNs, the nomogram based on subjective CT signs and AI histogram parameters had a good predictive ability to discriminate invasive lung adenocarcinoma from pre-invasive lung adenocarcinoma, and it has the potential to improve diagnostic efficiency and to help the patient management.
10.1007/s00432-023-05262-4
Preoperative CT-based radiomics combined with tumour spread through air spaces can accurately predict early recurrence of stage I lung adenocarcinoma: a multicentre retrospective cohort study.
Cancer imaging : the official publication of the International Cancer Imaging Society
OBJECTIVE:To develop and validate a prediction model for early recurrence of stage I lung adenocarcinoma (LUAD) that combines radiomics features based on preoperative CT with tumour spread through air spaces (STAS). MATERIALS AND METHODS:The most recent preoperative thin-section chest CT scans and postoperative pathological haematoxylin and eosin-stained sections were retrospectively collected from patients with a postoperative pathological diagnosis of stage I LUAD. Regions of interest were manually segmented, and radiomics features were extracted from the tumour and peritumoral regions extended by 3 voxel units, 6 voxel units, and 12 voxel units, and 2D and 3D deep learning image features were extracted by convolutional neural networks. Then, the RAdiomics Integrated with STAS model (RAISm) was constructed. The performance of RAISm was then evaluated in a development cohort and validation cohort. RESULTS:A total of 226 patients from two medical centres from January 2015 to December 2018 were retrospectively included as the development cohort for the model and were randomly split into a training set (72.6%, n = 164) and a test set (27.4%, n = 62). From June 2019 to December 2019, 51 patients were included in the validation cohort. RAISm had excellent discrimination in predicting the early recurrence of stage I LUAD in the training cohort (AUC = 0.847, 95% CI 0.762-0.932) and validation cohort (AUC = 0.817, 95% CI 0.625-1.000). RAISm outperformed single modality signatures and other combinations of signatures in terms of discrimination and clinical net benefits. CONCLUSION:We pioneered combining preoperative CT-based radiomics with STAS to predict stage I LUAD recurrence postoperatively and confirmed the superior effect of the model in validation cohorts, showing its potential to assist in postoperative treatment strategies.
10.1186/s40644-023-00605-3
Radiologic Identification of Pathologic Tumor Invasion in Patients With Lung Adenocarcinoma.
JAMA network open
Importance:It is currently unclear whether high-resolution computed tomography can preoperatively identify pathologic tumor invasion for ground-glass opacity lung adenocarcinoma. Objectives:To evaluate the diagnostic value of high-resolution computed tomography for identifying pathologic tumor invasion for ground-glass opacity featured lung tumors. Design, Setting, and Participants:This prospective, multicenter diagnostic study enrolled patients with suspicious malignant ground-glass opacity nodules less than or equal to 30 mm from November 2019 to July 2021. Thoracic high-resolution computed tomography was performed, and pathologic tumor invasion (invasive adenocarcinoma vs adenocarcinoma in situ or minimally invasive adenocarcinoma) was estimated before surgery. Pathologic nonadenocarcinoma, benign diseases, or those without surgery were excluded from analyses; 673 patients were recruited, and 620 patients were included in the analysis. Statistical analysis was performed from October 2021 to January 2022. Exposure:Patients were grouped according to pathologic tumor invasion. Main Outcomes and Measures:Primary end point was diagnostic yield for pathologic tumor invasion. Secondary end point was diagnostic value of radiologic parameters. Results:Among 620 patients (442 [71.3%] female; mean [SD] age, 53.5 [12.0] years) with 622 nodules, 287 (46.1%) pure ground-glass opacity nodules and 335 (53.9%) part-solid nodules were analyzed. The median (range) size of nodules was 12.1 (3.8-30.0) mm; 47 adenocarcinomas in situ, 342 minimally invasive adenocarcinomas, and 233 invasive adenocarcinomas were confirmed. Overall, diagnostic accuracy was 83.0% (516 of 622; 95% CI, 79.8%-85.8%), diagnostic sensitivity was 82.4% (192 of 233; 95% CI, 76.9%-87.1%), and diagnostic specificity was 83.3% (324 of 389; 95% CI, 79.2%-86.9%). For tumors less than or equal to 10 mm, 3.6% (8 of 224) were diagnosed as invasive adenocarcinomas. The diagnostic accuracy was 96.0% (215 of 224; 95% CI, 92.5%-98.1%), diagnostic specificity was 97.2% (210 of 216; 95% CI, 94.1%-99.0%); for tumors greater than 20 mm, 6.9% (6 of 87) were diagnosed as adenocarcinomas in situ or minimally invasive adenocarcinomas. The diagnostic accuracy was 93.1% (81 of 87; 95% CI, 85.6%-97.4%) and diagnostic sensitivity was 97.5% (79 of 81; 95% CI, 91.4%-99.7%). For tumors between 10 to 20 mm, the diagnostic accuracy was 70.7% (220 of 311; 95% CI, 65.3%-75.7%), diagnostic sensitivity was 75.0% (108 of 144; 95% CI, 67.1%-81.8%), and diagnostic specificity was 67.1% (112 of 167; 95% CI, 59.4%-74.1%). Tumor size (odds ratio, 1.28; 95% CI, 1.18-1.39) and solid component size (odds ratio, 1.31; 95% CI, 1.22-1.42) could each independently serve as identifiers of pathologic invasive adenocarcinoma. When the cutoff value of solid component size was 6 mm, the diagnostic sensitivity was 84.6% (95% CI, 78.8%-89.4%) and specificity was 82.9% (95% CI, 75.6%-88.7%). Conclusions and relevance:In this diagnostic study, radiologic analysis showed good performance in identifying pathologic tumor invasion for ground-glass opacity-featured lung adenocarcinoma, especially for tumors less than or equal to 10 mm and greater than 20 mm; these results suggest that a solid component size of 6 mm could be clinically applied to distinguish pathologic tumor invasion.
10.1001/jamanetworkopen.2023.37889
Quantifying invasiveness of clinical stage IA lung adenocarcinoma with computed tomography texture features.
Qiu Zhen-Bin,Zhang Chao,Chu Xiang-Peng,Cai Fei-Yue,Yang Xue-Ning,Wu Yi-Long,Zhong Wen-Zhao
The Journal of thoracic and cardiovascular surgery
OBJECTIVES:The study objectives were to establish and validate a nomogram for pathological invasiveness prediction in clinical stage IA lung adenocarcinoma and to help identify those potentially unsuitable for sublobar resection-based computed tomography texture features. METHOD:Patients with clinical stage IA lung adenocarcinoma who underwent surgery at Guangdong Provincial People's Hospital between January 2015 and October 2018 were retrospectively reviewed. All surgically resected nodules were pathologically classified into less-invasive and invasive cohorts. Each nodule was manually segmented, and its computerized texture features were extracted. Clinicopathological and computed tomographic texture features were compared between 2 cohorts. A nomogram for distinguishing the pathological invasiveness was established and validated. RESULTS:Among 428 enrolled patients, 249 were diagnosed with invasive pathological subtypes. Smoking status (odds ratio, 2.906; 95% confidence interval, 1.285-6.579; P = .011), mean computed tomography attenuation value (odds ratio, 1.005, 95% confidence interval, 1.002-1.007; P < .001), and entropy (odds ratio, 8.536, 95% confidence interval, 3.478-20.951; P < .001) were identified as independent predictors for pathological invasiveness by multivariate logistics regression analysis. The nomogram showed good calibration (P = .182) with an area under the curve of 0.849 when validated with testing set data. Decision curve analysis indicated the potentially clinical usefulness of the model with respect to treat-all or treat-none scenario. Compared with intraoperative frozen-section, the nomogram performed better in pathological invasiveness diagnosis (area under the curve, 0.815 vs 0.670; P = .00095). CONCLUSIONS:We established and validated a nomogram to compute the probability of invasiveness of clinical stage IA lung adenocarcinoma with great calibration, which may contribute to decisions related to resection extent.
10.1016/j.jtcvs.2020.12.092
Volume and Mass Doubling Time of Lung Adenocarcinoma according to WHO Histologic Classification.
Hong Jung Hee,Park Samina,Kim Hyungjin,Goo Jin Mo,Park In Kyu,Kang Chang Hyun,Kim Young Tae,Yoon Soon Ho
Korean journal of radiology
OBJECTIVE:This study aimed to evaluate the tumor doubling time of invasive lung adenocarcinoma according to the International Association of the Study for Lung Cancer (IASLC)/American Thoracic Society (ATS)/European Respiratory Society (ERS) histologic classification. MATERIALS AND METHODS:Among the 2905 patients with surgically resected lung adenocarcinoma, we retrospectively included 172 patients (mean age, 65.6 ± 9.0 years) who had paired thin-section non-contrast chest computed tomography (CT) scans at least 84 days apart with the same CT parameters, along with 10 patients with squamous cell carcinoma (mean age, 70.9 ± 7.4 years) for comparison. Three-dimensional semiautomatic segmentation of nodules was performed to calculate the volume doubling time (VDT), mass doubling time (MDT), and specific growth rate (SGR) of volume and mass. Multivariate linear regression, one-way analysis of variance, and receiver operating characteristic curve analyses were performed. RESULTS:The median VDT and MDT of lung cancers were as follows: acinar, 603.2 and 639.5 days; lepidic, 1140.6 and 970.1 days; solid/micropapillary, 232.7 and 221.8 days; papillary, 599.0 and 624.3 days; invasive mucinous, 440.7 and 438.2 days; and squamous cell carcinoma, 149.1 and 146.1 days, respectively. The adjusted SGR of volume and mass of the solid-/micropapillary-predominant subtypes were significantly shorter than those of the acinar-, lepidic-, and papillary-predominant subtypes. The histologic subtype was independently associated with tumor doubling time. A VDT of 465.2 days and an MDT of 437.5 days yielded areas under the curve of 0.791 and 0.795, respectively, for distinguishing solid-/micropapillary-predominant subtypes from other subtypes of lung adenocarcinoma. CONCLUSION:The tumor doubling time of invasive lung adenocarcinoma differed according to the IASCL/ATS/ERS histologic classification.
10.3348/kjr.2020.0592
What CT characteristics of lepidic predominant pattern lung adenocarcinomas correlate with invasiveness on pathology?
Aherne Emily A,Plodkowski Andrew J,Montecalvo Joseph,Hayan Sumar,Zheng Junting,Capanu Marinela,Adusumilli Prasad S,Travis William D,Ginsberg Michelle S
Lung cancer (Amsterdam, Netherlands)
OBJECTIVES:The International Association for the Study of Lung Cancer, American Thoracic Society and European Respiratory Society lung adenocarcinoma classification in 2011 defined three lepidic predominant patterns including adenocarcinoma in situ, minimally invasive adenocarcinoma and lepidic predominant adenocarcinoma. We sought to correlate the radiology and pathology findings and identify any computed tomography (CT) features which can be associated with invasive growth. MATERIALS AND METHODS:An institutional review board approved, retrospective study was conducted evaluating 63 patients with resected, pathologically confirmed, adenocarcinomas with predominant lepidic patterns. Preoperative CT images of the nodules were assessed using quantitative and qualitative radiographic descriptors while blinded to pathologic sub-classification and size. Maximum diameter was measured after evaluation of the axial, sagittal and coronal planes. Radiologic - pathologic associations were examined using Fisher's exact test, the Kruskal-Wallis test and the Spearman correlation coefficient (ρ). RESULTS AND CONCLUSION:Increasing maximum diameter of the whole lesion (ground glass and solid component) on CT was significantly associated with invasiveness (p = .003), as was the maximum pathologic specimen diameter (p = .008). Larger diameter of the solid component on CT was also found in lepidic predominant adenocarcinoma compared to minimally invasive adenocarcinoma (median 10.5 vs 2 mm, p = .005). More invasive tumors had higher visual estimated percentage solid component compared to whole lesion measurement on CT (p = .014). CT and pathologic measurements were positively correlated, although only moderately (ρ = .66) for the maximum whole lesion size and fair (ρ = .49) for solid/invasive component maximum measurements. Larger whole lesion size and solid component size of lepidic predominant pattern adenocarcinomas are associated with lesion invasiveness, although radiologic and pathologic lesion measurements are only fair-moderately positively correlated.
10.1016/j.lungcan.2018.01.013
Clinicopathological, Radiological, and Molecular Features of Primary Lung Adenocarcinoma with Morule-Like Components.
Disease markers
BACKGROUND:Morule-like component (MLC) was a rare structure in primary lung adenocarcinoma. We aimed to reveal the clinicopathological, radiological, immunohistochemical, and molecular features of lung adenocarcinoma with MLCs. METHODS:Twenty lung adenocarcinomas with MLCs were collected, and computed tomographic and histological documents were reviewed. Immunohistochemistry, targeted next-generation sequencing, and Sanger sequencing for gene were performed. RESULTS:There were 9 lepidic adenocarcinomas, 8 acinar adenocarcinomas, 2 papillary adenocarcinomas, and 1 minimally invasive adenocarcinoma. Most patients (16/17) were shown a pure solid nodule, and 1 patient was shown a partly solid nodule on chest computed tomography (CT). Nine cases were accompanied with micropapillary components, and 3 were with cribriform components in which 2 suffered a worse prognosis. No significant association was found between the MCLs and the overall survival of lung adenocarcinoma ( = 0.109). The MLCs were often arranged in whorled or streaming patterns. The cells in MLCs showed syncytial and mild appearance. The MLCs were positive for E-cadherin, CK7, TTF-1, napsin-A, vimentin, and -catenin (membrane), and negative for CK5/6, p40, p63, Synaptophysin, chromogranin A, and Cdx-2. mutation, fusion, 2 amplification, and mutation were detected in 16 cases, 2 cases, 1 case, and 1 case, respectively. mutation was more frequent in adenocarcinomas with MLCs than those without MLCs ( = 0.040). gene mutation was not detected in any patients. CONCLUSIONS:MLC is often observed in the background of acinar, lepidic, and papillary adenocarcinomas. Lung adenocarcinomas with MLCs tend to appear as a solid mass on CT and harbor gene mutations. The micropapillary components and cribriform components may cause poor prognosis of lung adenocarcinomas with MLCs. Vimentin is always positive in MLCs, and it is a useful marker for the identification of MLCs.
10.1155/2021/9186056
Preoperative prediction of the degree of differentiation of lung adenocarcinoma presenting as sub-solid or solid nodules with a radiomics nomogram.
Clinical radiology
AIM:To develop and validate a radiomics nomogram for prediction of degree of differentiation in lung adenocarcinoma presenting as sub-solid or solid nodules. MATERIALS AND METHODS:A total of 438 patients with histopathologically confirmed adenocarcinoma (248 non-poorly differentiated and 190 poorly differentiated) were divided into training cohort (n=235) and internal validation cohort (n=203) according to surgery sequence. Sixty patients form public TCIA dataset were selected for external validation. One thousand, two hundred and eighteen radiomics features were extracted from each volumetric region of interest and a least absolute shrinkage and selection operator logistic regression was applied to select meaningful radiomic features for building a radiomics score (Rad-score) model. A nomogram model incorporating the Rad-score and type was established after multivariable logistic regression. The discrimination efficiency, calibration efficacy, and clinical utility value of the nomogram were evaluated. RESULTS:The Rad-score model could predict the differentiation degree of lung adenocarcinoma with an area under the curve (AUC) of 0.83 (95% confidence interval [CI]: 0.78-0.89) in the internal validation cohort. The AUC of the nomogram and radiographic model was 0.86 (95% CI: 0.80-0.91), 0.78 (95% CI: 0.72-0.84) in the internal validation cohort respectively. The AUC of the nomogram in the external validation cohort was 0.73 (95% CI: 0.58-0.88). Delong's test showed that the nomogram performed better than radiographic features alone (p=0.001). CONCLUSIONS:The proposed radiomics nomogram has the potential to predict the differentiation degree of lung adenocarcinoma preoperatively.
10.1016/j.crad.2022.05.015
[Value of PET/CT Combined with CT Three-dimensional Reconstruction
in Distinguishing Different Pathological Subtypes of Early Lung Adenocarcinoma].
You Jie,Zhang Guozhong,Gao Xianglong,Chen Yong,Shu Yusheng
Zhongguo fei ai za zhi = Chinese journal of lung cancer
BACKGROUND:The good prognosis of lepidic predominant invasive adenocarcinoma (LPA) and adenocarcinoma in situ (AIS)/microinvasive adenocarcinoma (MIA) in the pathological subtypes of early lung adenocarcinoma is similar, and the means to distinguish LPA from non-LPA is urgently needed in clinical practice. This study intends to analyze the correlation between positron emission computed tomography (PET)/computed tomography (CT) maximal standard uptake value (SUVmax) with CT three-dimensional reconstruction parameters and the pathological subtypes of early lung adenocarcinoma with part-solid nodules (PSNs) in preoperative imaging. METHODS:The data of early lung adenocarcinoma patients who underwent anatomical pneumonectomy at the Department of Thoracic Surgery of Northern Jiangsu People's Hospital from January 2016 to January 2019 retrospectively analyzed and subsolid nodules on imaging were showed. All patients with enhanced chest CT and PET/CT data can be obtained completely, using Mimics software to perform three-dimensional reconstruction to obtain tumor volume, 3-dimensional mean-CT value (3Dm-CT) of tumor and SUVmax, using SPSS 25.0 for statistical analysis and GraphPad Prism 8.3.0 for drawing receiver operating curve (ROC). P<0.05 indicates that the difference is statistically significant. RESULTS:67 patients were included in this study. All patients were divided into two groups according to different pathological subtypes. AIS, MIA and LPA in invasive adenocarcinoma (IAC) were in the low-risk group, 28 cases (41.8%), and the remaining non-LPA were in high-risk group, 39 cases (58.2%). SUVmax (t=3.153, P=0.002), tumor volume (t=3.331, P=0.001), solid/ground glass component volume (t=2.74, P=0.006)/(t=3.127, P=0.002) and 3Dm-CT of solid/ground glass component (t=3.655, P<0.001)/(t=7.082, P<0.001) between the two groups were all statistically significant. ROC curve prompts: SUVmax [area under curve (AUC)=0.727], tumor volume (AUC=0.740), ground glass component volume (AUC=0.725), 3Dm-CT of solid components (AUC=0.763), 3Dm-CT of ground glass components (AUC=0.756) have the best predictive performance. The above-mentioned covariates with AUC>0.7 were included in the multivariate ROC curve analysis, and the joint predictor (AUC=0.835) was obtained with medium or above predictive value. CONCLUSIONS:PET/CT SUVmax and CT three-dimensional reconstruction parameters have a significant correlation with the different pathological subtypes of early lung adenocarcinoma with PSNs in imaging. The combination of SUVmax, tumor volume, ground glass component volume and 3Dm-CT of solid/ground glass component CT value has certain value in identifying the pathological subtype of early stage lung adenocarcinoma with PSNs nodules in imaging.
10.3779/j.issn.1009-3419.2021.101.19
The value of CT-based radiomics in early assessment of chemotherapeutic effect in patients with advanced lung adenocarcinoma: a preliminary study.
Acta radiologica (Stockholm, Sweden : 1987)
BACKGROUND:Computed tomography (CT) is the preferred method for evaluating the therapeutic effect of lung cancer. Radiomics parameters can provide a lot of supplementary information for clinical diagnosis and treatment. PURPOSE:To investigate the value of radiomics features of CT imaging to predict and evaluate the early efficacy of chemotherapy in patients with advanced lung adenocarcinoma. MATERIAL AND METHODS:A total of 101 patients with advanced lung adenocarcinoma were enrolled. Patients were classified into a response group and non-response group according to RECIST 1.1 standard. All patients underwent chest CT examination before and after two cycles of chemotherapy. A total of 293 radiomics features were calculated. The features between response group and non-response group were compared before and after chemotherapy. The diagnostic efficacy was evaluated using the receiver operating characteristic curve. RESULTS:The six pre-chemotherapy radiomics features were selected, with area under the curve (AUC), sensitivity, and specificity at 0.720, 68.3%, and 69.0% in the training group and 0.573, 50.0%, and 76.9% in the test group, respectively. The eleven post-chemotherapy radiomics features were selected, with AUC, sensitivity, specificity at 0.789, 75.6%, and 75.9% in the training group and 0.718, 61.1%, and 76.9% in the test group, respectively. The prognostic value of △f8, △f16, %f8, and %f16 were higher than the other features with AUCs of 0.787, 0.837, 0.763, and 0.877, respectively. CONCLUSION:Radiomics is expected to provide more valuable information for evaluating the chemotherapy efficacy of lung adenocarcinoma.
10.1177/02841851221078290
Development of a novel combined nomogram integrating deep-learning-assisted CT texture and clinical-radiological features to predict the invasiveness of clinical stage IA part-solid lung adenocarcinoma: a multicentre study.
Clinical radiology
AIM:To develop a novel combined nomogram based on deep-learning-assisted computed tomography (CT) texture (DL-TA) and clinical-radiological features for the preoperative prediction of invasiveness in patients with clinical stage IA lung adenocarcinoma manifesting as part-solid nodules (PSNs). MATERIALS AND METHODS:This study was conducted from January 2015 to October 2021 at three centres: 355 patients with 355 PSN lung adenocarcinomas who underwent surgical resection were included and classified into the training (n=222) and validation (n=133) cohorts. PSN segmentation on CT images was performed automatically with a commercial deep-learning algorithm, and CT texture features were extracted. The least absolute shrinkage and selection operator was used for feature selection and transformed into a DL-TA score. The combined nomogram that incorporated the DL-TA score and identified clinical-radiological features was developed for the prediction of pathological invasiveness of the PSNs and validated in terms of discrimination and calibration. RESULTS:The present study generated a combined nomogram for predicting the invasiveness of PSNs that included age, consolidation-to-tumour ratio, smoking status, and DL-TA score, with a C-index of 0.851 (95% confidence interval: 0.826-0.877) for the training cohort and 0.854 (95% confidence interval: 0.817-0.891) for the validation cohort, indicating good discrimination. Furthermore, the model had a Brier score of 0.153 for the training cohort and 0.135 for the validation cohort, indicating good calibration. CONCLUSION:The developed combined nomogram consisting of the DL-TA score and clinical-radiological features and has the potential to predict the individual risk for the invasiveness of stage IA PSN lung adenocarcinomas.
10.1016/j.crad.2023.07.002
Value of preoperative F-FDG PET/CT and HRCT in predicting the differentiation degree of lung adenocarcinoma dominated by solid density.
PeerJ
Purpose:To evaluate the value of positron emission tomography/computed tomography (PET/CT) combined with high-resolution CT (HRCT) in determining the degree of differentiation of lung adenocarcinoma. Methods:From January 2018 to January 2022, 88 patients with solid density nodules that are lung adenocarcinoma were surgically treated. All patients were examined using HRCT and PET/CT before surgery. During HRCT, two independent observers assessed the presence of lobulation, spiculation, pleural indentation, vascular convergence, and air bronchial signs (bronchial distortion and bronchial disruption). The diameter and CT value of the nodules were measured simultaneously. During PET/CT, the maximum standard uptake value (SUVmax), mean standard uptake value (SUVmean), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) of the nodules were measured. The risk factors of pathological classification were predicted by logistic regression analysis. Results:All 88 patients (mean age 60 ± 8 years; 44 males and 44 females) were evaluated. The average nodule size was 2.6 ± 1.1 cm. The univariate analysis showed that carcinoembryonic antigen (CEA), pleural indentation, vascular convergence, bronchial distortion, and higher SUVmax were more common in poor differentiated lung adenocarcinoma, and in the multivariate analysis, pleural indentation, vascular convergence, and SUVmax were predictive factors. The combined diagnosis using these three factors showed that the area under the curve (AUC) was 0.735. Conclusion:SUVmax >6.99 combined with HRCT (pleural indentation sign and vascular convergence sign) is helpful to predict the differentiation degree of lung adenocarcinoma dominated by solid density.
10.7717/peerj.15242
Prediction of EGFR mutations by conventional CT-features in advanced pulmonary adenocarcinoma.
Chen Yanqing,Yang Yang,Ma Longbai,Zhu Huiyuan,Feng Tienan,Jiang Sen,Wei Youyong,Wang Tingting,Sun Xiwen
European journal of radiology
OBJECTIVE:This study assessed the ability of conventional computed tomography (CT) features (including primary tumors, metastatic lesions, lymph nodes, and emphysema) to predict epidermal growth factor receptor (EGFR) mutations in advanced pulmonary adenocarcinoma. METHODS:Patients who were diagnosed with advanced pulmonary adenocarcinoma between January 2017 and August 2017 and had undergone a chest CT and EGFR mutation testing were enrolled in this retrospective study. Qualitative and quantitative CT-features and clinical characteristics evaluated in this study included: primary tumor location, size, and morphology (including degree of lobulation, density, calcification, cavitation, vacuole sign, and air bronchogram), size and distribution of lung and pleural metastatic nodules, size and status of hilar and mediastinal lymph nodes, emphysema, organs with distant metastasis, and patient age, sex, and smoking history. RESULTS:Of 201 patients, 107 (53.23%) were EGFR-mutation positive. The multivariate logistic regression indicated that EGFR mutations were significantly associated with smaller lymph nodes, a lower percentage of deep lobulation of the primary tumor and partial fusion of lymph nodes, and absence of emphysema. The area under the curve of logistic regression model for predicting EGFR mutations was 0.898. CONCLUSIONS:Conventional CT-features, including emphysema, degree of primary tumor lobulation, and lymph node size and status, help to predict the presence or absence of EGFR mutations in advanced pulmonary adenocarcinoma. Additionally, these same CT-features demonstrated that the CT manifestations of the EGFR mutant group were of relatively lower malignancy and less invasive as compared to the wild-type EGFR group.
10.1016/j.ejrad.2019.01.005
Establishment and visualization of a model based on high-resolution CT qualitative and quantitative features for prediction of micropapillary or solid components in invasive lung adenocarcinoma.
Journal of cancer research and clinical oncology
OBJECTIVE:To predict the existence of micropapillary or solid components in invasive adenocarcinoma, a model was constructed using qualitative and quantitative features in high-resolution computed tomography (HRCT). METHODS:Through pathological examinations, 176 lesions were divided into two groups depending on the presence or absence of micropapillary and/or solid components (MP/S): MP/S- group (n = 128) and MP/S + group (n = 48). Multivariate logistic regression analyses were used to identify independent predictors of the MP/S. Artificial intelligence (AI)-assisted diagnostic software was used to automatically identify the lesions and extract corresponding quantitative parameters on CT images. The qualitative, quantitative, and combined models were constructed according to the results of multivariate logistic regression analysis. The receiver operating characteristic (ROC) analysis was conducted to evaluate the discrimination capacity of the models with the area under the curve (AUC), sensitivity, and specificity calculated. The calibration and clinical utility of the three models were determined using the calibration curve and decision curve analysis (DCA), respectively. The combined model was visualized in a nomogram. RESULTS:The multivariate logistic regression analysis using both qualitative and quantitative features indicated that tumor shape (P = 0.029 OR = 4.89; 95% CI 1.175-20.379), pleural indentation (P = 0.039 OR = 1.91; 95% CI 0.791-4.631), and consolidation tumor ratios (CTR) (P < 0.001; OR = 1.05; 95% CI 1.036-1.070) were independent predictors for MP/S + . The areas under the curve (AUC) of the qualitative, quantitative, and combined models in predicting MP/S + were 0.844 (95% CI 0.778-0.909), 0.863 (95% CI 0.803-0.923), and 0.880 (95% CI 0.824-0.937). The combined model of AUC was the most superior and statistically better than qualitative model. CONCLUSION:The combined model could assist doctors to evaluate patient's prognoses and devise personalized diagnostic and treatment protocols for patients.
10.1007/s00432-023-04854-4
FDG PET/CT in a Case of Lung Adenocarcinoma With Diffuse Cavitary Intrapulmonary Metastases.
Clinical nuclear medicine
ABSTRACT:We describe FDG PET/CT findings in a case of lung adenocarcinoma with diffuse cavitary intrapulmonary metastases at initial diagnosis. High-resolution CT of the chest showed the primary solid tumor in the right upper lobe and numerous cavitating metastases ranging from a few millimeters to 1 cm in the bilateral lungs. FDG PET/CT showed intense activity of the primary tumor, diffuse activity of the lung metastases, and hypermetabolic metastases in the mediastinal lymph nodes and bones. Familiarity with this atypical intrapulmonary metastatic pattern of lung cancer may be helpful for the diagnosis and differential diagnosis.
10.1097/RLU.0000000000004534
Comprehensive Computed Tomography Radiomics Analysis of Lung Adenocarcinoma for Prognostication.
Lee Geewon,Park Hyunjin,Sohn Insuk,Lee Seung-Hak,Song So Hee,Kim Hyeseung,Lee Kyung Soo,Shim Young Mog,Lee Ho Yun
The oncologist
BACKGROUND:In this era of personalized medicine, there is an expanded demand for advanced imaging biomarkers that reflect the biology of the whole tumor. Therefore, we investigated a large number of computed tomography-derived radiomics features along with demographics and pathology-related variables in patients with lung adenocarcinoma, correlating them with overall survival. MATERIALS AND METHODS:Three hundred thirty-nine patients who underwent operation for lung adenocarcinoma were included. Analysis was performed using 161 radiomics features, demographic, and pathologic variables and correlated each with patient survival. Prognostic performance for survival was compared among three models: (a) using only clinicopathological data; (b) using only selected radiomics features; and (c) using both clinicopathological data and selected radiomics features. RESULTS:At multivariate analysis, age, pN, tumor size, type of operation, histologic grade, maximum value of the outer 1/3 of the tumor, and size zone variance were statistically significant variables. In particular, maximum value of outer 1/3 of the tumor reflected tumor microenvironment, and size zone variance represented intratumor heterogeneity. Integration of 31 selected radiomics features with clinicopathological variables led to better discrimination performance. CONCLUSION:Radiomics approach in lung adenocarcinoma enables utilization of the full potential of medical imaging and has potential to improve prognosis assessment in clinical oncology. IMPLICATIONS FOR PRACTICE:Two radiomics features were prognostic for lung cancer survival at multivariate analysis: (a) maximum value of the outer one third of the tumor reflects the tumor microenvironment and (b) size zone variance represents the intratumor heterogeneity. Therefore, a radiomics approach in lung adenocarcinoma enables utilization of the full potential of medical imaging and could play a larger role in clinical oncology.
10.1634/theoncologist.2017-0538
CT imaging-based histogram features for prediction of EGFR mutation status of bone metastases in patients with primary lung adenocarcinoma.
Shen Tong-Xu,Liu Lin,Li Wen-Hui,Fu Ping,Xu Kai,Jiang Yu-Qing,Pan Feng,Guo Yan,Zhang Meng-Chao
Cancer imaging : the official publication of the International Cancer Imaging Society
OBJECTIVE:To identify imaging markers that reflect the epidermal growth factor receptor (EGFR) mutation status by comparing computed tomography (CT) imaging-based histogram features between bone metastases with and without EGFR mutation in patients with primary lung adenocarcinoma. MATERIALS AND METHODS:This retrospective study included 57 patients, with pathologically confirmed bone metastasis of primary lung adenocarcinoma. EGFR mutation status of bone metastases was confirmed by gene detection. The CT imaging of the metastatic bone lesions which were obtained between June 2014 and December 2017 were collected and analyzed. A total of 42 CT imaging-based histogram features were automatically extracted. Feature selection was conducted using Student's t-test, Mann-Whitney U test, single-factor logistic regression analysis and Spearman correlation analysis. A receiver operating characteristic (ROC) curve was plotted to compare the effectiveness of features in distinguishing between EGFR(+) and EGFR(-) groups. DeLong's test was used to analyze the differences between the area under the curve (AUC) values. RESULTS:Three histogram features, namely range, skewness, and quantile 0.975 were significantly associated with EGFR mutation status. After combining these three features and combining range and skewness, we obtained the same AUC values, sensitivity and specificity. Meanwhile, the highest AUC value was achieved (AUC 0.783), which also had a higher sensitivity (0.708) and specificity (0.788). The differences between AUC values of the three features and their various combinations were statistically insignificant. CONCLUSION:CT imaging-based histogram features of bone metastases with and without EGFR mutation in patients with primary lung adenocarcinoma were identified, and they may contribute to diagnosis and prediction of EGFR mutation status.
10.1186/s40644-019-0221-9
Can peritumoral radiomics increase the efficiency of the prediction for lymph node metastasis in clinical stage T1 lung adenocarcinoma on CT?
Wang Xiang,Zhao Xingyu,Li Qiong,Xia Wei,Peng Zhaohui,Zhang Rui,Li Qingchu,Jian Junming,Wang Wei,Tang Yuguo,Liu Shiyuan,Gao Xin
European radiology
OBJECTIVES:To evaluate the efficiency of radiomics model on CT images of intratumoral and peritumoral lung parenchyma for preoperative prediction of lymph node (LN) metastasis in clinical stage T1 peripheral lung adenocarcinoma patients. METHODS:Three hundred sixty-six peripheral lung adenocarcinoma patients with clinical stage T1 were evaluated using five CT scanners. For each patient, two volumes of interest (VOIs) on CT were defined as the gross tumor volume (GTV) and the peritumoral volume (PTV, 1.5 cm around the tumor). One thousand nine hundred forty-six radiomic features were obtained from each VOI, and then refined for reproducibility and redundancy. The refined features were investigated for usefulness in building radiomic signatures by mRMR feature ranking method and LASSO classifier. Multivariable logistic regression analysis was used to develop a radiomic nomogram incorporating the radiomic signature and clinical parameters. The prediction performance was evaluated on the validation cohort. RESULTS:The radiomic signatures using the features of GTV and PTV showed a good ability in predicting LN metastasis with an AUC of 0.829 (95% CI, 0.745-0.913) and 0.825 (95% CI, 0.733-0.918), respectively. By incorporating the features of GTV and PTV, the AUC of radiomic signature increased to 0.843 (95% CI, 0.770-0.916). The AUC of radiomic nomogram was 0.869 (95% CI, 0.800-0.938). CONCLUSIONS:Radiomic signatures of GTV and PTV both had a good prediction ability in the prediction of LN metastasis, and there is no significant difference of AUC between the two groups. The proposed nomogram can be conveniently used to facilitate the preoperative prediction of LN metastasis in T1 peripheral lung adenocarcinomas. KEY POINTS:• Radiomics from peritumoral lung parenchyma increase the efficiency of the prediction for lymph node metastasis in clinical stage T1 lung adenocarcinoma on CT. • A radiomic nomogram was developed and validated to predict LN metastasis. • Different scan parameters on CT showed that radiomics signature had good predictive performance.
10.1007/s00330-019-06084-0
Optimization of CT windowing for diagnosing invasiveness of adenocarcinoma presenting as sub-solid nodules.
Cui Xiaonan,Fan Shuxuan,Heuvelmans Marjolein A,Han Daiwei,Zhao Yingru,Groen Harry J M,Dorrius Monique D,Oudkerk Matthijs,de Bock Geertruida H,Vliegenthart Rozemarijn,Ye Zhaoxiang
European journal of radiology
PURPOSE:To evaluate the optimal window setting to diagnose the invasiveness of lung adenocarcinoma in sub-solid nodules (SSNs). METHODS:We retrospectively included 437 SSNs and randomly divided them 3:1 into a training group (327) and a testing group (110). The presence of a solid component was regarded as indicator of invasiveness. At fixed window level (WL) of 35 Hounsfield Units (HU), two readers adjusted the window width (WW) in the training group and recorded once a solid component appeared or disappeared on CT images acquired at 120 kVp. The optimal WW cut-off value to differentiate between invasive and pre-invasive lesions, based on the receiver operating characteristic (ROC) curve, was defined as "core" WW. The diagnostic performances of the mediastinal window setting (WW/WL, 350/35 HU) and core window setting were then compared in the testing group. RESULTS:Of the 437 SSNs, 88 were pre-invasive [17 atypical adenomatous hyperplasia (AAH) and 71 adenocarcinoma in situ (AIS)], 349 were invasive [233 minimally invasive adenocarcinoma (MIA), 116 invasive adenocarcinoma (IA)]. In training group, the core WW of 1175 HU was the optimal cut-off to detect solid components of SSNs (AUC:0.79). In testing group, the sensitivity, specificity, positive, negative predictive value, and diagnostic accuracy for SSN invasiveness were 49.4%, 90.5%, 95.7%, 29.7%, and 57.3% for mediastinal window setting, and 87.6%, 76.2%, 91.6%, 76.2%, and 85.5% for core window setting. CONCLUSION:At 120 kVp, core window setting (WW/WL, 1175/35 HU) outperformed the traditional mediastinal window setting to diagnose the invasiveness of SSNs.
10.1016/j.ejrad.2020.108981
CT characteristics and pathological implications of early stage (T1N0M0) lung adenocarcinoma with pure ground-glass opacity.
Jin Xin,Zhao Shao-hong,Gao Jie,Wang Dian-jun,Wu Jian,Wu Chong-chong,Chang Rui-ping,Ju Hai-yue
European radiology
OBJECTIVES:To analyze the CT characteristics and pathological classification of early lung adenocarcinoma (T1N0M0) with pure ground-glass opacity (pGGO). METHODS:Ninety-four lesions with pGGO on CT in 88 patients with T1N0M0 lung adenocarcinoma were selected from January 2010 to December 2012. All lesions were confirmed by pathology. CT appearances were analyzed including lesion location, size, density, uniformity, shape, margin, tumour-lung interface, internal and surrounding malignant signs. Lesion size and density were compared using analysis of variance, lesion size also assessed using ROC curves. Gender of patients, lesion location and CT appearances were compared using χ²-test. RESULTS:There were no significant differences in gender, lesion location and density with histological invasiveness (P > 0.05). The ROC curve showed that the possibility of invasive lesion was 88.73% when diameter of lesion was more than 10.5 mm. There was a significant difference between lesion uniformity and histological invasiveness (P = 0.01). There were significant differences in margin, tumour-lung interface, air bronchogram with histological invasiveness ( P = 0.02,P = 0.00,P = 0.048). The correlation index of lesion size and uniformity was r = 0.45 (P = 0.00). CONCLUSIONS:The lesion size and uniformity, tumour-lung interface and the air bronchogram can help predict invasive extent of early stage lung adenocarcinoma with pGGO. KEY POINTS:• CT characteristics and pathological classification of pGGO lung adenocarcinoma smaller than 3 cm • The optimal cut-off value for discriminating preinvasive from invasive lesions was 10.5 mm • Uniformity was significant difference between histological subtypes and correlated with lesion size • Tumour margin, tumour-lung interface and air bronchogram showed different between histological types • No significant difference in gender, lesion location and density with histological subtypes.
10.1007/s00330-015-3637-z
The "solid" component within subsolid nodules: imaging definition, display, and correlation with invasiveness of lung adenocarcinoma, a comparison of CT histograms and subjective evaluation.
Tu WenTing,Li ZhaoBin,Wang Yun,Li Qiong,Xia Yi,Guan Yu,Xiao Yi,Fan Li,Liu ShiYuan
European radiology
OBJECTIVE:To validate three proposed definitions of the "solid" component of subsolid nodules, as compared to CT histograms and the use of different window settings, for discriminating the invasiveness of adenocarcinomas in a manner that facilitates routine clinical assessment. METHODS:We retrospectively analyzed 328 pathologically confirmed lung adenocarcinomas, manifesting as subsolid nodules. Three-dimensional CT histograms were generated by setting 11 CT attenuation intervals from - 400 to 50 HU, at 50 HU intervals, and the voxel percentage within each CT attenuation interval was generated automatically. Three definitions of the "solid" component were proposed, and 10 medium window settings were set to evaluate the "solid" component. The diagnostic performance of the three definitions for identifying invasive adenocarcinoma was compared with that of CT histogram analysis and subjective evaluation with medium window settings. RESULTS:A parallel diagnosis using five intervals with the largest AUC (AUC ≥ 0.797) demonstrated good differential diagnostic performance, with 78% sensitivity and 73.7% specificity. Definition 2 (visibility in the mediastinum window) yielded higher accuracy (75.6%) than the other two definitions (p < 0.01). A medium window setting of - 50 WL/2 WW gave a larger AUC than the other nine medium window settings as well as definition 2, with 82.5% specificity and 88.5% PPV, which was higher than those of parallel diagnosis with CT histogram and definition 2. CONCLUSION:Using - 50 WL/2 WW is the optimum approach for evaluating the "solid" component and discriminating invasiveness, superior to using 3D CT histograms and definition 2, and convenient in routine clinical assessment. KEY POINTS:• - 50 WL/2 WW gave a larger AUC than definition 2. • The specificity of - 50 WL/2 WW was higher than CT histograms. • - 50 WL/2 WW offers the best evaluation of the solid component.
10.1007/s00330-018-5778-3
Utility of CT radiomics for prediction of PD-L1 expression in advanced lung adenocarcinomas.
Yoon Jiyoung,Suh Young Joo,Han Kyunghwa,Cho Hyoun,Lee Hye-Jeong,Hur Jin,Choi Byoung Wook
Thoracic cancer
BACKGROUND:We aimed to assess if quantitative radiomic features can predict programmed death ligand 1 (PD-L1) expression in advanced stage lung adenocarcinoma. METHODS:This retrospective study included 153 patients who had advanced stage (>IIIA by TNM classification) lung adenocarcinoma with pretreatment thin section computed tomography (CT) images and PD-L1 expression test results in their pathology reports. Clinicopathological data were collected from electronic medical records. Visual analysis and radiomic feature extraction of the tumor from pretreatment CT were performed. We constructed two models for multivariate logistic regression analysis (one based on clinical variables, and the other based on a combination of clinical variables and radiomic features), and compared c-statistics of the receiver operating characteristic curves of each model to identify the model with the higher predictability. RESULTS:Among 153 patients, 53 patients were classified as PD-L1 positive and 100 patients as PD-L1 negative. There was no significant difference in clinical characteristics or imaging findings on visual analysis between the two groups (P > 0.05 for all). Rad-score by radiomic analysis was higher in the PD-L1 positive group than in the PD-L1 negative group with a statistical significance (-0.378 ± 1.537 vs. -1.171 ± 0.822, P = 0.0008). A prediction model that uses clinical variables and CT radiomic features showed higher performance compared to a prediction model that uses clinical variables only (c-statistic = 0.646 vs. 0.550, P = 0.0299). CONCLUSIONS:Quantitative CT radiomic features can predict PD-L1 expression in advanced stage lung adenocarcinoma. A prediction model composed of clinical variables and CT radiomic features may facilitate noninvasive assessment of PD-L1 expression. KEY POINTS:Significant findings of the study Quantitative CT radiomic features can help predict PD-L1 expression, whereas none of the qualitative imaging findings is associated with PD-L1 positivity. What this study adds A prediction model composed of clinical variables and CT radiomic features may facilitate noninvasive assessment of PD-L1 expression.
10.1111/1759-7714.13352
Detection efficacy of analog [F]FDG PET/CT, digital [F]FDG, and [N]NH PET/CT: a prospective, comparative study of patients with lung adenocarcinoma featuring ground glass nodules.
European radiology
OBJECTIVES:This prospective study compared the detection efficacy of analog F-fluoro-2-deoxyglucose (F-FDG) positron emission tomography (PET)/computed tomography (CT) (aF PET/CT), digital [F]FDG PET/CT (dF PET/CT), and digital N-ammonia (N-NH) PET/CT (dN PET/CT) for patients with lung adenocarcinoma featuring ground glass nodules (GGNs). METHODS:Eighty-seven patients with lung adenocarcinoma featuring GGNs who underwent dF and dN PET/CT were enrolled. Based on the GGN component, diameter, and solid-part size, 87 corresponding patients examined using aF PET/CT were included, with age, sex, and lesion characteristics closely matched. Images were visually evaluated, and the tumor to background ratio (TBR) was used for semi-quantitative analysis. RESULTS:Ultimately, 40 and 47 patients with pure GGNs (pGGNs) and mixed GGNs (mGGNs), respectively, were included. dF PET/CT revealed more positive lesions and higher tracer uptake in GGNs than did aF PET/CT (53/87 vs. 26/87, p < 0.05; TBR: 3.08 ± 4.85 vs. 1.42 ± 0.93, p < 0.05), especially in mGGNs (44/47 vs. 26/47, p < 0.05; TBR: 4.48 ± 6.17 vs. 1.78 ± 1.16, p < 0.05). However, dN PET/CT detected more positive lesions than did dF PET/CT (71/87 vs. 53/87, p < 0.05), especially in pGGNs (24/40 vs. 9/40, p < 0.05). CONCLUSIONS:dF PET/CT provides superior detection efficacy over aF PET/CT for patients with lung adenocarcinoma featuring GGNs, particularly mGGNs. dN PET/CT revealed superior detection efficacy over dF PET/CT, particularly in pGGNs. aF, dF, and dN PET/CT are valuable non-invasive examinations for lung cancer featuring GGNs, with dN PET/CT offering the best detection performance. KEY POINTS:• Digital PET/CT provides superior detection efficacy over analog PET/CT in patients with lung adenocarcinoma featuring GGNs. • dN PET/CT can offer more help in the early detection of malignant GGN.
10.1007/s00330-022-09186-4
Association of CT findings with invasive subtypes and the new grading system of lung adenocarcinoma.
Clinical radiology
AIM:To predict the differentiation between invasive growth patterns and new grades of lung adenocarcinoma (LAC) using computed tomography (CT). MATERIALS AND METHODS:The CT features of 180 surgically treated LAC patients were compared retrospectively to pathological invasive subtypes and tumour grades as defined by the new grading system published in 2021 by the World Health Organization. Two radiologists reviewed the images semi-quantitatively and independently. Univariable and multivariable regression models were built from the statistical means of their assessments to predict invasive subtypes and grades. The area under the curve (AUC) calculation was used to select the best models. The Youden index was applied to determine the cut-off values for radiological parameters. RESULTS:The acinar/papillary patterns were associated with ill-defined margins, lower consolidation/tumour ratio and air bronchogram. The solid growth pattern was associated with a well-defined margin and hypodensity, and the micropapillary (MP) subtype with spiculation. From Grades 1 to 3, the amount of air bronchogram decreased and the consolidation/tumour ratio increased. In the sub-analyses, the best model for differentiating Grade 2 from Grade 1 had the following CT features: solid/subsolid type, consolidation/tumour ratio, well-defined margin, and air bronchogram (AUC = 0.783) and Grade 3 from Grade 2: size of the consolidation part/whole tumour ratio, size of the consolidation part, and well-defined margin (AUC = 0.759). The interobserver agreements between the two radiologists varied between 0.67 and 0.98. CONCLUSIONS:Air bronchogram, consolidation/tumour ratio, and well-defined margin are among the best imaging findings to discriminate between both invasive subtypes and the new grades in LAC.
10.1016/j.crad.2022.11.011
Prediction of high-grade patterns of stage IA lung invasive adenocarcinoma based on high-resolution CT features: a bicentric study.
European radiology
OBJECTIVES:This study aims to predict the high-grade pattern (HGP) of stage IA lung invasive adenocarcinoma (IAC) based on the high-resolution CT (HRCT) features. METHODS:The clinical, pathological, and HRCT imaging data of 457 patients (from bicentric) with pathologically confirmed stage IA IAC (459 lesions in total) were retrospectively analyzed. The 459 lesions were classified into high-grade pattern (HGP) (n = 101) and non-high-grade pattern (n-HGP) (n = 358) groups depending on the presence of HGP (micropapillary and solid) in pathological results. The clinical and pathological data contained age, gender, smoking history, tumor stage, pathological type, and presence or absence of tumor spread through air spaces (STAS). CT features consisted of lesion location, size, density, shape, spiculation, lobulation, vacuole, air bronchogram, and pleural indentation. The independent predictors for HGP were screened by univariable and multivariable logistic regression analyses. The clinical, CT, and clinical-CT models were constructed according to the multivariable analysis results. RESULTS:The multivariate analysis suggested the independent predictors of HGP, encompassing tumor size (p = 0.001; OR = 1.090, 95% CI 1.035-1.148), density (p < 0.001; OR = 9.454, 95% CI 4.911-18.199), and lobulation (p = 0.002; OR = 2.722, 95% CI 1.438-5.154). The AUC values of clinical, CT, and clinical-CT models for predicting HGP were 0.641 (95% CI 0.583-0.699) (sensitivity = 69.3%, specificity = 79.2%), 0.851 (95% CI 0.806-0.896) (sensitivity = 79.2%, specificity = 79.6%), and 0.852 (95% CI 0.808-0.896) (sensitivity = 74.3%, specificity = 85.8%). CONCLUSION:The logistic regression model based on HRCT features has a good diagnostic performance for the high-grade pattern of stage IA IAC. KEY POINTS:• The AUC values of clinical, CT, and clinical-CT models for predicting high-grade patterns were 0.641 (95% CI 0.583-0.699), 0.851 (95% CI 0.806-0.896), and 0.852 (95% CI 0.808-0.896). • Tumor size, density, and lobulation were independent predictive markers for high-grade patterns. • The logistic regression model based on HRCT features has a good diagnostic performance for the high-grade patterns of invasive adenocarcinoma.
10.1007/s00330-022-09379-x
Histogram-based models on non-thin section chest CT predict invasiveness of primary lung adenocarcinoma subsolid nodules.
Oikonomou Anastasia,Salazar Pascal,Zhang Yuchen,Hwang David M,Petersen Alexander,Dmytriw Adam A,Paul Narinder S,Nguyen Elsie T
Scientific reports
109 pathologically proven subsolid nodules (SSN) were segmented by 2 readers on non-thin section chest CT with a lung nodule analysis software followed by extraction of CT attenuation histogram and geometric features. Functional data analysis of histograms provided data driven features (FPC1,2,3) used in further model building. Nodules were classified as pre-invasive (P1, atypical adenomatous hyperplasia and adenocarcinoma in situ), minimally invasive (P2) and invasive adenocarcinomas (P3). P1 and P2 were grouped together (T1) versus P3 (T2). Various combinations of features were compared in predictive models for binary nodule classification (T1/T2), using multiple logistic regression and non-linear classifiers. Area under ROC curve (AUC) was used as diagnostic performance criteria. Inter-reader variability was assessed using Cohen's Kappa and intra-class coefficient (ICC). Three models predicting invasiveness of SSN were selected based on AUC. First model included 87.5 percentile of CT lesion attenuation (Q.875), interquartile range (IQR), volume and maximum/minimum diameter ratio (AUC:0.89, 95%CI:[0.75 1]). Second model included FPC1, volume and diameter ratio (AUC:0.91, 95%CI:[0.77 1]). Third model included FPC1, FPC2 and volume (AUC:0.89, 95%CI:[0.73 1]). Inter-reader variability was excellent (Kappa:0.95, ICC:0.98). Parsimonious models using histogram and geometric features differentiated invasive from minimally invasive/pre-invasive SSN with good predictive performance in non-thin section CT.
10.1038/s41598-019-42340-5
A study on the differential of solid lung adenocarcinoma and tuberculous granuloma nodules in CT images by Radiomics machine learning.
Scientific reports
To study the classification efficiency of using texture feature machine learning method in distinguishing solid lung adenocarcinoma (SADC) and tuberculous granulomatous nodules (TGN) that appear as solid nodules (SN) in non-enhanced CT images. 200 patients with SADC and TGN who underwent thoracic non-enhanced CT examination from January 2012 to October 2019 were included in the study, 490 texture eigenvalues of 6 categories were extracted from the lesions in the non-enhanced CT images of these patients for machine learning, the classification prediction model is established by using relatively the best classifier selected according to the fitting degree of learning curve in the process of machine learning, and the effectiveness of the model was tested and verified. The logistic regression model of clinical data (including demographic data and CT parameters and CT signs of solitary nodules) was used for comparison. The prediction model of clinical data was established by logistic regression, and the classifier was established by machine learning of radiologic texture features. The area under the curve was 0.82 and 0.65 for the prediction model based on clinical CT and only CT parameters and CT signs, and 0.870 based on Radiomics characteristics. The machine learning prediction model developed by us can improve the differentiation efficiency of SADC and TGN with SN, and provide appropriate support for treatment decisions.
10.1038/s41598-023-32979-6
Prognostic analysis and risk stratification of lung adenocarcinoma undergoing EGFR-TKI therapy with time-serial CT-based radiomics signature.
European radiology
OBJECTIVES:To evaluate the value of time-serial CT radiomics features in predicting progression-free survival (PFS) for lung adenocarcinoma (LUAD) patients after epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) therapy. MATERIALS AND METHODS:LUAD patients treated with EGFR-TKIs were retrospectively included from three independent institutes and divided into training and validation cohorts. Intratumoral and peritumoral features were extracted from time-serial non-contrast chest CT (including pre-therapy and first follow-up images); moreover, the percentage variation per unit time (day) was introduced to adjust for the different follow-up periods of each patient. Test-retest was performed to exclude irreproducible features, while the Boruta algorithm was used to select critical radiomics features. Radiomics signatures were constructed with random forest survival models in the training cohort and compared against baseline clinical characteristics through Cox regression and nonparametric testing of concordance indices (C-indices). RESULTS:The training cohort included 131 patients (74 women, 56.5%) from one institute and the validation cohort encompassed 41 patients (24 women, 58.5%) from two other institutes. The optimal signature contained 10 features and 7 were unit time feature variations. The comprehensive radiomics model outperformed the pre-therapy clinical characteristics in predicting PFS (training: 0.78, 95% CI: [0.72, 0.84] versus 0.55, 95% CI: [0.49, 0.62], p < 0.001; validation: 0.72, 95% CI: [0.60, 0.84] versus 0.54, 95% CI: [0.42, 0.66], p < 0.001). CONCLUSION:Radiomics signature derived from time-serial CT images demonstrated optimal prognostic performance of disease progression. This dynamic imaging biomarker holds the promise of monitoring treatment response and achieving personalized management. KEY POINTS:• The intrinsic tumor heterogeneity can be highly dynamic under the therapeutic effect of EGFR-TKI treatment, and the inevitable development of drug resistance may disrupt the duration of clinical benefit. Decision-making remained challenging in practice to detect the emergence of acquired resistance during the early response phase. • Time-serial CT-based radiomics signature integrating intra- and peritumoral features offered the potential to predict progression-free survival for LUAD patients treated with EGFR-TKIs. • The dynamic imaging signature allowed for prognostic risk stratification.
10.1007/s00330-022-09123-5
CT imaging features regarding ground-glass nodules and solid lesions reflect prognostication of synchronous multiple lung adenocarcinoma.
Medicine
The prognosis of synchronous multiple lung adenocarcinoma (SMLA) dramatically differs due to its nature of multiple primaries or intrapulmonary metastases. This study aimed to assess computed tomography (CT)-reflected SMLA features regarding ground-glass nodules (GGNs) and solid lesions and their correlation with prognostication. One seventy eight SMLA patients who underwent surgical resection were reviewed. According to preoperative CT features, patients were categorized as: multiple GGN (MG) group: MGs without solid lesions; solid plus GGN (SPG) group: one solid lesion and at least one GGN; multiple solid (MS) group: MS lesions, with or without GGNs. Clinical characteristics, disease-free survival (DFS), and overall survival (OS) were retrieved. Largest tumor size (P < .001) and lymph-node metastasis prevalence (P < .001) were different among three groups, which were highest in the MS group, followed by the SPG group, and lowest in the MG group. Besides, the dominant tumor subtype also varied among the three groups (P < .001), while no difference in other clinical characteristics was discovered. DFS was more deteriorative in the MS group compared to the SPG group (P = .017) and MG group (P < .001), while of no difference between the SPG group and MG group (P = .128). Meanwhile, OS exhibited similar treads among the three groups. Besides, after multivariate Cox analyses adjustment, MS versus MG independently correlated with DFS (P = .030) and OS (P = .027), but SPG versus MG did not. In conclusion, preoperative CT-imaging MS lesions reflect advanced disease features and poor prognosis compared to MG and solid lesion plus GGN in SMLA patients who underwent surgical resection.
10.1097/MD.0000000000031339
Can texture features improve the differentiation of infiltrative lung adenocarcinoma appearing as ground glass nodules in contrast-enhanced CT?
Gao Chen,Xiang Ping,Ye Jianfeng,Pang Peipei,Wang Shiwei,Xu Maosheng
European journal of radiology
OBJECTIVES:To investigate the validity and efficacy of comparing texture features from contrast-enhanced images with non-enhanced images in identifying infiltrative lung adenocarcinoma represented as ground glass nodules (GGN). MATERIALS AND METHODS:A retrospective cohort study was conducted with patients presenting with lung adenocarcinoma and treated at a single centre between January 2015 to December 2017. All patients underwent standard and contrast-enhanced thoracic CT scans with 0.5 mm collimation and 1 mm slice reconstruction thickness before surgery. A total of 34 lung adenocarcinoma patients (representing 34 lesions) were analysed; including 21 instances of invasive adenocarcinoma (IAC) lesions, 4 instances of adenocarcinoma in situ (AIS) lesions, and 9 minimally invasive adenocarcinoma (MIA) lesions. After radiologists manually segmented the lesions, texture features were quantitatively extracted using Artificial Intelligence Kit (AK) software. Then, multivariate logistic regression analysis based on standard and contrast-enhanced CT texture features was employed to analyse the invasiveness of lung adenocarcinoma lesions appearing as GGNs. A receiver operating characteristic (ROC) curve analysis was used to evaluate the performance of those models. RESULTS:A total of 21 quantitative texture features were extracted using the AK software. After dimensionality reduction, 5 and 3 features extracted from thin-section unenhanced and contrast-enhanced CT, respectively, were used to establish the model. The area under the ROC curve (AUC) values for unenhanced CT and enhanced CT features were 0.890 and 0.868, respectively. There was no significant difference (P = 0.190) in the AUC between models based on non-enhanced and contrast-enhanced CT texture features. CONCLUSION:Compared with unenhanced CT, texture features extracted from contrast-enhanced CT provided no benefit in improving the differential diagnosis of infiltrative lung adenocarcinoma from non-infiltrative malignancies appearing as GGNs.
10.1016/j.ejrad.2019.06.010
Micro-computed tomography images of lung adenocarcinoma: detection of lepidic growth patterns.
Nakamura Shota,Mori Kensaku,Iwano Shingo,Kawaguchi Koji,Fukui Takayuki,Hakiri Shuhei,Ozeki Naoki,Oda Masahiro,Yokoi Kohei
Nagoya journal of medical science
Micro-computed tomography (µCT) provides extremely high-resolution images of samples and can be employed as a non-destructive inspection tool. Using µCT, we can obtain images comparable with microscopic images. In this work, we have attempted to take high-resolution images of the human lung using µCT. Compared to clinical high-resolution computed tomography (HRCT) images of living body (in-vivo imaging), we can obtain extremely high-resolution images by µCT of ex-vivo tissues (resected lungs) as three-dimensional data. The purpose of this study was to distinguish between areas of normal lung and lung cancer by µCT images in order to study the feasibility of cancer diagnosis using this novel radiological image modality. Ten resected human lungs containing primary cancer were fixed by Heitzman's methods to obtain high-resolution µCT images. After fixation of the lung, images of the specimens were taken by µCT between January 2016 and November 2017. The imaging conditions were tube voltage: 90 kV and tube current: 110 µA. To compare details of images gained by conventional HRCT and µCT, we measured the thickness of the alveolar walls of the normal lung area and the cancer area of which alveoli might be replaced by tumor cells, and compared their appearance by means of histopathological images. All the nodules were diagnosed as adenocarcinoma. The median whole tumor size was 18 mm (9 mm-24 mm). Each specimen was clearly divided into areas of normal alveolar wall and of thickened alveolar wall on µCT 'visually'. Median thickness of alveolar walls of the normal lung was 0.037 mm (0.034 mm-0.048 mm), and that of the cancer area was 0.084 mm (0.074 mm-0.094 mm); there was a statistically significant difference between both thicknesses by Student's -test ( < 0.01). The area of thickened alveolar walls on µCT corresponded well with the area of microscopically lepidic growth patterns of adenocarcinoma. We found that µCT images could be correctly divided by alveolar walls into normal lung area and lung cancer area. Further detailed investigations with regard to µCT are needed to make comparable histological diagnoses using µCT images with conventional microscopic methods of pathological diagnoses.
10.18999/nagjms.82.1.25
3D multi-scale, multi-task, and multi-label deep learning for prediction of lymph node metastasis in T1 lung adenocarcinoma patients' CT images.
Zhao Xingyu,Wang Xiang,Xia Wei,Zhang Rui,Jian Junming,Zhang Jiayi,Zhu Yechen,Tang Yuguo,Li Zhen,Liu Shiyuan,Gao Xin
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
The diagnosis of preoperative lymph node (LN) metastasis is crucial to evaluate possible therapy options for T1 lung adenocarcinoma patients. Radiologists preoperatively diagnose LN metastasis by evaluating signs related to LN metastasis, like spiculation or lobulation of pulmonary nodules in CT images. However, this type of evaluation is subjective and time-consuming, which may result in poor consistency and low efficiency of diagnoses. In this study, a 3D Multi-scale, Multi-task, and Multi-label classification network (3M-CN) was proposed to predict LN metastasis, as well as evaluate multiple related signs of pulmonary nodules in order to improve the accuracy of LN metastasis prediction. The following key approaches were adapted for this method. First, a multi-scale feature fusion module was proposed to aggregate the features from different levels for which different labels be best modeled at different levels; second, an auxiliary segmentation task was applied to force the model to focus more on the nodule region and less on surrounding unrelated structures; and third, a cross-modal integration module called the refine layer was designed to integrate the related risk factors into the model to further improve its confidence level. The 3M-CN was trained using data from 401 cases and then validated on both internal and external datasets, which consisted of 100 cases and 53 cases, respectively. The proposed 3M-CN model was then compared with existing state-of-the-art methods for prediction of LN metastasis. The proposed model outperformed other methods, achieving the best performance with AUCs of 0.945 and 0.948 in the internal and external test datasets, respectively. The proposed model not only obtain strong generalization, but greatly enhance the interpretability of the deep learning model, increase doctors' confidence in the model results, conform to doctors' diagnostic process, and may also be transferable to the diagnosis of other diseases.
10.1016/j.compmedimag.2021.101987
CT-Guided Core Biopsy for Peripheral Sub-solid Pulmonary Nodules to Predict Predominant Histological and Aggressive Subtypes of Lung Adenocarcinoma.
Tsai Ping-Chung,Yeh Yi-Chen,Hsu Po-Kuei,Chen Chun-Ku,Chou Teh-Ying,Wu Yu-Chung
Annals of surgical oncology
BACKGROUND:Adenocarcinoma is the most common type of lung cancer, and pre-operative biopsy plays an important role to determine its major subtypes. As proposed by the International Association for the Study of Lung Cancer (IASLC), American Thoracic Society (ATS), and European Respiratory Society (ERS) in 2011, the predominant histological subtype of adenocarcinoma is an indicator of outcomes and recurrence rate. However, the value of CT-guided core biopsy in predicting the predominant subtype and detecting the presence of an aggressive subtype of adenocarcinoma, peripheral sub-solid nodule, has less been discussed. METHODS:We retrospectively reviewed 318 consecutive peripheral sub-solid nodules that underwent percutaneous CT-guided lung biopsy and surgical resection, between October 2015 and December 2018 and were diagnosed as adenocarcinoma with histological subtype. The subtyping results from biopsy and surgical pathology were compared to evaluate the concordance rate. RESULTS:The overall concordance rate between biopsy and surgical pathology in determining the predominant histological subtype was 64%. Better concordance was found in small tumors (≤ 2 cm), in predicting either predominant histology (χ = 7.091, P = 0.008) or high grade adenocarcinoma, micropapillary and/or solid subtype, MIP-SOL (χ = 22.301, P < 0.001). The analysis of ground glass opacity (GGO) component (C/T ratio) obtained significantly higher accuracy in the pure GGO group than in the other two groups in predicting predominant histology or high grade adenocarcinoma (χ = 17.560, P < 0.001 and χ = 61.938, P < 0.001, respectively). CONCLUSIONS:CT-guided core biopsies provide additional value in predicting the histological subtype of lung adenocarcinoma after surgical resection, especially in small tumors (≤ 2 cm) or an initially pure GGO group.
10.1245/s10434-020-08511-9
Clinical Value of F-FDG PET/CT in Prediction of Visceral Pleural Invasion of Subsolid Nodule Stage I Lung Adenocarcinoma.
Chen Zhifeng,Jiang Suxiang,Li Zhoulei,Rao Liangjun,Zhang Xiangsong
Academic radiology
RATIONALE AND OBJECTIVES:This study investigated the utility of F-fluorodeoxyglucose positron emission tomography/computed tomography (F-FDG PET/CT) for predicting visceral pleural invasion (VPI) of subsolid nodule (SSN) stage I lung adenocarcinoma. MATERIALS AND METHODS:A retrospective analysis of F-FDG PET/CT data from 65 postsurgical cases with surgical pathology-confirmed SSN lung adenocarcinoma identified significant VPI predictors using multivariate logistic regression. RESULTS:Nodule and solid component sizes, solid component-to-tumor ratios, pleural indentations, distances between nodules and pleura, and maximum standardized uptake values (SUVmax) differed significantly between VPI-positive (n = 30) and VPI-negative (n = 35) cases on univariate analysis. The distance between the nodule and pleura and SUVmax were significant independent VPI predictors on multivariate analysis. Areas under the curve of the distance between the nodule and pleura and SUVmax on receiver operating characteristic curves were 0.76 and 0.79, respectively; both factors were 0.90. The area under the curve of combined predictors was significantly superior to the distance between the nodule and pleura only but not SUVmax alone. The threshold of the distance between the nodule and pleura, to predict VPI was 4.50 mm, with 96.67% sensitivity, and 57.14% specificity. The threshold of SUVmax to predict VPI was 1.05, with 100% sensitivity and 60% specificity. The sensitivity and specificity of model 2 using the independent predictive factors were 96.67%, and 71.43%, respectively. CONCLUSION:Distance between the nodule and pleura and SUVmax are independent predictors of VPI in SSN stage I lung adenocarcinoma. Further, combining these factors improves their predictive ability.
10.1016/j.acra.2020.01.019
CT-based Radiogenomic Analysis of Clinical Stage I Lung Adenocarcinoma with Histopathologic Features and Oncologic Outcomes.
Radiology
Background A preoperative predictive model is needed that can be used to identify patients with lung adenocarcinoma (LUAD) who have a higher risk of recurrence or metastasis. Purpose To investigate associations between CT-based radiomic consensus clustering of stage I LUAD and clinical-pathologic features, genomic data, and patient outcomes. Materials and Methods Patients who underwent complete surgical resection for LUAD from April 2014 to December 2017 with preoperative CT and next-generation sequencing data were retrospectively identified. Comprehensive radiomic analysis was performed on preoperative CT images; tumors were classified as solid, ground glass, or mixed. Patients were clustered into groups based on their radiomics features using consensus clustering, and clusters were compared with tumor genomic alterations, histopathologic features, and recurrence-specific survival (Kruskal-Wallis test for continuous data, χ or Fisher exact test for categorical data, and log-rank test for recurrence-specific survival). Cluster analysis was performed on the entire cohort and on the solid, ground-glass, and mixed lesion subgroups. Results In total, 219 patients were included in the study (median age, 68 years; interquartile range, 63-74 years; 150 [68%] women). Four radiomic clusters were identified. Cluster 1 was associated with lepidic, acinar, and papillary subtypes (76 of 90 [84%]); clusters 2 (13 of 50 [26%]) and 4 (13 of 45 [29%]) were associated with solid and micropapillary subtypes ( < .001). The alterations were highest in cluster 1 (38 of 90 [42%], = .004). Clusters 2, 3, and 4 were associated with lymphovascular invasion (19 of 50 [38%], 14 of 34 [41%], and 28 of 45 [62%], respectively; < .001) and tumor spread through air spaces (32 of 50 [64%], 21 of 34 [62%], and 31 of 45 [69%], respectively; < .001). alterations (14 of 45 [31%]; = .006), phosphoinositide 3-kinase pathway alterations (22 of 45 [49%], < .001), and risk of recurrence (log-rank < .001) were highest in cluster 4. Conclusion CT-based radiomic consensus clustering enabled identification of associations between radiomic features and clinicalpathologic and genomic features and outcomes in patients with clinical stage I lung adenocarcinoma. © RSNA, 2022 See also the editorial by Nishino in this issue.
10.1148/radiol.211582
Adenocarcinoma spectrum lesions of the lung: Detection, pathology and treatment strategies.
Succony L,Rassl D M,Barker A P,McCaughan F M,Rintoul R C
Cancer treatment reviews
Adenocarcinoma has become the most prevalent lung cancer sub-type and its frequency is increasing. The earliest stages in the development of lung adenocarcinomas are visible using modern computed tomography (CT) as ground glass nodules. These pre-invasive nodules can progress over time to become invasive lung adenocarcinomas. Lesions in this developmental pathway are termed 'adenocarcinoma spectrum' lesions. With the introduction of lung cancer screening programs there has been an increase in the detection of these lesions raising questions about natural history, surveillance and treatment. Here we review how the radiological appearance of an adenocarcinoma spectrum lesion relates to its underlying pathology and explore the natural history and factors driving lesion progression. We examine the molecular changes that occur at each stage of adenocarcinoma spectrum lesion development, including the effects of the driver mutations, EGFR and KRAS, that are key to invasive adenocarcinoma pathology. A better understanding of the development of pre-invasive disease will create treatment targets. Our understanding of how tumours interact with the immune system has led to the development of new therapeutic strategies. We review the role of the immune system in the development of adenocarcinoma spectrum lesions. With a clear preinvasive phase there is an opportunity to treat early adenocarcinoma spectrum lesions before an invasive lung cancer develops. We review current management including surveillance, surgical resection and oncological therapy as well as exploring potential future treatment avenues.
10.1016/j.ctrv.2021.102237
Spectrum of Lung Adenocarcinoma.
Hutchinson Barry D,Shroff Girish S,Truong Mylene T,Ko Jane P
Seminars in ultrasound, CT, and MR
Lung cancer remains the most common cause of cancer death in the United States of America and worldwide despite continued advances in lung cancer screening as well as surgical, medical, and radiation oncological treatments. Adenocarcinoma is the most common histological subtype of primary lung cancer and has recently been reorganized into a spectrum ranging from preinvasive lesions to invasive adenocarcinoma. An understanding of the pathology, diagnosis, and management of the spectrum of lung adenocarcinoma is more important than ever, considering the central role of the radiologist. The aim of this review is to describe the subtypes of the lung adenocarcinoma spectrum in terms of histological and imaging features, their pattern of growth on imaging, management, staging, and evolving knowledge of tumor genetics.
10.1053/j.sult.2018.11.009
Predicting micropapillary or solid pattern of lung adenocarcinoma with CT-based radiomics, conventional radiographic and clinical features.
Respiratory research
BACKGROUND:To build prediction models with radiomics features, clinical/conventional radiographic signs and combined scores for the discrimination of micropapillary or solid subtypes (high-risk subtypes) of lung adenocarcinoma. METHODS:This retrospective study enrolled 351 patients with and without high-risk subtypes. Least Absolute Shrinkage and Selection Operator (LASSO) regression with cross-validation was performed to determine the optimal features of radiomics model. Missing clinical data were imputed by Multiple Imputation with Chain Equations (MICE). Clinical model with radiographic signs was built and scores of both models were integrated to establish combined model. Receiver operating characteristics (ROC) curves, area under ROC curves and decision curve analysis (DCA) were plotted to evaluate the model performance and clinical application. RESULTS:Stratified splitting allocated 246 patients into training set. MICE for missing values obtained complete and unbiased data for the following analysis. Ninety radiomic features and four clinical/conventional radiographic signs were used to predict the high-risk subtypes. The radiomic model, clinical model and combined model achieved AUCs of 0.863 (95%CI: 0.817-0.909), 0.771 (95%CI: 0.713-0.713) and 0.872 (95%CI: 0.829-0.916) in the training set, and 0.849 (95%CI: 0.774-0.924), 0.778 (95%CI: 0.687-0.868) and 0.853 (95%CI: 0.782-0.925) in the test set. Decision curve showed that the radiomic and combined models were more clinically useful when the threshold reached 37.5%. CONCLUSIONS:Radiomics features could facilitate the prediction of subtypes of lung adenocarcinoma. A simple combination of radiomics and clinical scores generated a robust model with high performance for the discrimination of micropapillary or solid subtype of lung adenocarcinoma.
10.1186/s12931-023-02592-2
Lung Adenocarcinoma: CT Features Associated with Spread through Air Spaces.
Kim Seon Kyoung,Kim Tae Jung,Chung Myung Jin,Kim Tae Sung,Lee Kyung Soo,Zo Jae Ill,Shim Young Mog
Radiology
Purpose To identify the features at CT that are predictive of spread through air spaces (STAS) in surgically resected lung adenocarcinomas. Materials and Methods For this retrospective study, presence of STAS was evaluated in 948 consecutive patients who underwent surgical resection for lung adenocarcinoma from April 2015 to December 2016. Patients who were positive for STAS and negative for STAS were matched at a ratio of 1:2 by using patient variables (age, sex, and smoking status). CT features (ie, percentage of solid component, maximum diameter of solid component, lesion density, location, margin, shape, pseudocavity, calcification, central low attenuation, ill-defined peripheral opacity, air bronchogram, satellite lesions, and pleural retraction) were analyzed by using multivariable logistic regression and receiver operating characteristic curves. Results The final study population consisted of 276 patients (mean age, 59 years; age range, 32-78 years) including 129 men (mean age, 60 years; age range, 36-78 years) and 147 women (mean age, 59 years; age range, 32-78 years). Ninety-two patients were positive for STAS and 184 patients were negative for STAS. STAS was more common in solid tumors (71 of 92; 77%) than in part-solid (21 of 92; 23%) or ground-glass lesions (0 of 92; 0%) (P < .001). STAS was also associated with central low attenuation, ill-defined opacity, air bronchogram, and percentage of solid component (all P < .001). Percentage of solid component was an independent predictor of STAS (odds ratio, 1.06; 95% confidence interval: 1.03, 1.08) and a cut-off value of 90% showed a discriminatory power with a sensitivity of 89.2% and a specificity of 60.3%. Conclusion Percentage of solid component was independently associated with spread through air spaces in lung adenocarcinomas. © RSNA, 2018 Online supplemental material is available for this article.
10.1148/radiol.2018180431
Peritumoral radiomics features on preoperative thin-slice CT images can predict the spread through air spaces of lung adenocarcinoma.
Scientific reports
The spread through air spaces (STAS) is recognized as a negative prognostic factor in patients with early-stage lung adenocarcinoma. The present study aimed to develop a machine learning model for the prediction of STAS using peritumoral radiomics features extracted from preoperative CT imaging. A total of 339 patients who underwent lobectomy or limited resection for lung adenocarcinoma were included. The patients were randomly divided (3:2) into training and test cohorts. Two prediction models were created using the training cohort: a conventional model based on the tumor consolidation/tumor (C/T) ratio and a machine learning model based on peritumoral radiomics features. The areas under the curve for the two models in the testing cohort were 0.70 and 0.76, respectively (P = 0.045). The cumulative incidence of recurrence (CIR) was significantly higher in the STAS high-risk group when using the radiomics model than that in the low-risk group (44% vs. 4% at 5 years; P = 0.002) in patients who underwent limited resection in the testing cohort. In contrast, the 5-year CIR was not significantly different among patients who underwent lobectomy (17% vs. 11%; P = 0.469). In conclusion, the machine learning model for STAS prediction based on peritumoral radiomics features performed better than the C/T ratio model.
10.1038/s41598-022-14400-w
CT-pathologic correlation in lung adenocarcinoma and squamous cell carcinoma.
Medicine
Distinguishing lung adenocarcinoma from squamous cell carcinoma (SCC) is clinically important. Computed tomography (CT) scan is an economical, effective, noninvasive, commonly available, and quick diagnostic way for lung cancer. In this study, we aim to compare the CT characteristics in adenocarcinoma and SCC.Data from 275 cases (259 adenocarcinoma and 16 SCC) were retrospectively compared. CT characteristics, including lesion size and shape, single/multifocal lesions, location of the tumor, the margin of lobes, whether the lesion had deep lobulated margin, bronchial cut-off sign, signs of dilated bronchial arteries, signs of vascular bundle thickening, signs of short burrs, spinous processes, and pleural indentation, were compared in 148 cases (137 adenocarcinoma and 11 SCC).Patients with adenocarcinoma were more likely to be female (44.2% vs 25.0%, P = .017). Compared with SCC, adenocarcinomas were more likely to have deep lobulated margin (81.0% vs 54.5%, P = .038), less likely to have smooth lobes margin (2.7% vs 83.3%, P < .001), more likely to have vascular bundle thickening (37.2% vs 0, P = .016) and pleural indentation (59.9% vs 18.2%, P = .01), and marginally less likely to have dilated bronchial arteries (17.5% vs 45.5%, P = .064). No significant difference was observed regarding to characteristics, including tumor size, location of the tumor, signs of bronchial cut-off, dilated bronchial arteries, short burrs, or spinous processes.CT scan has the potential to help to distinguish lung adenocarcinoma and SCC in a fast and commonly available way. CT could be a rough but fast way to diagnosis, and may thus shorten the waiting time to treatment and allow more time for clinicians, patients, and their families to prepare for future treatment.
10.1097/MD.0000000000013362
Inaccuracy of lung adenocarcinoma subtyping using preoperative biopsy specimens.
Huang Kuo-Yang,Ko Pin-Zuo,Yao Chih-Wei,Hsu Cheng-Nan,Fang Hsin-Yuan,Tu Chih-Yeh,Chen Hung-Jen
The Journal of thoracic and cardiovascular surgery
BACKGROUND:The prognostic significance of the new classification of lung adenocarcinoma proposed in the 2015 World Health Organization guideline has been validated. This study aimed to compare the preoperative classification of the adenocarcinoma subtype based on computed tomography-guided 18-gauge core needle biopsy (CTNB) or radial probe endobronchial ultrasound (R-EBUS) specimens, with the postoperative classification based on the resected specimens. METHODS:We retrospectively analyzed a consecutive series of 128 patients (60 CTNB and 68 R-EBUS) who underwent surgery for preoperatively confirmed lung adenocarcinoma between 2010 and 2014. Comprehensive histological subtyping was performed according to the 2015 World Health Organization classification system. Diagnostic concordance of subtypes between small biopsy and resection specimens was assessed. RESULTS:Concordant subtyping of adenocarcinomas between the predominant pattern on resections and biopsy sections was observed in 58.6% of cases (75 of 128; 95% confidence interval [CI], 49.9%-66.8%). Preoperative subtyping was accurate in only 30% of samples (3 of 10) with a predominance of solid patterns. None of the 5 micropapillary predominant cases was detected by CTNB or R-EBUS. For the concordance of the presence or absence of micropapillary/solid component, the sensitivity was as low as 16.5% (95% CI, 9.1%-26.5%). The detection rate by CTNB/R-EBUS increased with the increase in the percentage of micropapillary/solid component; however, even in the ≥40% micropapillary/solid group, only 24% of cases were detected by CTNB/R-EBUS. CONCLUSIONS:The accuracy of the estimation of adenocarcinoma histological subtype based on preoperative biopsy sections was unsatisfactory.
10.1016/j.jtcvs.2017.02.059
CT texture analysis-based nomogram for the preoperative prediction of visceral pleural invasion in cT1N0M0 lung adenocarcinoma: an external validation cohort study.
Zuo Z,Li Y,Peng K,Li X,Tan Q,Mo Y,Lan Y,Zeng W,Qi W
Clinical radiology
AIM:To develop a nomogram based on computed tomography (CT) texture analysis for the preoperative prediction of visceral pleural invasion in patients with cT1N0M0 lung adenocarcinoma. MATERIALS AND METHODS:A dataset of chest CT containing lung nodules was collected from two institutions, and all surgically resected nodules were classified pathologically based on the presence of visceral pleural invasion. Each nodule on the CT image was segmented automatically by artificial-intelligence software and its CT texture features were extracted. The dataset was divided into training and external validation cohorts according to the institution, and a nomogram for predicting visceral pleural invasion was developed and validated. RESULTS:Of a total of 313 patients enrolled from two independent institutions, 63 were diagnosed with visceral pleural invasion. Three-dimensional (3D) CT long diameter, skewness, and sphericity, and chronic obstructive pulmonary disease were identified as independent predictors for visceral pleural invasion by multivariable logistic regression. The nomogram based on multivariable logistic regression showed great discriminative ability, as indicated by a C-index of 0.890 (95% confidence interval [CI]: 0.867-0.914) and 0.864 (95% CI: 0.817-0.911) for the training and external validation cohorts, respectively. Additionally, calibration of the nomogram revealed good predictive ability, as indicated by the Brier score (0.108 and 0.100 for the training and external validation cohorts, respectively). CONCLUSIONS:A nomogram was developed that could compute the probability of visceral pleural invasion in patients with cT1N0M0 lung adenocarcinoma with good calibration and discrimination. The nomogram has potential as a reliable tool for clinical evaluation and decision-making.
10.1016/j.crad.2021.11.008
Radiologic-Pathologic Correlation of Solid Portions on Thin-section CT Images in Lung Adenocarcinoma: A Multicenter Study.
Yanagawa Masahiro,Kusumoto Masahiko,Johkoh Takeshi,Noguchi Masayuki,Minami Yuko,Sakai Fumikazu,Asamura Hisao,Tomiyama Noriyuki,
Clinical lung cancer
BACKGROUND:Measuring the size of invasiveness on computed tomography (CT) for the T descriptor size was deemed important in the 8th edition of the TNM lung cancer classification. We aimed to correlate the maximal dimensions of the solid portions using both lung and mediastinal window settings on CT imaging with the pathologic invasiveness (> 0.5 cm) in lung adenocarcinoma patients. MATERIALS AND METHODS:The study population consisted of 378 patients with a histologic diagnosis of adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), invasive adenocarcinoma (IVA)-lepidic, IVA-acinar and/or IVA-papillary, and IVA-micropapillary and/or solid adenocarcinoma. A panel of 15 radiologists was divided into 2 groups (group A, 9 radiologists; and group B, 6 radiologists). The 2 groups independently measured the maximal and perpendicular dimensions of the solid components and entire tumors on the lung and mediastinal window settings. The solid proportion of nodule was calculated by dividing the solid portion size (lung and mediastinal window settings) by the nodule size (lung window setting). The maximal dimensions of the invasive focus were measured on the corresponding pathologic specimens by 2 pathologists. RESULTS:The solid proportion was larger in the following descending order: IVA-micropapillary and/or solid, IVA-acinar and/or papillary, IVA-lepidic, MIA, and AIS. For both groups A and B, a solid portion > 0.8 cm in the lung window setting or > 0.6 cm in the mediastinal window setting on CT was a significant indicator of pathologic invasiveness > 0.5 cm (P < .001; receiver operating characteristic analysis using Youden's index). CONCLUSION:A solid portion > 0.8 cm on the lung window setting or solid portion > 0.6 cm on the mediastinal window setting on CT predicts for histopathologic invasiveness to differentiate IVA from MIA and AIS.
10.1016/j.cllc.2017.12.005
Pathologic stratification of operable lung adenocarcinoma using radiomics features extracted from dual energy CT images.
Bae Jung Min,Jeong Ji Yun,Lee Ho Yun,Sohn Insuk,Kim Hye Seung,Son Ji Ye,Kwon O Jung,Choi Joon Young,Lee Kyung Soo,Shim Young Mog
Oncotarget
PURPOSE:To evaluate the usefulness of surrogate biomarkers as predictors of histopathologic tumor grade and aggressiveness using radiomics data from dual-energy computed tomography (DECT), with the ultimate goal of accomplishing stratification of early-stage lung adenocarcinoma for optimal treatment. RESULTS:Pathologic grade was divided into grades 1, 2, and 3. Multinomial logistic regression analysis revealed i-uniformity and 97.5th percentile CT attenuation value as independent significant factors to stratify grade 2 or 3 from grade 1. The AUC value calculated from leave-one-out cross-validation procedure for discriminating grades 1, 2, and 3 was 0.9307 (95% CI: 0.8514-1), 0.8610 (95% CI: 0.7547-0.9672), and 0.8394 (95% CI: 0.7045-0.9743), respectively. MATERIALS AND METHODS:A total of 80 patients with 91 clinically and radiologically suspected stage I or II lung adenocarcinoma were prospectively enrolled. All patients underwent DECT and F-18-fluorodeoxyglucose (FDG) positron emission tomography (PET)/CT, followed by surgery. Quantitative CT and PET imaging characteristics were evaluated using a radiomics approach. Significant features for a tumor aggressiveness prediction model were extracted and used to calculate diagnostic performance for predicting all pathologic grades. CONCLUSIONS:Quantitative radiomics values from DECT imaging metrics can help predict pathologic aggressiveness of lung adenocarcinoma.
10.18632/oncotarget.13476
Pathologic and gene expression comparison of CT- screen detected and routinely detected stage I/0 lung adenocarcinoma in NCCN risk-matched cohorts.
Cancer treatment and research communications
INTRODUCTION:Although three randomized control trials have proven mortality benefit of CT lung cancer screening (CTLS), <5% of eligible US smokers are screened. Some attribute this to fear of harm conveyed at shared decision visits, including the harm of overdiagnosis/overtreatment of indolent BAC-like adenocarcinoma. METHODS:Since the frequency of indolent cancers has not been compared between CTLS and routinely detected cohorts, we compare pathology and RNA expression of 86 NCCN high-risk CTLS subjects to 83 high-risk (HR-R) and 51 low-risk (LR-R) routinely detected patients. Indolent adenocarcinoma was defined as previously described for low malignant potential (LMP) adenocarcinoma along with AIS/MIA. Exome RNA sequencing was performed on a subset of high-risk (CTLS and HR-R) FFPE tumor samples. RESULTS:Indolent adenocarcinoma (AIS, MIA, and LMP) showed 100% disease-specific survival (DSS) with similar frequency in CTLS (18%) and HR-R (20%) which were comparatively lower than LR-R (33%). Despite this observation, CTLS exhibited intermediate DSS between HR-R and LR-R (5-year DSS: 88% CTLS, 82% HR-R, & 95% LR-R, p = 0.047), possibly reflecting a 0.4 cm smaller median tumor size and lower frequency of tumor necrosis compared to HR-R. WGCNA gene modules derived from TCGA lung adenocarcinoma correlated with aggressive histologic patterns, mitotic activity, and tumor invasive features, but no significant differential expression between CTLS and HR-R was observed. CONCLUSION:CTLS subjects are at no greater risk of overdiagnosis from indolent adenocarcinoma (AIS, MIA, and LMP) than risk-matched patients whose cancers are discovered in routine clinical practice. Improved outcomes likely reflect detection and treatment at smaller size.
10.1016/j.ctarc.2021.100486
Improving the accuracy of prognosis for clinical stage I solid lung adenocarcinoma by radiomics models covering tumor per se and peritumoral changes on CT.
Liu Kunfeng,Li Kunwei,Wu Tingfan,Liang Mingzhu,Zhong Yinghua,Yu Xiangyang,Li Xin,Xie Chuanmiao,Zhang Lanjun,Liu Xueguo
European radiology
OBJECTIVES:To assess methods to improve the accuracy of prognosis for clinical stage I solid lung adenocarcinoma using radiomics based on different volumes of interests (VOIs). METHODS:This retrospective study included patients with postoperative clinical stage I solid lung adenocarcinoma from two hospitals, center 1 and center 2. Three databases were generated: dataset A (training set from center 1), dataset B (internal test set from center 1), and dataset C (external validation test from center 2). Disease-free survival (DFS) data were collected. CT radiomics models were constructed based on four VOIs: gross tumor volume (GTV), 3 mm external to the tumor border (peritumoral volume [PTV]), 6 mm crossing tumor border (PTV), and 6 mm external to the tumor border (PTV). The area under the receiver operating characteristic curve (AUC) was used to compare the model accuracies. RESULTS:A total of 334 patients were included (204 and 130 from centers 1 and 2). The model using PTV (AUC 0.81 [95% confidence interval {CI}: 0.75, 0.94], 0.81 [0.63, 0.90] for datasets B and C) outperformed the other three models, GTV (0.73 [0.58, 0.81], 0.73 [0.58, 0.83]), PTV (0.76 [0.52, 0.87], 0.75 [0.60, 0.83]), and PTV (0.72 [0.60, 0.81], 0.69 [0.59, 0.81]), in datasets B and C, all p < 0.05. CONCLUSIONS:A radiomics model based on a VOI of 6 mm crossing tumor border more accurately predicts prognosis of clinical stage I solid lung adenocarcinoma than that based on VOIs including overall tumor or external rims of 3 mm and 6 mm. KEY POINTS:• Radiomics is a useful approach to improve the accuracy of prognosis for stage I solid adenocarcinoma. • The radiomics model based on VOIs that includes 3 mm within and external to the tumor border (peritumoral volume [PTV]) outperformed models that included either only the tumor itself or those that only included the peritumoral volume.
10.1007/s00330-021-08194-0
Additional value of metabolic parameters to PET/CT-based radiomics nomogram in predicting lymphovascular invasion and outcome in lung adenocarcinoma.
Nie Pei,Yang Guangjie,Wang Ning,Yan Lei,Miao Wenjie,Duan Yanli,Wang Yanli,Gong Aidi,Zhao Yujun,Wu Jie,Zhang Chuantao,Wang Maolong,Cui Jingjing,Yu Mingming,Li Dacheng,Sun Yanqin,Wang Yangyang,Wang Zhenguang
European journal of nuclear medicine and molecular imaging
PURPOSE:Lymphovascular invasion (LVI) impairs surgical outcomes in lung adenocarcinoma (LAC) patients. Preoperative prediction of LVI is challenging by using traditional clinical and imaging parameters. The purpose of this study was to investigate the value of the radiomics nomogram integrating clinical factors, CT features, and maximum standardized uptake value (SUVmax) to predict LVI and outcome in LAC and to evaluate the additional value of the SUVmax to the PET/CT-based radiomics nomogram. METHODS:A total of 272 LAC patients (87 LVI-present LACs and 185 LVI-absent LACs) with PET/CT scans were retrospectively enrolled, and 160 patients with SUVmax ≥ 2.5 of them were used for PET radiomics analysis. Clinical data and CT features were analyzed to select independent LVI predictors. The performance of the independent LVI predictors and SUVmax was evaluated. Two-dimensional (2D) and three-dimensional (3D) CT radiomics signatures (RSs) and PET-RS were constructed with the least absolute shrinkage and selection operator algorithm and radiomics scores (Rad-scores) were calculated. The radiomics nomograms, incorporating Rad-score and independent clinical and CT factors, with SUVmax (RNWS) or without SUVmax (RNWOS) were built. The performance of the models was assessed with respect to calibration, discrimination, and clinical usefulness. All the clinical, PET/CT, pathologic, therapeutic, and radiomics parameters were assessed to identify independent predictors of progression-free survival (PFS). RESULTS:CT morphology was the independent LVI predictor. SUVmax provided better discrimination capability compared with CT morphology in the training set (P < 0.001) and test set (P = 0.042). A total of 1409 CT and PET radiomics features were extracted and reduced to 8, 8, and 10 features to build the 2D CT-RS, 3D CT-RS, and the PET-RS, respectively. There was no significant difference in AUC between the 2D-RS and 3D-RS (P > 0.05), and 2D CT-RS showed a relatively higher AUC than 3D CT-RS. The CT-RS, the CT-RNWOS, and the CT-RNWS showed good discrimination in the training set (AUC [area under the curve], 0.799, 0.796, and 0.851, respectively) and the test set (AUC, 0.818, 0.822, and 0.838, respectively). There was significant difference in AUC between the CT-RNWS and CT-RNWOS (P = 0.044) in the training set. Decision curve analysis (DCA) demonstrated the CT-RNWS outperformed the CT-RS and the CT-RNWOS in terms of clinical usefulness. Furthermore, DCA showed the PETCT-RNWS provided the highest net benefit compared with the PET-RNWS and CT-RNWS. PFS was significantly different between the pathologic and RNWS-predicted LVI-present and LVI-absent patients (P < 0.001). Carbohydrate antigen 125 (CA125), carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), pathologic LVI, histologic subtype, and SUVmax were independent predictors of PFS in the 244 CT-RNWS-predicted cohort; and CA125, NSE, pathologic LVI, and SUVmax were the independent predictors of PFS in the 141 PETCT-RNWS-predicted cohort. CONCLUSIONS:The radiomics nomogram, incorporating Rad-score, clinical and PET/CT parameters, shows favorable predictive efficacy for LVI status in LAC. Pathologic LVI and SUVmax are associated with LAC prognosis.
10.1007/s00259-020-04747-5
Fusion of CT images and clinical variables based on deep learning for predicting invasiveness risk of stage I lung adenocarcinoma.
Medical physics
PURPOSE:To develop a novel multimodal data fusion model by incorporating computed tomography (CT) images and clinical variables based on deep learning for predicting the invasiveness risk of stage I lung adenocarcinoma that manifests as ground-glass nodules (GGNs) and compare the diagnostic performance of it with that of radiologists. METHODS:A total of 1946 patients with solitary and histopathologically confirmed GGNs with maximum diameter less than 3 cm were retrospectively enrolled. The training dataset containing 1704 GGNs was augmented by resampling, scaling, random cropping, and so forth, to generate new training data. A multimodal data fusion model based on residual learning architecture and two multilayer perceptron with attention mechanism combining CT images with patient general data and serum tumor markers was built. The distance-based confidence scores (DCS) were calculated and compared among multimodal data models with different combinations. An observer study was conducted and the prediction performance of the fusion algorithms was compared with that of the two radiologists by an independent testing dataset with 242 GGNs. RESULTS:Among the whole GGNs, 606 GGNs are confirmed as invasive adenocarcinoma (IA) and 1340 are non-IA. The proposed novel multimodal data fusion model combining CT images, patient general data, and serum tumor markers achieved the highest accuracy (88.5%), area under a ROC curve (0.957), F1 (81.5%), F1 (81.9%), and Matthews correlation coefficient (73.2%) for classifying between IA and non-IA GGNs, which was even better than the senior radiologist's performance (accuracy, 86.1%). In addition, the DCSs for multimodal data suggested that CT image had a stronger influence (0.9540) quantitatively than general data (0.6726) or tumor marker (0.6971). CONCLUSION:This study demonstrated that the feasibility of integrating different types of data including CT images and clinical variables, and the multimodal data fusion model yielded higher performance for distinguishing IA from non-IA GGNs.
10.1002/mp.15903
Pathological components and CT imaging analysis of the area adjacent pleura within the pure ground-glass nodules with pleural deformation in invasive lung adenocarcinoma.
BMC cancer
BACKGROUND:Pleural deformation is associated with the invasiveness of lung adenocarcinoma(LAC). Our study focused on the pathological components of the area adjacent pleura in pulmonary pure ground-glass nodules(pGGNs) with pleural deformations(P-pGGNs) confirmed to be invasive LAC without visceral pleural invasion (VPI) pathologically. METHODS:Computed tomography(CT) imaging features of nodules and pathological components of the area adjacent pleura were analyzed and recorded. Statistical analysis was performed for subgroups of P-pGGNs. RESULTS:The 81 enrolled patients with 81 P-pGGNs were finally involved in the analysis. None of solid/micropapillary group and none of VPI was observed, 54 alveoli/lepidics and 27 acinar/papillarys were observed. In P-pGGN with acinar/papillary components of the area adjacent pleura, invasive adenocarcinoma (IAC) was more common compared to minimally invasive adenocarcinoma (MIA, 74.07% vs. 25.93%; p < 0.001). The distance in alveoli/lepidic group was significantly larger (1.50 mm vs. 0.00 mm; p < 0.001) and the depth was significantly smaller (2.00 mm vs. 6.00 mm; p < 0.001) than that in acinar/papillary group. The CT attenuation value, maximum diameter and maximum vertical diameter was valuable to distinguish acinar/papillary group form alveoli/lepidic group(p < 0.05). The type d pleural deformation was the common pleural deformation in IAC(p = 0.028). CONCLUSIONS:The pathological components of the area adjacent pleura in P-pGGN without VPI confirmed to be invasive LAC could included alveoli/lepidics and acinar/papillarys. Some CT indicators that can identify the pathological invasive components of the area adjacent pleura in P-pGGNs.
10.1186/s12885-022-10043-2
Development and validation of a CT-based nomogram to predict spread through air space (STAS) in peripheral stage IA lung adenocarcinoma.
Japanese journal of radiology
INTRODUCTION:To develop and validate a simple-to-use nomogram based on preoperative CT to predict spread through air space (STAS) status of stage IA lung adenocarcinoma (ADC). METHODS:In this retrospective study, 434 patients with pathological proven periphery stage IA lung adenocarcinoma were included, which consisted of 349 patients from center I for training group and 85 patients from Center II for test group. STAS was identified in 53 patients (40 patient in the training group and 13 patients in the test group). On the basis of preoperative CT images, 19 morphological characteristics were analyzed. Univariable analysis was used to explore the association between clinical and CT characteristics and STAS status in the training group (P < 0.002). Independent risk factors for STAS were identified using multivariable logistic regression analysis and then used to build a nomogram for preoperative predicting STAS status. RESULTS:Type of nodules, diameter of solid component, lobulation and percentage of the solid component (PSC) were associated with STAS status of peripheral stage IA lung ADCs statistical significantly. Multivariate logistics regression analysis revealed that PSC and lobulation were independent risk factors for STAS. The nomogram based on these factors achieved good predictive performance for STAS with a C-index of 0.803 in the training group and a well-fitted calibration curve. Using a cut-off value which was obtained from Youden index of the receiver operating characteristic (ROC) curve, a diagnosis accuracy of 70.6% was obtained in the test group with sensitivity, specificity, positive prediction value (PPV) and negative prediction value (NPV) of 92.3%, 66.7%, 33.3% and 98.0%, respectively. CONCLUSION:The nomogram based on preoperative CT images could achieve good predictive performance for STAS status of lung adenocarcinomas. This simple-to-used model can facilitate surgeons for a rational operation pattern choice at bedside.
10.1007/s11604-021-01240-3
The Use of CT Pattern in Differentiating Non-invasive, Minimally Invasive and Invasive Variants of Lung Adenocarcinoma.
Mirka Hynek,Ferda Jiri,Krakorova Gabriela,Vodicka Josef,Mukensnabl Petr,Topolcan Ondrej,Kucera Radek
Anticancer research
BACKGROUND/AIM:This study determined whether computed tomography (CT) is an appropriate means by which to differentiate non-invasive and minimally invasive forms of pulmonary adenocarcinoma from the invasive variant. PATIENTS AND METHODS:A total of 64 patients (38 men and 26 women, aged 42-76, mean age 64), who underwent surgery for pulmonary adenocarcinoma and a chest CT no less than 1 month before surgery, were included in the study. Lesions exhibiting ground glass opacity or ground glass opacity with a solid component of 5 mm or smaller, were defined as minimally invasive or non-invasive adenocarcinomas. CT findings were correlated with histopathological examination. RESULTS:Distinguishing minimally invasive and non-invasive adenocarcinoma from invasive adenocarcinoma using CT was achieved with a sensitivity of 77.7%, a specificity of 97.8%, a positive predictive value of 93.3%, and a negative predictive value of 91.8%. CONCLUSION:CT can be useful in assessing the degree of invasiveness of pulmonary adenocarcinoma and is a potential tool for the individualization of treatment.
10.21873/anticanres.15257
CT and histopathologic characteristics of lung adenocarcinoma with pure ground-glass nodules 10 mm or less in diameter.
Wu Fang,Tian Shu-Ping,Jin Xin,Jing Rui,Yang Yue-Qing,Jin Mei,Zhao Shao-Hong
European radiology
OBJECTIVE:To evaluate CT and histopathologic features of lung adenocarcinoma with pure ground-glass nodule (pGGN) ≤10 mm in diameter. METHODS:CT appearances of 148 patients (150 lesions) who underwent curative resection of lung adenocarcinoma with pGGN ≤10 mm (25 atypical adenomatous hyperplasias, 42 adenocarcinoma in situs, 38 minimally invasive adenocarcinomas, and 45 invasive pulmonary adenocarcinomas) were analyzed for lesion size, density, bubble-like sign, air bronchogram, vessel changes, margin, and tumour-lung interface. CT characteristics were compared among different histopathologic subtypes. Univariate and multivariate analysis were used to assess the relationship between CT characteristics of pGGN and lesion invasiveness, respectively. RESULTS:There were statistically significant differences among histopathologic subtypes in lesion size, vessel changes, and tumour-lung interface (P<0.05). Univariate analysis revealed significant differences of vessel changes, margin and tumour-lung interface between preinvasive and invasive lesions (P<0.05). Logistic regression analysis showed that the vessel changes, unsmooth margin and clear tumour-lung interface were significant predictive factors for lesion invasiveness, with odds ratios (95% CI) of 2.57 (1.17-5.62), 1.83 (1.25-2.68) and 4.25 (1.78-10.14), respectively. CONCLUSION:Invasive lesions are found in 55.3% of subcentimeter pGGNs in our cohort. Vessel changes, unsmooth margin, and clear lung-tumour interface may indicate the invasiveness of lung adenocarcinoma with subcentimeter pGGN. KEY POINTS:• Invasive lesions were found in 55.3% of lung adenocarcinomas with subcentimeter pGGNs • Lesion size, vessel changes, and tumour-lung interface showed different among histopathologic subtypes • Vessel changes, unsmooth margin and clear tumour-lung interface were predictors for lesion invasiveness.
10.1007/s00330-017-4829-5
Incidence of Ct scan-detected pulmonary embolism in patients with oncogene-addicted, advanced lung adenocarcinoma.
Verso Melina,Chiari Rita,Mosca Stefano,Franco Laura,Fischer Matthias,Paglialunga Luca,Bennati Chiara,Scialpi Michele,Agnelli Giancarlo
Thrombosis research
BACKGROUND:Patients with stage IIIB-IV lung adenocarcinoma are at high-risk for pulmonary embolism (PE). In these patients, EGFR and KRAS mutations as well as EML4/ALK rearrangements are recognized as "drivers" and as targets for therapy. Data on the incidence of PE in oncogene-addicted lung cancer patients are limited. AIMS:The aims of this study were to evaluate the incidence of CT scan-detected PE in patients with stage IIIB-IV lung adenocarcinoma and to assess the potential correlation between the presence of these oncogenes and the PE risk. METHODS:Baseline staging or re-staging chest contrast-enhanced CT scans of patients with stage IIIB-IV lung adenocarcinoma were retrospectively reviewed and adjudicated for the presence of PE. Data on the oncogene drivers (EGFR, KRAS or EML4/ALK) of the same patients were collected. RESULTS:A total of 173 patients with lung adenocarcinoma were included in the study. 24.8% of patients were EGFR mutated (31/125), 21.6% were KRAS mutated (27/125) and 13.6% hadan EML4/ALK rearrangement (17/125). 41 patients had a CT-detected PE (23.7%). A PE was observed in 5 patients with EGFR mutation (16.2%), in 5 patients with KRAS mutation (18.5%), in 8 patients with ELM4/ALK mutation (47.1%). The presence of ELM4/ALK rearrangement was associated with an increased risk of PE [HR:2.06 (95%CI 1.08- 3.55)]. Risk of PE was not found to be associated with EGFR or KRAS mutations. CONCLUSIONS:Patients with advanced lung adenocarcinoma were at high risk for PE. The presence of EML4/ALK rearrangement was associated with an increased PE risk.
10.1016/j.thromres.2015.09.006
Meta-analysis of association between CT-based features and tumor spread through air spaces in lung adenocarcinoma.
Yin Qifan,Wang Huien,Cui Hongshang,Wang Wenhao,Yang Guang,Qie Peng,Xun Xuejiao,Han Shaohui,Liu Huining
Journal of cardiothoracic surgery
OBJECTIVE:Spread through air space (STAS) is a novel invasive pattern of lung adenocarcinoma and is also a risk factor for recurrence and worse prognosis of lung adenocarcinoma after sublobar resection. The aims of this study are to evaluate the association between computed tomography (CT)-based features and STAS for preoperative prediction of STAS in lung adenocarcinoma, eventually, which could help us choose appropriate surgical type. METHODS:Systematic research was conducted to search for studies published before September 1, 2019. The association between CT-based features of radiological tumor size>2 cm、pure solid nodule、 part-solid nodule or Percentage of solid component (PSC)>50% and STAS was evaluated. According to rigorous inclusion and exclusion criteria. Eight studies including 2385 patients published between 2015 and 2018 were finally enrolled in our meta-analysis. RESULTS:Our results clearly depicted that there is no significant relationship between radiological tumor size>2 cm and STAS with the combined OR of 1.47(95% CI:0.86-2.51). Meta-analysis of 3 studies showed that pure solid nodule in CT image were more likely to spread through air spaces with pooled OR of 3.10(95%CI2.17-4.43). Meta-analysis of 5 studies revealed the part-solid nodule in CT image may be more likely to appear STAS in adenocarcinoma (ADC) (combined OR:3.10,95%CI:2.17-4.43). PSC>50% in CT image was a significant independent predictor in the diagnosis of STAS in ADC from our meta-analysis with combined OR of 2.95(95%CI:1.88-4.63). CONCLUSION:In conclusion, The CT-based features of pure solid nodule、part-solid nodule、PSC>50% are promising imaging biomarkers for predicting STAS in ADC and may substantially influence the choice of surgical type. In future, more studies with well-designed and large-scale are needed to confirm the conclusion.
10.1186/s13019-020-01287-9
Morphological classification of pre-invasive lesions and early-stage lung adenocarcinoma based on CT images.
Gao Feng,Li Ming,Zhang Ziwei,Xiao Li,Zhang Guozhen,Zheng Xiangpeng,Hua Yanqing,Li Jianying
European radiology
OBJECTIVE:To retrospectively analyze the computed tomography (CT) features in patients with pre-invasive lesions and early-stage lung adenocarcinoma and to explore the correlation between tumor morphological changes and pathological diagnoses. MATERIALS AND METHODS:CT morphological characteristics in 2106 patients with pre-invasive (stage 0) and early-stage (stage I) lung adenocarcinoma were analyzed; lesions were confirmed by surgical pathology. Based on the morphological characteristics, the lesions were divided into eight types: I (cotton ball, ground-glass nodules), II (solid fill), III (granular), IV (dendriform), V (bubble-like lucencies), VI (alveolate or honeycomb), VII (scar-like), and VIII (notched or umbilication). The different distributions of eight morphological types in pathological types of the lesions and subtypes of invasive adenocarcinoma were analyzed by chi-squared or Fisher's exact test. Correlation between the percentage of ground-glass opacity in the lesions and pathology types were analyzed by two-tailed Pearson's test. RESULTS:A negative correlation was observed between the pathological types and proportion of ground-glass component in the lesions (p < 0.001 and r = - 0.583). Significant differences in morphological characteristics among various pathological types of pre-invasive lesions and early lung adenocarcinomas were observed (p < 0.05). Furthermore, among the different pathological subtypes of stage I invasive adenocarcinoma, the differences in their manifestation as morphological types I, II, III, and VI were statistically significant (p < 0.05). CONCLUSION:The eight types of morphological classification of pre-invasive lesions and early-stage (stage 0 or stage I) lung adenocarcinoma has different pathological bases, and morphological classification may be useful for the diagnosis and differential diagnosis of lung adenocarcinoma. KEY POINTS:• CT morphological classification of pre-invasive lesions and lung adenocarcinoma is intuitive. • CT morphological classification characterizes morphological changes of the entire lesion. • Different pathological types of lung adenocarcinoma have different morphological features.
10.1007/s00330-019-06149-0
Prediction model based on 18F-FDG PET/CT radiomic features and clinical factors of EGFR mutations in lung adenocarcinoma.
Zhao Hong-Yue,Su Ye-Xin,Zhang Lin-Han,Fu Peng
Neoplasma
The aim of this study was to build a prediction model for epidermal growth factor receptor (EGFR) mutations in lung adenocarcinoma. A retrospective analysis was performed on 88 patients with lung adenocarcinoma. All patients underwent an 18F-FDG PET/CT scan and genetic testing of EGFR before the treatment. In the training set, the radiomic features and clinical factors were screened out, and model-1 based on CT radiomic features, model-2 based on PET radiomic features, model-3 based on clinical factors, and model-4 based on radiomic features combined with clinical factors were established, respectively. The performance of the prediction model was assessed by area under the receiver operating characteristic (ROC) curve (AUC). The DeLong test was used to compare the performance of the models to screen out the optimal model, and then built the nomogram of the optimal model. The effect and clinical utility of the nomogram was verified in the validation cohort. In our analysis, model-4 was superior to the other prediction models in identifying EGFR mutations. The AUC was 0.864 (95% CI: 0.777-0.950), with a sensitivity of 0.714 and a specificity of 0.784. The nomogram of model-4 was established. In the validation cohort, the concordance index (C-index) value of the calibration curve of the nomogram model was 0.778 (95%CI: 0.585-0.970), and the nomogram had a good clinical utility. We demonstrated that the model based on 18F-FDG PET/CT radiomic features combined with clinical factors could predict EGFR mutations in lung adenocarcinoma, which was expected to be an important supplement to molecular diagnosis.
10.4149/neo_2021_201222N1388
18F-FDG PET/CT of Lung Adenocarcinoma With Ovarian Metastases.
García-Talavera Paloma,Colinas Daniel,Tamayo Pilar,Fra Joaquin,Montes Arnold
Clinical nuclear medicine
Patient was a 52-year-old woman with medical history of lung adenocarcinoma operated in 2009 (stage I, T2 N0 M0), showing increasing levels of tumor markers and a doubtful retrocrural adenopathy by means of CT scan with intravenous contrast. An F-FDG PET/CT was performed, which showed 2 hypermetabolic foci in both annexes. The anatomopathological study detected bilateral ovarian adenocarcinoma compatible with metastases of pulmonary origin.
10.1097/RLU.0000000000002490
CT radiomics-based prediction of anaplastic lymphoma kinase and epidermal growth factor receptor mutations in lung adenocarcinoma.
European journal of radiology
PURPOSE:To develop and validate a CT-based radiomic model to simultaneously diagnose anaplastic lymphoma kinase (ALK) rearrangements and epidermal growth factor receptor (EGFR) mutation status of lung adenocarcinoma and to assess whether peritumoural radiomic features add value in the prediction of mutation status. METHODS:503 patients with pathologically proven lung adenocarcinoma containing information on the mutation status were retrospectively included. Intratumoural and peritumoural radiomic features of the primary lesion were extracted from CT. We proposed two-level stepwise binary radiomics-based classification models to diagnose ALK (step1) and EGFR mutation status (step2). The performance of proposed models and added value of peritumoural radiomic features were evaluated by using the areas under receiver operating characteristic curves (AUC) and Obuchowski index in the development and validation sets. RESULTS:Regarding the prediction of ALK rearrangement, the diagnostic performance of the intratumoural radiomic model showed the AUC of 0.77 and 0.68 for the development and validation sets, respectively. As for EGFR mutation, the diagnostic performance of the intratumoural radiomic model showed the AUCs of 0.64 and 0.62 for the development and validation sets, respectively. The radiomics added value to the model based on clinical features (development set [radiomics + clinical model vs. clinical model]: Obuchowski index, 0.76 vs. 0.66, p < 0.001; validation set: 0.69 vs. 0.61, p = 0.075). Adding peritumoural features resulted in no improvement in terms of model performance. CONCLUSION:The CT radiomics-based model allowed the simultaneous prediction of the presence of ALK and EGFR mutations while adding value to the clinical features.
10.1016/j.ejrad.2021.109710
A comparative study for the evaluation of CT-based conventional, radiomic, combined conventional and radiomic, and delta-radiomic features, and the prediction of the invasiveness of lung adenocarcinoma manifesting as ground-glass nodules.
Clinical radiology
AIM:To investigate and compare the performance of conventional, radiomic, combined, and delta-radiomic features to predict the invasiveness of lung adenocarcinoma manifesting as ground-glass nodules (GGNs). MATERIALS AND METHODS:The present retrospective study included 216 GGNs confirmed surgically as pulmonary adenocarcinomas. All the thin-section computed tomography (CT) images were imported into the software of the United Imaging Intelligence research portal, and radiomic features were extracted with three-dimensional (3D) regions of interest. Least Absolute Shrinkage and Selection Operator was used to select the optimal radiomic features. Four models were constructed, including conventional, radiomic, combined conventional and radiomic, and delta-radiomic models. The receiver operating characteristic curves were built to evaluate the validity of these. RESULTS:The type, long diameter, shape, margin, vacuole, air bronchus, vascular convergence, and pleural traction exhibited significant differences between pre-invasive lesions (PILs)/minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IA) groups were selected for conventional model building. Nine radiomic features were selected to build the radiomic model. The four models indicated optimal performance (AUC > 0.7). The radiomic and combined models exhibited the highest diagnostic efficiency, and their AUC were 0.89 and 0.88 in the training set, and 0.87 and 0.88 in the validation set, respectively. The delta-radiomic model indicated that the AUC was 0.83 in the training set, and 0.76 in the validation set. Finally, the conventional model exhibited an AUC in the training and validation sets of 0.78 and 0.76. CONCLUSIONS:The radiomic model and combined model, in particular, and the delta-radiomic model all demonstrated improved diagnostic efficiency in differentiating IA from PIL/MIA than that of the conventional model.
10.1016/j.crad.2022.06.004
Different CT slice thickness and contrast-enhancement phase in radiomics models on the differential performance of lung adenocarcinoma.
Thoracic cancer
BACKGROUND:To investigate the effects of computed tomography (CT) reconstruction slice thickness and contrast-enhancement phase on the differential diagnosis performance of radiomic signature in lung adenocarcinoma. METHODS:A total of 187 patients who had been pathologically confirmed with lung adenocarcinoma and nonadenocarcinoma were divided into a training cohort (n = 149) and validation cohort (n = 38). All the patients underwent contrast-enhanced CT and the images were reconstructed with different slice thickness. The radiomic features were extracted from different slice thickness and scan phase. The logistic regression (LR) algorithm was used to build a machine learning model for each group. The area under the curve (AUC) obtained from the receiver operating characteristic (ROC) curve and DeLong test was used to evaluate its discriminating performance. RESULTS:Finally, 34 image features and five semantic features were selected to establish a radiomics model. Based on the three contrast-enhanced CT phases and four reconstruction slice thickness, 12 groups of radiomics models showed good discrimination ability with the AUCs range from 0.9287 to 0.9631, sensitivity range from 0.8349 to 0.9083, specificity range from 0.825 to 0.925 in the training group. Similar results were observed in the validation group. However, there was no statistical significance between the different CT scan phase groups and different slice thickness (p > 0.05). CONCLUSIONS:The radiomic analysis of contrast-enhanced CT can be used for the differential diagnosis of lung adenocarcinoma. Moreover, different slice thickness and contrast-enhanced scan phase did not affect the discriminating ability in the radiomics models.
10.1111/1759-7714.14459
Histological subtypes of solid-dominant invasive lung adenocarcinoma: differentiation using dual-energy spectral CT.
Li Q,Li X,Li X-Y,He X-Q,Chu Z-G,Luo T-Y
Clinical radiology
AIM:To investigate the value of dual-energy spectral computed tomography (DESCT) for evaluating the histological subtypes of solid-dominant invasive lung adenocarcinoma (SILADC). MATERIALS AND METHODS:Sixty-seven patients with SILADC were enrolled. All patients underwent DESCT and were divided into Group I (those with a lepidic/acinar/papillary predominant pattern) and Group II (those with a solid/micropapillary predominant pattern) based on their correlation with prognosis. Patient clinicopathological characteristics, DESCT morphological features, and quantitative parameters of the tumours were compared between both groups. Multiparametric analysis was performed using binary logistic regression with DESCT findings. Receiver operating characteristic (ROC) curves were used to assess the diagnostic performance of single-parameter and multiparametric analysis. RESULTS:Patient gender, lymph nodes status, pathological TNM stage, and histological differentiation significantly differed between the two groups (all p<0.05). Moreover, significant differences were observed between both groups in DESCT morphological features including tumour size, necrosis, calcification, air bronchogram, and vascular convergence sign, and quantitative parameters including K, effective atomic number, and water concentration on unenhanced CT and iodine concentration in the arterial and venous phases (all p<0.05). Multiparametric analysis showed that tumour size, air bronchogram, K and effective atomic number on unenhanced CT were the most effective variations for predicting the histological subtypes of SILADC and obtained an area under the ROC curve (AUC) of 0.906. CONCLUSIONS:DESCT was useful for differentiating histological subtypes with different prognosis of SILADC.
10.1016/j.crad.2020.08.034
Pathologic classification of adenocarcinoma of lung.
Van Schil Paul E,Sihoe Alan D L,Travis William D
Journal of surgical oncology
Recently, the 1999/2004 World Health Organization (WHO) classification of adenocarcinoma became less useful from a clinical standpoint as most adenocarcinomas belonged to the mixed subtype and the term bronchioloalveolar carcinoma (BAC) gave rise to much confusion among clinicians. For these reasons a new adenocarcinoma classification was introduced in 2011 by a joint working group of the International Association for the Study of Lung Cancer (IASLC), American Thoracic Society (ATS), and European Respiratory Society (ERS). This represents an international, multidisciplinary effort joining pathologists, molecular biologists, pulmonary physicians, thoracic oncologists, radiologists, and thoracic surgeons. Currently, a distinction is made between pre-invasive lesions, minimally invasive and invasive lesions. The confusing term BAC is not used anymore and new subcategories include adenocarcinoma in situ and minimally invasive adenocarcinoma. Several aspects of this classification are discussed with main emphasis on its correlation with imaging techniques and its impact on diagnosis, treatment and prognosis. On chest computed tomography (CT) a distinction is made between solid and subsolid nodules, the latter comprising ground glass opacities (GGO), and partly solid lesions. Several studies incorporating CT and positron emission tomographic (PET) data show a good imaging-pathologic correlation. With the implementation of screening programs early lung cancer has become a hotly debated topic and sublobar resection is currently reconsidered for early lesions without lymph node involvement. This new classification will also have an impact on the TNM classification. Thoracic surgeons will continue to play a major role in the application, evaluation and further refinement of this new adenocarcinoma classification.
10.1002/jso.23397
Is there any correlation between spectral CT imaging parameters and PD-L1 expression of lung adenocarcinoma?
Chen Mai-Lin,Shi An-Hui,Li Xiao-Ting,Wei Yi-Yuan,Qi Li-Ping,Sun Ying-Shi
Thoracic cancer
BACKGROUND:The aim of this study was to explore whether spectral computed tomography (CT) imaging parameters are associated with PD-L1 expression of lung adenocarcinoma. METHODS:Spectral CT imaging parameters (iodine concentrations [IC] of lesion in arterial phase [ICLa] and venous phase [ICLv], normalized IC [NICa/NICv]-normalized to the IC in the aorta, slope of the spectral HU curve [λHUa/λHUv] and enhanced monochromatic CT number [CT40keVa/v, CT70keVa/v] on 40 and 70 keV images) were analyzed in 34 prospectively enrolled lung adenocarcinoma patients with common molecular pathological markers including PD-L1 expression detected with immunohistochemistry. Patients were divided into two groups: positive PD-L1 expression and negative PD-L1 expression groups. Two-sample Mann-Whitney U test was used to test the difference of spectral CT imaging parameters between the two groups. RESULTS:The CT40keVa (127.03 ± 37.92 vs. -54.69 ± 262.04), CT40keVv (124.39 ± 34.71 vs. -45.73 ± 238.97), CT70keVa (49.56 ± 11.76 vs. -136.51 ± 237.08) and CT70keVv (46.13 ± 15.81 vs. -133.10 ± 230.72) parameters in the positive PD-L1 expression group of lung adenocarcinoma were significantly higher than the negative PD-L1 expression group (all P < 0.05). There was no difference detected in IC, NIC and λHU of the arterial and venous phases between both groups (all P > 0.05). CONCLUSION:CT40keVa, CT40keVv, CT70keVa and CT70keVv were increased in positive PD-L1 expression. These parameters may be used to distinguish the PD-L1 expression state of lung adenocarcinoma.
10.1111/1759-7714.13273
Lung adenocarcinoma associated with cystic airspaces: Predictive value of CT features in assessing pathologic invasiveness.
European journal of radiology
OBJECTIVES:Lung adenocarcinoma associated with cystic airspaces (LACA) is a unique entity with limited understanding. Our aim was to evaluate the radiological characteristics of LACA and to study which criteria were predictive of invasiveness. METHODS:A retrospective monocentric analysis of consecutive patients with pathologically confirmed LACA was performed. The diagnosed adenocarcinomas were classified into preinvasive (atypical adenomatous hyperplasia, adenocarcinoma in situ, or minimally invasive adenocarcinoma) and invasive adenocarcinomas. Eight clinical features and twelve CT features were evaluated. Univariable and multivariable analyses were performed to analyse the correlation between invasiveness, and CT and clinical features. The inter-observer agreement was evaluated using κ statistics and intraclass correlation coefficients. The predictive performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC). RESULTS:A total of 252 patients with 265 lesions (128 men and 124 women; mean age, 58.0 ± 11.1 years) were enrolled. Multivariable logistic regression indicated that multiple cystic airspaces (OR, 5.599; 95 % CI, 1.865-16.802), irregular shape of cystic airspace (OR, 3.236; 95 % CI, 1.073-9.761), entire tumour size (OR, 1.281; 95 % CI, 1.075-1.526), and attenuation (OR, 1.007; 95 % CI, 1.005-1.010) were independent risk factors for invasive LACA. The AUC of the logistic regression model was 0.964 (95 % CI, 0.944-0.985). CONCLUSION:Multiple cystic airspaces, irregular shape of cystic airspace, entire tumour size, and attenuation were identified as independent risk factors for invasive LACA. The prediction model gives a good predictive performance, providing additional diagnostic information.
10.1016/j.ejrad.2023.110947
Diagnosis of Invasive Lung Adenocarcinoma Based on Chest CT Radiomic Features of Part-Solid Pulmonary Nodules: A Multicenter Study.
Wu Guangyao,Woodruff Henry C,Shen Jing,Refaee Turkey,Sanduleanu Sebastian,Ibrahim Abdalla,Leijenaar Ralph T H,Wang Rui,Xiong Jingtong,Bian Jie,Wu Jianlin,Lambin Philippe
Radiology
Background Solid components of part-solid nodules (PSNs) at CT are reflective of invasive adenocarcinoma, but studies describing radiomic features of PSNs and the perinodular region are lacking. Purpose To develop and to validate radiomic signatures diagnosing invasive lung adenocarcinoma in PSNs compared with the Brock, clinical-semantic features, and volumetric models. Materials and Methods This retrospective multicenter study (, NCT03872362) included 291 patients (median age, 60 years; interquartile range, 55-65 years; 191 women) from January 2013 to October 2017 with 297 PSN lung adenocarcinomas split into training ( = 229) and test ( = 68) data sets. Radiomic features were extracted from the different regions (gross tumor volume [GTV], solid, ground-glass, and perinodular). Random-forest models were trained using clinical-semantic, volumetric, and radiomic features, and an online nodule calculator was used to compute the Brock model. Performances of models were evaluated using standard metrics such as area under the curve (AUC), accuracy, and calibration. The integrated discrimination improvement was applied to assess model performance changes after the addition of perinodular features. Results The radiomics model based on ground-glass and solid features yielded an AUC of 0.98 (95% confidence interval [CI]: 0.96, 1.00) on the test data set, which was significantly higher than the Brock (AUC, 0.83 [95% CI: 0.72, 0.94]; = .007), clinical-semantic (AUC, 0.90 [95% CI: 0.83, 0.98]; = .03), volumetric GTV (AUC, 0.87 [95% CI: 0.78, 0.96]; = .008), and radiomics GTV (AUC, 0.88 [95% CI: 0.80, 0.96]; = .01) models. It also achieved the best accuracy (93% [95% CI: 84%, 98%]). Both this model and the model with added perinodular features showed good calibration, whereas adding perinodular features did not improve the performance (integrated discrimination improvement, -0.02; = .56). Conclusion Separating ground-glass and solid CT radiomic features of part-solid nodules was useful in diagnosing the invasiveness of lung adenocarcinoma, yielding a better predictive performance than the Brock, clinical-semantic, volumetric, and radiomics gross tumor volume models. See also the editorial by Nishino in this issue. Published under a CC BY 4.0 license.
10.1148/radiol.2020192431
CT texture analysis of lung adenocarcinoma: can Radiomic features be surrogate biomarkers for EGFR mutation statuses.
Mei Dongdong,Luo Yan,Wang Yan,Gong Jingshan
Cancer imaging : the official publication of the International Cancer Imaging Society
OBJECTIVE:To investigate whether radiomic features can be surrogate biomarkers for epidermal growth factor receptor (EGFR) mutation statuses. MATERIALS AND METHODS:Two hundred ninety six consecutive patients, who underwent CT examinations before operation within 3 months and had EGFR mutations tested, were enrolled in this retrospective study. CT texture features were extracted using an open-source software with whole volume segmentation. The association between CT texture features and EGFR mutation statuses were analyzed. RESULTS:In the 296 patients, there were 151 patients with EGFR mutations (51%). Logistic analysis identified that lower age (Odds Ratio[OR]: 0.968,95% confidence interval [CI]:0.946~0.990, p = 0.005) and a radiomic feature named GreyLevelNonuniformityNormalized (OR: 0.012, 95% CI:0.000~0.352, p = 0.01) were predictors for exon 19 mutation; higher age (OR: 1.027, 95%CI:1.003~1.052,p = 0.025), female sex (OR: 2.189, 95%CI:1.264~3.791, p = 0.005) and a radiomic feature named Maximum2DDiameterColumn (OR: 0.968, 95%CI:0.946~0.990], p = 0.005) for exon 21 mutation; and female sex (OR: 1.883,95%CI:1.064~3.329, p = 0.030), non-smoking status (OR: 2.070, 95%CI:1.090~3.929, p = 0.026) and a radiomic feature termed SizeZone NonUniformityNormalized (OR: 0.010, 95% CI:0.0001~0.852, p = 0.042) for EGFR mutations. Areas under the curve (AUCs) of combination with clinical and radiomic features to predict exon 19 mutation, exon 21 mutation and EGFR mutations were 0.655, 0.675 and 0.664, respectively. CONCLUSION:Several radiomic features are associated with EGFR mutation statuses of lung adenocarcinoma. Combination with clinical files, moderate diagnostic performance can be obtained to predict EGFR mutation status of lung adenocarcinoma. Radiomic features might harbor potential surrogate biomarkers for identification of EGRF mutation statuses.
10.1186/s40644-018-0184-2
Radiomics for lung adenocarcinoma manifesting as pure ground-glass nodules: invasive prediction.
Sun Yingli,Li Cheng,Jin Liang,Gao Pan,Zhao Wei,Ma Weiling,Tan Mingyu,Wu Weilan,Duan Shaofeng,Shan Yuqing,Li Ming
European radiology
OBJECTIVES:To investigate the value of radiomics based on CT imaging in predicting invasive adenocarcinoma manifesting as pure ground-glass nodules (pGGNs). METHODS:This study enrolled 395 pGGNs with histopathology-confirmed benign nodules or adenocarcinoma. A total of 396 radiomic features were extracted from each labeled nodule. A Rad-score was constructed with the least absolute shrinkage and selection operator (LASSO) in the training set. Multivariate logistic regression analysis was conducted to establish the radiographic model and the combined radiographic-radiomics model. The predictive performance was validated by receiver operating characteristic (ROC) curve. Based on the multivariate logistic regression analysis, an individual prediction nomogram was developed and the clinical utility was assessed. RESULTS:Five radiomic features and four radiographic features were selected for predicting the invasive lesions. The combined radiographic-radiomics model (AUC 0.77; 95% CI, 0.69-0.86) performed better than the radiographic model (AUC 0.71; 95% CI, 0.62-0.81) and Rad-score (AUC 0.72; 95% CI, 0.63-0.81) in the validation set. The clinical utility of the individualized prediction nomogram developed using the Rad-score, margin, spiculation, and size was confirmed in the validation set. The decision curve analysis (DCA) indicated that using a model with Rad-score to predict the invasive lesion would be more beneficial than that without Rad-score and the clinical model. CONCLUSIONS:The proposed radiomics-based nomogram that incorporated the Rad-score, margin, spiculation, and size may be utilized as a noninvasive biomarker for the assessment of invasive prediction in patients with pGGNs. KEY POINTS:• CT-based radiomics analysis helps invasive prediction manifested as pGGNs. • The combined radiographic-radiomics model may be utilized as a noninvasive biomarker for predicting invasive lesion for pGGNs. • Radiomics-based individual nomogram may serve as a vital decision support tool to identify invasive pGGNs, obviating further workup and blind follow-up.
10.1007/s00330-020-06776-y
The many faces of lung adenocarcinoma: A pictorial essay.
Pascoe Heather M,Knipe Henry C,Pascoe Diane,Heinze Stefan B
Journal of medical imaging and radiation oncology
Lung adenocarcinoma has a spectrum of appearances on CT, many of which mimic non-malignant processes. The general radiologist has a major role in guiding the management of abnormalities detected on chest CT and an awareness of these appearances is vital. We describe the protean imaging manifestations of lung adenocarcinoma.
10.1111/1754-9485.12779
Primary Invasive Mucinous Adenocarcinoma of the Lung: Prognostic Value of CT Imaging Features Combined with Clinical Factors.
Wang Tingting,Yang Yang,Liu Xinyue,Deng Jiajun,Wu Junqi,Hou Likun,Wu Chunyan,She Yunlang,Sun Xiwen,Xie Dong,Chen Chang
Korean journal of radiology
OBJECTIVE:To investigate the association between CT imaging features and survival outcomes in patients with primary invasive mucinous adenocarcinoma (IMA). MATERIALS AND METHODS:Preoperative CT image findings were consecutively evaluated in 317 patients with resected IMA from January 2011 to December 2015. The association between CT features and long-term survival were assessed by univariate analysis. The independent prognostic factors were identified by the multivariate Cox regression analyses. The survival comparison of IMA patients was investigated using the Kaplan-Meier method and propensity scores. Furthermore, the prognostic impact of CT features was assessed based on different imaging subtypes, and the results were adjusted using the Bonferroni method. RESULTS:The median follow-up time was 52.8 months; the 5-year disease-free survival (DFS) and overall survival rates of resected IMAs were 68.5% and 77.6%, respectively. The univariate analyses of all IMA patients demonstrated that 15 CT imaging features, in addition to the clinicopathologic characteristics, significantly correlated with the recurrence or death of IMA patients. The multivariable analysis revealed that five of them, including imaging subtype ( = 0.002), spiculation ( < 0.001), tumor density ( = 0.008), air bronchogram ( < 0.001), emphysema ( < 0.001), and location ( = 0.029) were independent prognostic factors. The subgroup analysis demonstrated that pneumonic-type IMA had a significantly worse prognosis than solitary-type IMA. Moreover, for solitary-type IMAs, the most independent CT imaging biomarkers were air bronchogram and emphysema with an adjusted p value less than 0.05; for pneumonic-type IMA, the tumors with mixed consolidation and ground-glass opacity were associated with a longer DFS (adjusted = 0.012). CONCLUSION:CT imaging features characteristic of IMA may provide prognostic information and individual risk assessment in addition to the recognized clinical predictors.
10.3348/kjr.2020.0454
Ranking the most influential predictors of CT-based radiomics feature values in metastatic lung adenocarcinoma.
European journal of radiology
PURPOSE:To investigate which acquisition, post-processing, tumor, and patient characteristics contribute the most to the value of radiomics features (RFs) in lung adenocarcinoma in order to better understand and order the potential sources of bias in radiomics studies in a multivariate setting. METHODS:This single-center retrospective study included all consecutive patients with newly-diagnosed lung adenocarcinoma treated between December 2016 and September 2018 who had pre-treatment contrast-enhanced CT-scan showing ≥ 2 target lesions per response evaluation criteria in solid tumors (RECIST) v1.1. All measurable lesions were manually segmented; 49 RFs were extracted using LIFEx v7.0.0. Afterwards, we reverted the usual radiomics approach (i.e., predicting a clinical outcome base on multiple RFs). To do so, for each RF, random forests and linear regression algorithms were trained using cross-validation to predict the RF value depending on the following variables: patient, mutational status, phase of CT-scan acquisition, discretization (binsize), lesion location, lesion volume, and best response obtained during the first line of treatment (partial response per RECIST vs other). The most important contributors to the value of reproducible RFs (intra-class correlation coefficient > 0.80) according to the best random forests model (selected via R-squared) were ranked. RESULTS:101 patients (median age: 62.3) were included, with a median of 5 target lesions per patient (range: 2-10) providing 466 segmented lesions. Twenty-nine RFs were reproducible. The most important predictors of the reproducible RFs values were, in order: tumor volume, binsize, tumor location, CT-scan phase, KRAS mutation, and treatment response (average importance: 61.7%, 57.4%, 8.1%, 3.3%, 3%, and 2.7%, respectively). The treatment response and KRAS and EGFR/ROS1/ALK mutational status remained independently correlated with the RF value for 64.3%, 32.1%, and 50% reproducible RFs, respectively. CONCLUSION:Tumor volume, location, acquisition and post-processing parameters should systematically be incorporated in radiomics-based modeling; however, most reproducible RFs do have significant relationships with mutational status and treatment response.
10.1016/j.ejrad.2022.110472
Comparative proteomic analysis of exhaled breath condensate between lung adenocarcinoma and CT-detected benign pulmonary nodule patients.
Cancer biomarkers : section A of Disease markers
BACKGROUND:Lung cancer is the leading cause of cancer mortality worldwide. The collection of exhaled breath condensate (EBC) is a non-invasive method that may have enormous potential as a biomarker for the early detection of lung cancer. OBJECTIVE:To investigate the proteomic differences of EBC between lung cancer and CT-detected benign nodule patients, and determine whether these proteins could be potential biomarkers. METHODS:Proteomic analysis was performed on individual samples from 10 lung cancer patients and 10 CT-detected benign nodule patients using data-independent acquisition (DIA) mass spectrometry. RESULTS:A total of 1,254 proteins were identified, and 21 proteins were differentially expressed in the lung adenocarcinoma group compared to the benign nodule group (p< 0.05). The GO analysis showed that most of these proteins were involved in neutrophil-related biological processes, and the KEGG analysis showed these proteins were mostly annotated to pyruvate and propanoate metabolism. Through protein-protein interactions (PPIs) analysis, ME1 and LDHB contributed most to the interaction-network of these proteins. CONCLUSION:Significantly differentially expressed proteins were detected between lung cancer and the CT-detected benign nodule group from EBC samples, and these proteins might serve as potential novel biomarkers of EBC for early lung cancer detection.
10.3233/CBM-203269
CT-defined Visceral Pleural Invasion in T1 Lung Adenocarcinoma: Lack of Relationship to Disease-Free Survival.
Kim Hyungjin,Goo Jin Mo,Kim Young Tae,Park Chang Min
Radiology
BackgroundPathologic visceral pleural invasion (pVPI) leads to upstaging from T1 to T2. However, it is unclear whether the CT features for pVPI can be reliably used as a clinical T2 descriptor for preoperative staging.PurposeTo validate the diagnostic accuracy and analyze the prognostic value of CT findings for the prediction of pVPI in patients with resected node-negative lung adenocarcinoma.Materials and MethodsThis retrospective cohort study included clinical T1N0M0 adenocarcinomas resected between 2009 and 2015. The diagnostic CT findings suggestive of pVPI were evaluated by a thoracic radiologist. The accuracy of diagnostic CT findings in relation to pVPI and accuracy for disease-free survival (DFS) were evaluated by using test performance metrics and multivariable Cox regression analysis, respectively.ResultsThe authors analyzed 695 patients (median age, 63 years; 411 women). Data for pVPI were not available in six patients. The accuracy of CT features for pVPI ranged from 62.7% (432 of 689 patients) to 72.3% (498 of 689 patients). Positive predictive values ranged from 44.1% (173 of 392 patients) to 56.4% (88 of 156 patients), which indicated that about half of the CT-based predictions were false-positive. Multivariable Cox regression models showed that none of the combinations of CT findings were independent predictors of DFS (adjusted hazard ratios, 1.40, 1.48, 1.06, and 1.21 for each combination; > .05 for all). In addition, pVPI was not an independent prognostic factor (adjusted hazard ratio, 1.27; = .26), whereas age and clinical T category were independent prognostic factors in all Cox models ( < .05 for all).ConclusionCT features of pathologic visceral pleural invasion (pVPI) have an accuracy of 62.7%-72.3%. CT features of pVPI were not independent prognostic factors for disease-free survival in clinical T1 lung adenocarcinomas. This argues against the use of CT features of visceral pleural invasion as T2 descriptors in the clinical staging of lung cancer.© RSNA, 2019See also the editorial by Nishino in this issue.
10.1148/radiol.2019190297
Use of deep learning to predict postoperative recurrence of lung adenocarcinoma from preoperative CT.
International journal of computer assisted radiology and surgery
PURPOSE:Although surgery is the primary treatment for lung cancer, some patients experience recurrence at a certain rate. If postoperative recurrence can be predicted early before treatment is initiated, it may be possible to provide individualized treatment for patients. Thus, in this study, we propose a computer-aided diagnosis (CAD) system that predicts postoperative recurrence from computed tomography (CT) images acquired before surgery in patients with lung adenocarcinoma using a deep convolutional neural network (DCNN). METHODS:This retrospective study included 150 patients who underwent curative surgery for primary lung adenocarcinoma. To create original images, the tumor part was cropped from the preoperative contrast-enhanced CT images. The number of input images to the DCNN was increased to 3000 using data augmentation. We constructed a CAD system by transfer learning using a pretrained VGG19 model. Tenfold cross-validation was performed five times. Cases with an average identification rate of 0.5 or higher were determined to be a recurrence. RESULTS:The median duration of follow-up was 73.2 months. The results of the performance evaluation showed that the sensitivity, specificity, and accuracy of the proposed method were 0.75, 0.87, and 0.82, respectively. The area under the receiver operating characteristic curve was 0.86. CONCLUSION:We demonstrated the usefulness of DCNN in predicting postoperative recurrence of lung adenocarcinoma using preoperative CT images. Because our proposed method uses only CT images, we believe that it has the advantage of being able to assess postoperative recurrence on an individual patient basis, both preoperatively and noninvasively.
10.1007/s11548-022-02694-0
Lung Cancer Screening by Low-Dose CT Scan: Baseline Results of a French Prospective Study.
Leleu Olivier,Basille Damien,Auquier Marianne,Clarot Caroline,Hoguet Estelle,Pétigny Valérie,Addi Amale Aït,Milleron Bernard,Chauffert Bruno,Berna Pascal,Jounieaux Vincent
Clinical lung cancer
BACKGROUND:Lung cancer mortality has been found to decrease significantly with low-dose (LD) computed tomographic (CT) screening among current or former smokers. However, such a screening program is not implemented in France. This study assessed the feasibility of a lung cancer screening program using LD CT scan in a French administrative territory. We report here the results of the first screening round. PATIENTS AND METHODS:DEP KP80 was a single-arm prospective study initiated in May 2016. Participants aged 55 to 74 years, current or former smokers of ≥ 30 pack-years, were recruited. An annual LD CT scan was scheduled. Our algorithms considered nodules < 5 mm as negative findings and nodules > 10 mm as positive; for intermediate nodules between 5 and 10 mm, 3-month CT scan with doubling time measurement was recommended. All general practitioners, pulmonologists, and radiologists from the Somme department were solicited to participate. Subjects were selected by general practitioners or pulmonologists who checked the inclusion criteria and prescribed the CT scan. RESULTS:Over a 2.5-year period, 1307 subjects were recruited. Screening was negative in 733 cases (77.2%), positive in 54 (5.7%), and indeterminate in 162 (17.1%). After the 3-month scans, 57 subjects screened positive: 26 patients exhibited 31 lung cancers (67.7% of stage 0 to I), of whom 76.9% underwent surgical resection, and 29 had no cancer (false-positive rate = 3.1%). The prevalence of lung cancer was 2.7%. CONCLUSION:This study demonstrated the feasibility of organized lung cancer screening using LD CT scan within a real-life context in the general population.
10.1016/j.cllc.2019.10.014
Differentiation of predominant subtypes of lung adenocarcinoma using a quantitative radiomics approach on CT.
Park Sohee,Lee Sang Min,Noh Han Na,Hwang Hye Jeon,Kim Seonok,Do Kyung-Hyun,Seo Joon Beom
European radiology
OBJECTIVES:To develop a model for differentiating the predominant subtype-based prognostic groups of lung adenocarcinoma using CT radiomic features, and to validate its performance in comparison with radiologists' assessments. METHODS:A total of 993 patients presenting with invasive lung adenocarcinoma between March 2010 and June 2016 were identified. Predominant histologic subtypes were categorized into three groups according to their prognosis (group 0: lepidic; group 1: acinar/papillary; group 2: solid/micropapillary). Seven hundred eighteen radiomic features were extracted from segmented lung cancers on contrast-enhanced CT. A model-development set was formed from the images of 893 patients, while 100 image sets were reserved for testing. A least absolute shrinkage and selection operator method was used for feature selection. Performance of the radiomic model was evaluated using receiver operating characteristic curve analysis, and accuracy on the test set was compared with that of three radiologists with varying experiences (6, 7, and 19 years in chest CT). RESULTS:Our model differentiated the three groups with areas under the curve (AUCs) of 0.892 and 0.895 on the development and test sets, respectively. In pairwise discrimination, the AUC was highest for group 0 vs. 2 (0.984). The accuracy of the model on the test set was higher than the averaged accuracy of the three radiologists without statistical significance (73.0% vs. 61.7%, p = 0.059). For group 2, the model achieved higher PPV than the observers (85.7% vs. 35.0-48.4%). CONCLUSIONS:Predominant subtype-based prognostic groups of lung adenocarcinoma were classified by a CT-based radiomic model with comparable performance to radiologists. KEY POINTS:• A CT-based radiomic model differentiated three prognosis-based subtype groups of lung adenocarcinoma with areas under the curve (AUCs) of 0.892 and 0.895 on development and test sets, respectively. • The CT-based radiomic model showed near perfect discrimination between group 0 and group 2 (AUCs, 0.984-1.000). • The accuracy of the CT-based radiomic model was comparable to the averaged accuracy of the three radiologists with 6, 7, and 19 years of clinical experience in chest CT (73.0% vs. 61.7%, p = 0.059), achieving a higher positive predictive value for group 2 than the observers (85.7% vs. 35.0-48.4%).
10.1007/s00330-020-06805-w
Identification of pathological subtypes of early lung adenocarcinoma based on artificial intelligence parameters and CT signs.
Fang Weiyuan,Zhang Guorui,Yu Yali,Chen Hongjie,Liu Hong
Bioscience reports
OBJECTIVE:To explore the value of quantitative parameters of artificial intelligence (AI) and computed tomography (CT) signs in identifying pathological subtypes of lung adenocarcinoma appearing as ground-glass nodules (GGNs). METHODS:CT images of 224 GGNs from 210 individuals were collected retrospectively and classified into atypical adenomatous hyperplasia (AAH)/adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) groups. AI was used to identify GGNs and to obtain quantitative parameters, and CT signs were recognized manually. The mixed predictive model based on logistic multivariate regression was built and evaluated. RESULTS:Of the 224 GGNs, 55, 93, and 76 were AAH/AIS, MIA, and IAC, respectively. In terms of AI parameters, from AAH/AIS to MIA, and IAC, there was a gradual increase in two-dimensional mean diameter, three-dimensional mean diameter, mean CT value, maximum CT value, and volume of GGNs (all P<0.0001). Except for the CT signs of the location, and the tumor-lung interface, there were significant differences among the three groups in the density, shape, vacuolar signs, air bronchogram, lobulation, spiculation, pleural indentation, and vascular convergence signs (all P<0.05). The areas under the curve (AUC) of predictive model 1 for identifying the AAH/AIS and MIA and model 2 for identifying MIA and IAC were 0.779 and 0.918, respectively, which were greater than the quantitative parameters independently (all P<0.05). CONCLUSION:AI parameters are valuable for identifying subtypes of early lung adenocarcinoma and have improved diagnostic efficacy when combined with CT signs.
10.1042/BSR20212416
Outcome prediction in resectable lung adenocarcinoma patients: value of CT radiomics.
Choe Jooae,Lee Sang Min,Do Kyung-Hyun,Kim Seonok,Choi Sehoon,Lee June-Goo,Seo Joon Beom
European radiology
OBJECTIVES:Lung adenocarcinoma shows broad spectrum of prognosis and histologic heterogeneity. This study was to investigate the prognostic value of CT radiomics in resectable lung adenocarcinoma patients and assess its incremental value over clinical-pathologic risk factors. METHODS:This retrospective analysis evaluated 1058 patients who underwent curative surgery for lung adenocarcinoma (training cohort: N = 754; temporal validation cohort: N = 304). Radiomics features were extracted from preoperative contrast-enhanced CT. Radiomics signature to predict disease-free survival (DFS) and overall survival (OS) was generated. Association between the radiomics signature and prognosis were evaluated using univariable and multivariable Cox proportional hazards regression analyses. Incremental value of the radiomics signature beyond clinical-pathologic risk factors was assessed using concordance index (C-index). RESULTS:The radiomics signatures were independently associated with DFS (hazard ratio [HR], 1.920; p < 0.001) and OS (HR, 2.079; p < 0.001). The radiomics signature showed performance comparable to stage in estimation of DFS (C-index, 0.724 vs 0.685) and OS (0.735 vs 0.703). The radiomics added prognostic value to clinical-pathologic models (stage and histologic subtype) in predicting DFS (C-index, 0.764 vs 0.713; p < 0.001), which was also shown in the validation cohort (0.782 vs 0.734; p = 0.016). In terms of OS, including radiomics led to significant improvement in prognostic performance of the clinical-pathologic model (stage and age) in the training cohort (0.784 vs 0.737; p < 0.001), but the improvement was not significant in the validation cohort (0.805 vs 0.734; p = 0.149). CONCLUSIONS:CT radiomics was effective in predicting prognosis in lung adenocarcinoma patients, providing additional prognostic information beyond clinical-pathologic risk factors. KEY POINTS:• CT radiomics signature was an independent prognostic factor predicting disease-free and overall survival along with clinical risk factors of lung adenocarcinoma (stage, histologic subtype, and age). • CT radiomics added prognostic value to clinical-pathologic models (stage and subtype) in predicting disease-free survival (C-index for integrated model and clinical-pathologic model, 0.764 vs 0.713; p < 0.001), which was also proven in the validation cohort (0.782 vs 0.734; p = 0.016). • Integrated model incorporating radiomics signature can successfully stratify patients into high-risk, intermediate-, or low-risk groups in patients with resectable lung adenocarcinoma.
10.1007/s00330-020-06872-z
The Growth Rate of Subsolid Lung Adenocarcinoma Nodules at Chest CT.
de Margerie-Mellon Constance,Ngo Long H,Gill Ritu R,Monteiro Filho Antonio C,Heidinger Benedikt H,Onken Allison,Medina Mayra A,VanderLaan Paul A,Bankier Alexander A
Radiology
Background Confirming that subsolid adenocarcinomas show exponential growth is important because it would justify using volume doubling time to assess their growth. Purpose To test whether the growth of lung adenocarcinomas manifesting as subsolid nodules at chest CT is accurately represented by an exponential model. Materials and Methods Patients with lung adenocarcinomas manifesting as subsolid nodules surgically resected between January 2005 and May 2018, with three or more longitudinal CT examinations before resection, were retrospectively included. Overall volume (for all nodules) and solid component volume (for part-solid nodules) were measured over time. A linear mixed-effects model was used to identify the growth pattern (linear, exponential, quadratic, or power law) that best represented growth. The interactions between nodule growth and clinical, CT morphologic, and pathologic parameters were studied. Results Sixty-nine patients (mean age, 70 years ± 9 [standard deviation]; 48 women) with 74 lung adenocarcinomas were evaluated. Overall growth and solid component growth were better represented by an exponential model (adjusted = 0.89 and 0.95, respectively) than by a quadratic model ( = 0.88 and 0.93, respectively), a linear model ( = 0.87 and 0.92, respectively), or a power law model ( = 0.82 and 0.93, respectively). Faster overall volume growth was associated with a history of lung cancer ( < .001), a baseline nodule volume less than 500 mm ( = .03), and histologic findings of invasive adenocarcinoma ( < .001). The median volume doubling time of noninvasive adenocarcinoma was significantly longer than that of invasive adenocarcinoma (939 days [interquartile range, 588-1563 days] vs 678 days [interquartile range, 392-916 days], respectively; = .01). Conclusion The overall volume growth of adenocarcinomas manifesting as subsolid nodules at chest CT was best represented by an exponential model compared with the other tested models. This justifies the use of volume doubling time for the growth assessment of these nodules. © RSNA, 2020 See also the editorial by Kuriyama and Yanagawa in this issue.
10.1148/radiol.2020192322
Histopathologic Basis for a Chest CT Deep Learning Survival Prediction Model in Patients with Lung Adenocarcinoma.
Radiology
Background A preoperative CT-based deep learning (DL) prediction model was proposed to estimate disease-free survival in patients with resected lung adenocarcinoma. However, the black-box nature of DL hinders interpretation of its results. Purpose To provide histopathologic evidence underpinning the DL survival prediction model and to demonstrate the feasibility of the model in identifying patients with histopathologic risk factors through unsupervised clustering and a series of regression analyses. Materials and Methods For this retrospective study, data from patients who underwent curative resection for lung adenocarcinoma without neoadjuvant therapy from January 2016 to September 2020 were collected from a tertiary care center. Seven histopathologic risk factors for the resected adenocarcinoma were documented: the aggressive adenocarcinoma subtype (cribriform, morular, solid, or micropapillary-predominant subtype); mediastinal nodal metastasis (pN2); presence of lymphatic, venous, and perineural invasion; visceral pleural invasion (VPI); and mutation status. Unsupervised clustering using 80 DL model-driven CT features was performed, and associations between the patient clusters and the histopathologic features were analyzed. Multivariable regression analyses were performed to investigate the added value of the DL model output to the semantic CT features (clinical T category and radiologic nodule type [ie, solid or subsolid]) for histopathologic associations. Results A total of 1667 patients (median age, 64 years [IQR, 57-71 years]; 975 women) were evaluated. Unsupervised patient clusters 3 and 4 were associated with all histopathologic risk factors ( < .01) except for mutation status ( = .30 for cluster 3). After multivariable adjustment, model output was associated with the aggressive adenocarcinoma subtype (odds ratio [OR], 1.03; 95% CI: 1.002, 1.05; = .03), venous invasion (OR, 1.03; 95% CI: 1.004, 1.06; = .02), and VPI (OR, 1.08; 95% CI: 1.06, 1.10; < .001), independently of the semantic CT features. Conclusion The deep learning model extracted CT imaging surrogates for the histopathologic profiles of lung adenocarcinoma. © RSNA, 2022 . See also the editorial by Yanagawa in this issue.
10.1148/radiol.213262
Inflammatory Microenvironment in Early Non-Small Cell Lung Cancer: Exploring the Predictive Value of Radiomics.
Cancers
Patient prognosis is a critical consideration in the treatment decision-making process. Conventionally, patient outcome is related to tumor characteristics, the cancer spread, and the patients' conditions. However, unexplained differences in survival time are often observed, even among patients with similar clinical and molecular tumor traits. This study investigated how inflammatory radiomic features can correlate with evidence-based biological analyses to provide translated value in assessing clinical outcomes in patients with NSCLC. We analyzed a group of 15 patients with stage I NSCLC who showed extremely different OS outcomes despite apparently harboring the same tumor characteristics. We thus analyzed the inflammatory levels in their tumor microenvironment (TME) either biologically or radiologically, focusing our attention on the NLRP3 cancer-dependent inflammasome pathway. We determined an NLRP3-dependent peritumoral inflammatory status correlated with the outcome of NSCLC patients, with markedly increased OS in those patients with a low rate of NLRP3 activation. We consistently extracted specific radiomic signatures that perfectly discriminated patients' inflammatory levels and, therefore, their clinical outcomes. We developed and validated a radiomic model unleashing quantitative inflammatory features from CT images with an excellent performance to predict the evolution pattern of NSCLC tumors for a personalized and accelerated patient management in a non-invasive way.
10.3390/cancers14143335
[Research Advances and Obstacles of CT-based Radiomics in Diagnosis and Treatment of Lung Cancer].
Li Jiawei,Li Xiadong,Chen Xueqin,Ma Shenglin
Zhongguo fei ai za zhi = Chinese journal of lung cancer
Radiomics, a technology based on multimodal medical image processing and analysis, is able to extract automatically and analyze massive data from computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography/computed tomography (PET/CT) via high-performance computer algorithm in order to pursue early diagnosis of disease, benign and malignant tumor discrimination, dynamic evaluation of disease treatment, and individualized precision therapy. To date, many studies demonstrate that radiomics not only has great potential in early diagnosis of lung cancer and prediction of genotype, treatment efficacy, as well as prognosis but also is based on imaging methods that are noninvasive, inexpensive, and repeatable. It does demonstrate precious values in guiding the clinical diagnosis and treatment of lung cancer, especially in the personalized and precise treatments and researches of lung cancer. However, the consistency and reproducibility of radiomics and the selection of robust characteristics still warrant further researches.
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10.3779/j.issn.1009-3419.2020.101.36
Lung cancer identification: a review on detection and classification.
Thakur Shailesh Kumar,Singh Dhirendra Pratap,Choudhary Jaytrilok
Cancer metastasis reviews
Lung cancer is one of the most common diseases among humans and one of the major causes of growing mortality. Medical experts believe that diagnosing lung cancer in the early phase can reduce death with the illustration of lung nodule through computed tomography (CT) screening. Examining the vast amount of CT images can reduce the risk. However, the CT scan images incorporate a tremendous amount of information about nodules, and with an increasing number of images make their accurate assessment very challenging tasks for radiologists. Recently, various methods are evolved based on handcraft and learned approach to assist radiologists. In this paper, we reviewed different promising approaches developed in the computer-aided diagnosis (CAD) system to detect and classify the nodule through the analysis of CT images to provide radiologists' assistance and present the comprehensive analysis of different methods.
10.1007/s10555-020-09901-x
A Community-based Pulmonary Nodule Clinic: Improving Lung Cancer Stage at Diagnosis.
Melton Nathaniel,Lazar John F,Moritz Troy A
Cureus
Objective Pulmonary nodules (PNs) are a common incidental finding and are often how lung cancer is discovered. Our goal was to determine if establishing a pulmonary nodule clinic (PNC) in a community healthcare setting would lead to an earlier stage at diagnosis. Methods A single healthcare system retrospective review was conducted of all PNC patients from 2010-2015 diagnosed with lung cancer. The stage at diagnosis was analyzed and compared to lung cancer patients in our healthcare system outside the PNC and to national data. Five-year survival rates for PNC patients from 2010-2012 were also analyzed. Results A total of 119 patients and 127 lung cancers were diagnosed through the PNC from 2010-2015. There were 990 lung cancers, with a known stage, diagnosed outside the PNC in our healthcare system from 2010 to 2015. Two hundred and eighty one (28.4%) cancers were Stage I, compared to 69 (54.3%) (p <0.0001) through the PNC; 110 (11.1%) cancers were diagnosed at Stage II compared to 17 (13.4%) through the PNC (0.4471); 277 (25.7%) cancers were diagnosed at Stage III, compared to 21 (16.5%) through the PNC (p 0.0060); 598 (60.4%) cancers were diagnosed at Stage IV, compared to 20 (15.7%) through the PNC (p <0.0001). Five-year survival rates for patients diagnosed in 2010 were 80% (four of five patients), 79.2% (19/24) in 2011, and 62.2% (23/37) in 2012. Conclusions Lung cancer survival is directly related to the stage at diagnosis. Establishment of our PNC has led to an earlier stage at diagnosis compared to the general lung cancer population in our community.
10.7759/cureus.4226
Solitary pulmonary nodule as the initial manifestation of isolated metastasis from prostate cancer without bone involvement: A case report.
Kosaka Tatsuaki,Iizuka Shuhei,Yoneda Tatsuaki,Otsuki Yoshiro,Nakamura Toru
International journal of surgery case reports
INTRODUCTION:Isolated lung metastases from prostate cancer without any other organ involvement are rare. They are commonly in the form of diffuse or multiple lesions and rarely emerge as a solitary pulmonary nodule. PRESENTATION OF CASE:A 61-year-old man who had undergone a laparoscopic-assisted radical prostatectomy for prostate cancer 16 months prior presented with a growing solitary pulmonary nodule. Positron emission tomography/computed tomography showed an abnormal uptake in the nodule without any other organ involvement. A surgical specimen by a thoracoscopic wedge resection proved a diagnosis of a metastasis from prostate cancer. He is currently alive only with worsening pulmonary metastases at 7 years after the lung surgery. DISCUSSION:A rare entity of isolated pulmonary metastases could be a sole finding of metastatic prostate cancer over the years and its initial manifestation could emerge as a solitary pulmonary nodule. It poses a diagnostic challenge because primary lung cancer is the leading differential diagnosis of solitary pulmonary nodules and is also one of the most frequent second primary malignancies in prostate cancer survivors. CONCLUSION:An aggressive surgical biopsy is essential for definitive histopathological and immunohistochemical analyses of solitary pulmonary nodules to distinguish a rare form of an isolated pulmonary relapse from a second primary lung cancer in prostate cancer survivors.
10.1016/j.ijscr.2021.106681
The determinants of lung cancer after detecting a solitary pulmonary nodule are different in men and women, for both chest radiograph and CT.
Chilet-Rosell Elisa,Parker Lucy A,Hernández-Aguado Ildefonso,Pastor-Valero María,Vilar José,González-Álvarez Isabel,Salinas-Serrano José María,Lorente-Fernández Fermina,Domingo M Luisa,Lumbreras Blanca
PloS one
OBJECTIVES:To determine the factors associated with lung cancer diagnosis and mortality after detecting a solitary pulmonary nodule (SPN) in routine clinical practice, in men and in women for both chest radiograph and CT. MATERIALS AND METHODS:A 5-year follow-up of a retrospective cohort of of 25,422 (12,594 men, 12,827 women) patients aged ≥35 years referred for chest radiograph or CT in two hospitals in Spain (2010-2011). SPN were detected in 893 (546 men, 347 women) patients. We estimated the cumulative incidence of lung cancer at 5-years, the association of patient and nodule characteristics with SPN malignancy using Poisson logistic regression, stratifying by sex and type of imaging test. We calculated lung cancer specific mortality rate by sex and SPN detection and hazard rates by cox regression. RESULTS:133 (14.9%) out of 893 patients with an SPN and 505 (2.06%) of the 24,529 patients without SPN were diagnosed with lung cancer. Median diameter of SPN in women who developed cancer was larger than in men. Men who had a chest radiograph were more likely to develop a lung cancer if the nodule was in the upper-lobes, which was not the case for women. In patients with an SPN, smoking increased the risk of lung cancer among men (chest radiograph: RR = 11.3, 95%CI 1.5-83.3; CT: RR = 7.5, 95%CI 2.2, 26.0) but smoking was not significantly associated with lung cancer diagnosis or mortality among women with an SPN. The relative risk of lung cancer diagnosis in women with SPN versus those without was much higher compared to men (13.7; 95%CI 9.2, 20.4 versus 6.2; 95%CI 4.9,7.9). CONCLUSION:The factors associated with SPN malignancy and 5-year lung cancer mortality were different among men and women, especially regarding smoking history and SPN characteristics, where we observed a relatively high rate of lung cancer diagnosis among female non-smokers.
10.1371/journal.pone.0221134
Vasculature surrounding a nodule: A novel lung cancer biomarker.
Lung cancer (Amsterdam, Netherlands)
PURPOSE:To investigate whether the vessels surrounding a nodule depicted on non-contrast, low-dose computed tomography (LDCT) can discriminate benign and malignant screen detected nodules. MATERIALS AND METHODS:We collected a dataset consisting of LDCT scans acquired on 100 subjects from the Pittsburgh Lung Screening study (PLuSS). Fifty subjects were diagnosed with lung cancer and 50 subjects had suspicious nodules later proven benign. For the lung cancer cases, the location of the malignant nodule in the LDCT scans was known; while for the benign cases, the largest nodule in the LDCT scan was used in the analysis. A computer algorithm was developed to identify surrounding vessels and quantify the number and volume of vessels that were connected or near the nodule. A nonparametric receiver operating characteristic (ROC) analysis was performed based on a single nodule per subject to assess the discriminability of the surrounding vessels to provide a lung cancer diagnosis. Odds ratio (OR) were computed to determine the probability of a nodule being lung cancer based on the vessel features. RESULTS:The areas under the ROC curves (AUCs) for vessel count and vessel volume were 0.722 (95% CI=0.616-0.811, p<0.01) and 0.676 (95% CI=0.565-0.772), respectively. The number of vessels attached to a nodule was significantly higher in the lung cancer group 9.7 (±9.6) compared to the non-lung cancer group 4.0 (±4.3) CONCLUSION: Our preliminary results showed that malignant nodules are often surrounded by more vessels compared to benign nodules, suggesting that the surrounding vessel characteristics could serve as lung cancer biomarker for indeterminate nodules detected during LDCT lung cancer screening using only the information collected during the initial visit.
10.1016/j.lungcan.2017.10.008
Computer-Aided Diagnosis (CAD) of Pulmonary Nodule of Thoracic CT Image Using Transfer Learning.
Journal of digital imaging
Computer-aided diagnosis (CAD) has already been widely used in medical image processing. We recently make another trial to implement convolutional neural network (CNN) on the classification of pulmonary nodules of thoracic CT images. The biggest challenge in medical image classification with the help of CNN is the difficulty of acquiring enough samples, and overfitting is a common problem when there are not enough images for training. Transfer learning has been verified as reasonable in dealing with such problems with an acceptable loss value. We use the classic LeNet-5 model to classify pulmonary nodules of thoracic CT images, including benign and malignant pulmonary nodules, and different malignancies of the malignant nodules. The CT images are obtained from Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) where both pulmonary nodule scanning and nodule annotations are available. These images are labeled and stored in a medical images knowledge base (KB), which is designed and implemented in our previous work. We implement the 10-folder cross validation (CV) to testify the robustness of the classification model we trained. The result demonstrates that the transfer learning of the LeNet-5 is good for classifying pulmonary nodules of thoracic CT images, and the average values of Top-1 accuracy are 97.041% and 96.685% respectively. We believe that our work is beneficial and has potential for practical diagnosis of lung nodules.
10.1007/s10278-019-00204-4
Malignant melanoma without primary, presenting as solitary pulmonary nodule: a case report.
Tsaknis George,Naeem Muhammad,Singh Advitya,Vijayakumar Siddharth
Journal of medical case reports
BACKGROUND:Solitary pulmonary nodules are the most common incidental finding on chest imaging. Their management is very well defined by several guidelines, with risk calculators for lung cancer being the gold standard. Solitary intramuscular metastasis combined with a solitary pulmonary nodule from malignant melanoma without a primary site is rare. CASE PRESENTATION:A 57-year-old white male was referred to our lung cancer service with solitary pulmonary nodule. After positron-emission tomography, we performed an ultrasound-guided core needle biopsy of an intramuscular solitary lesion, not identified on computed tomography scan, and diagnosed metastatic malignant melanoma. The solitary pulmonary nodule was resected and also confirmed metastatic melanoma. There was no primary skin lesion. The patient received oral targeted therapy and is disease-free 5 years later. CONCLUSIONS:Clinicians dealing with solitary pulmonary nodules must remain vigilant for other extrathoracic malignancies even in the absence of obvious past history. Lung metastasectomy may have a role in metastatic malignant melanoma with unknown primary.
10.1186/s13256-021-02933-z
The Effects of Perinodular Features on Solid Lung Nodule Classification.
Journal of digital imaging
Lung cancer is the most lethal malignant neoplasm worldwide, with an annual estimated rate of 1.8 million deaths. Computed tomography has been widely used to diagnose and detect lung cancer, but its diagnosis remains an intricate and challenging work, even for experienced radiologists. Computer-aided diagnosis tools and radiomics tools have provided support to the radiologist's decision, acting as a second opinion. The main focus of these tools has been to analyze the intranodular zone; nevertheless, recent works indicate that the interaction between the nodule and its surroundings (perinodular zone) could be relevant to the diagnosis process. However, only a few works have investigated the importance of specific attributes of the perinodular zone and have shown how important they are in the classification of lung nodules. In this context, the purpose of this work is to evaluate the impact of using the perinodular zone on the characterization of lung lesions. Motivated by reproducible research, we used a large public dataset of solid lung nodule images and extracted fine-tuned radiomic attributes from the perinodular and intranodular zones. Our best-evaluated model obtained an average AUC of 0.916, an accuracy of 84.26%, a sensitivity of 84.45%, and specificity of 83.84%. The combination of attributes from the perinodular and intranodular zones in the image characterization resulted in an improvement in all the metrics analyzed when compared to intranodular-only characterization. Therefore, our results highlighted the importance of using the perinodular zone in the solid pulmonary nodules classification process.
10.1007/s10278-021-00453-2
Utility of liquid-based cytology on residual needle rinses collected from core needle biopsy for lung nodule diagnosis.
Lan Zhihua,Zhang Xiaoli,Ma Xin,Hu Yiyan,Zhang Jing,Yang Fang
Cancer medicine
BACKGROUND:Core needle biopsy (CNB) has become the most common tissue sampling modality for pathological diagnosis of peripheral lung nodules. However, approximately 10% of pulmonary CNB specimens cannot be unambiguously diagnosed, even with auxiliary techniques. This retrospective study investigated the diagnostic value of liquid-based cytology on residual pulmonary CNB material collected from needle rinses. METHODS:Computed tomography-guided pulmonary CNB specimens and relevant cytology of CNB needle rinses (CNR) from July 2017 to June 2020 were reviewed. A total of 406 patients, each of whom underwent a CNB procedure, were included in the study. RESULTS:Of the 406 cases, a more serious diagnosis was rendered by CNR in 6.4% (n = 26) of cases. Furthermore, among these 26 cases, 13 malignancies were confirmed only from CNR. Of the remaining 13 patients with uncertain lesions identified from CNR, six were diagnosed with definite benign lesions from tissue samples, five were found to harbor malignant neoplasms through repeated CNB or follow-up examination, and two had tuberculosis. The sensitivity (320/332, 96.4%) of combined CNR/CNB (both CNR and CNB) in distinguishing malignancies from benign lesions was higher than that of CNB alone (307/332, 92.5%). A total of 320 malignant neoplasms included 198 cases of primary lung adenocarcinoma and 71 cases of primary lung squamous cell carcinoma. CONCLUSIONS:CNR with higher nuclear and cytoplasmic resolution than CNB exhibited a high diagnostic efficacy for differentiating malignant from benign lesions in the lung. Moreover, combined CNR/CNB achieved optimal results in reducing the false-negative rate and the subtyping of non-small cell lung cancer.
10.1002/cam4.3949
The management of hydatidiform mole with lung nodule: a retrospective analysis in 53 patients.
Li Xiao,Xu Yaping,Liu Yuanyuan,Cheng Xiaodong,Wang Xinyu,Lu Weiguo,Xie Xing
Journal of gynecologic oncology
OBJECTIVE:To investigate the significance of lung nodule in hydatidiform mole, we retrospectively compared the clinical outcomes of those patients treated with different strategies. METHODS:The patients were divided into three groups: chemotherapy immediately once lung nodule was detected (group 1, n=17), delayed chemotherapy until human chorionic gonadotrophin (hCG) level met the diagnostic criteria for gestational trophoblastic neoplasia (GTN) (group 2, n=18), and hCG surveillance alone until hCG level was normalized spontaneously (group 3, n=18). The clinical parameters of these patients were collected and analyzed. RESULTS:Totally 53 (4.0%) patients were included from 1,323 cases with molar pregnancy during past 16 years. Among them, the diameters of lung nodules were 0.3-2.5 cm. Chemotherapy cycles for achieving hCG normalization and the failure rate of first-line chemotherapy in group 1 were significantly increased than that in group 2 (5 vs. 3 cycles, p=0.000, 58.8% vs. 11.1%, p=0.005). The hCG level of all 18 cases in group 3 was normalized spontaneously within 6 months. Of those, lung nodules of 9 patients disappeared spontaneously, accounting for 25% (9/36) of patients who initially selected observation. The proportion of single nodule in group 3 was significantly higher than that in group 2 (10/18 vs. 2/18, p=0.012). CONCLUSION:Our results suggest that lung nodule alone is not an adequate indication of chemotherapy in molar pregnancy. hCG surveillance is safe for patients with lung nodule, especially with single nodule, as long as their hCG levels do not meet International Federation of Gynecology and Obstetrics diagnostic criteria for GTN.
10.3802/jgo.2019.30.e16
FDG avid solitary pulmonary nodule mimicking lung cancer.
Alduraibi Alaa Khalid
Radiology case reports
A healthy 49-year-old nonsmoker lady, who was found to have an incidental finding of a lung lesion on a chest X-ray. A Chest CT scan was performed and revealed left upper lobe, 1.5 cm solitary nodule with ground glass borders that highly suspicious for Bronchioloalveolar carcinoma and warranted further investigation to rule out malignancy. The FDG PET and/or CT scan was performed for staging and further evaluation and it displayed avidity of the nodule with a standardized uptake value (SUV) of 6.2, no abnormal uptake elsewhere in the body. CT guided biopsy was arranged and the histopathology result revealed eosinophilic pneumonia.
10.1016/j.radcr.2022.01.038
Spiculation Sign Recognition in a Pulmonary Nodule Based on Spiking Neural P Systems.
Qiu Shi,Sun Jingtao,Zhou Tao,Gao Guilong,He Zhenan,Liang Ting
BioMed research international
The spiculation sign is one of the main signs to distinguish benign and malignant pulmonary nodules. In order to effectively extract the image feature of a pulmonary nodule for the spiculation sign distinguishment, a new spiculation sign recognition model is proposed based on the doctors' diagnosis process of pulmonary nodules. A maximum density projection model is established to fuse the local three-dimensional information into the two-dimensional image. The complete boundary of a pulmonary nodule is extracted by the improved Snake model, which can take full advantage of the parallel calculation of the Spike Neural P Systems to build a new neural network structure. In this paper, our experiments show that the proposed algorithm can accurately extract the boundary of a pulmonary nodule and effectively improve the recognition rate of the spiculation sign.
10.1155/2020/6619076
Defining growth in small pulmonary nodules using volumetry: results from a "coffee-break" CT study and implications for current nodule management guidelines.
Bartlett Emily C,Kemp Samuel V,Rawal Bhavin,Devaraj Anand
European radiology
OBJECTIVES:An increase in lung nodule volume on serial CT may represent true growth or measurement variation. In nodule guidelines, a 25% increase in nodule volume is frequently used to determine that growth has occurred; this is based on previous same-day, test-retest (coffee-break) studies examining metastatic nodules. Whether results from prior studies apply to small non-metastatic nodules is unknown. This study aimed to establish the interscan variability in the volumetric measurements of small-sized non-metastatic nodules. METHODS:Institutional review board approval was obtained for this study. Between March 2019 and January 2021, 45 adults (25 males; mean age 65 years, range 37-84 years) with previously identified pulmonary nodules (30-150 mm) requiring surveillance, without a known primary tumour, underwent two same-day CT scans. Non-calcified solid nodules were measured using commercial volumetry software, and interscan variability of volume measurements was assessed using a Bland-Altman method and limits of agreement. RESULTS:One hundred nodules (range 28-170 mm; mean 81.1 mm) were analysed. The lower and upper limits of agreement for the absolute volume difference between the two scans were - 14.2 mm and 12.0 mm respectively (mean difference 1.09 mm, range - 33-12 mm). The lower and upper limits of agreement for relative volume difference were - 16.4% and 14.6% respectively (mean difference 0.90%, range - 24.1-32.8%). CONCLUSIONS:The interscan volume variability in this cohort of small non-metastatic nodules was smaller than that in previous studies involving lung metastases of varying sizes. An increase of 15% in nodule volume on sequential CT may represent true growth, and closer surveillance of these nodules may be warranted. KEY POINTS:• In current pulmonary nodule management guidelines, a threshold of 25% increase in volume is required to determine that true growth of a pulmonary nodule has occurred. • This test-retest (coffee break) study has demonstrated that a smaller threshold of 15% increase in volume may represent true growth in small non-metastatic nodules. • Closer surveillance of some small nodules growing 15-25% over a short interval may be appropriate.
10.1007/s00330-021-08302-0
Primary pulmonary meningioma presenting as a micro solid nodule: A rare case report.
Xu Kai-Kai,Tian Feng,Cui Yong
Thoracic cancer
An ectopic meningioma, such as a primary pulmonary meningioma (PPM), is a rare type of tumor that primarily originates outside of the central nervous system. A 65-year-old female patient underwent a thoracoscopic lung wedge resection of the right lower lobe for a micro solid nodule detected via computed tomography. The histologic result revealed a PPM. PPMs manifested with micro solid nodules are a very rare occurrence in clinical practice. Increased awareness of the clinical and pathological characteristics of this rare disease can assist thoracic surgical teams to apply adequate management.
10.1111/1759-7714.12639
Efficacy and Safety Analysis of Multislice Spiral CT-Guided Transthoracic Lung Biopsy in the Diagnosis of Pulmonary Nodules of Different Sizes.
Computational and mathematical methods in medicine
Objective:This study is aimed at investigating the efficacy and safety of multislice spiral CT-guided transthoracic lung biopsy in the diagnosis of pulmonary nodules of different sizes. Methods:Data of 78 patients with pulmonary nodules who underwent CT-guided transthoracic lung biopsy in our hospital from January 2020 to December 2021 were retrospectively analyzed, and they were divided into the small nodules group ( = 12), medium nodules group ( = 35), and large nodules group ( = 31) according to the diameter of pulmonary nodules. The results of puncture biopsy and final diagnosis of pulmonary nodules of different sizes were compared. The incidence of complications in patients with pulmonary nodules of different sizes was compared. Univariate analysis was used to compare the incidence of complications in 78 patients. Logistic multiple regression analysis was used to analyze the independent risk factors of pneumothorax in patients with pulmonary nodule puncture. Logistic multiple regression analysis was used to analyze the independent risk factors of pulmonary hemorrhage in patients with pulmonary nodule puncture. Results:The diagnostic accuracy, sensitivity, and specificity were 83.33%, 100.00%, and 77.78% in small nodules group. The diagnostic accuracy, sensitivity, and specificity of medium nodules group were 85.71%, 100.00%, and 73.68%, respectively. The diagnostic accuracy, sensitivity, and specificity of large nodules group were 93.55%, 100.00%, and 33.33%, respectively. There was no significant difference in the incidence of pneumothorax among the three groups ( > 0.05). The incidence of pulmonary hemorrhage in small nodule group was higher than that in the medium nodule group and large nodule group, and the difference was statistically significant ( < 0.05). There was no significant difference in the incidence of total complications among the three groups ( > 0.05). There were statistically significant differences in clinical data such as the needle tract length, the puncture position, and the distance of the puncture needle passing through the lung tissue in patients with or without pneumothorax ( < 0.05). There were statistically significant differences in needle tract length, distance of puncture needle passing through lung tissue, and size of pulmonary nodules in patients with or without pulmonary hemorrhage ( > 0.05). Logistic multivariate analysis showed that needle tract length ≤ 50 mm, lateral decubitus position, and the distance of puncture needle passing through lung tissue ≥ 14 mm were independent risk factors for pneumothorax after puncture in patients with pulmonary nodules ( < 0.05). The needle tract length > 50 mm, the distance of puncture needle passing through lung tissue ≥ 14 mm, and small nodules (pulmonary nodules diameter ≤ 10 mm) were independent risk factors for pulmonary hemorrhage after puncture in patients with pulmonary nodules ( < 0.05). Conclusion:Multislice spiral CT-guided transthoracic lung biopsy is effective in diagnosing pulmonary nodules of different sizes.
10.1155/2022/8192832
Age, comorbidity, life expectancy, and pulmonary nodule follow-up in older veterans.
Wong Melisa L,Shi Ying,Fung Kathy Z,Ngo Sarah,Elicker Brett M,Brown James K,Hiatt Robert A,Tang Victoria L,Walter Louise C
PloS one
BACKGROUND:Pulmonary nodule guidelines do not indicate how to individualize follow-up according to comorbidity or life expectancy. OBJECTIVES:To characterize comorbidity and life expectancy in older veterans with incidental, symptom-detected, or screen-detected nodules in 2008-09 compared to 2013-14. To determine the impact of these patient factors on four-year nodule follow-up among the 2008-09 subgroup. DESIGN:Retrospective cohort study. SETTING:Urban Veterans Affairs Medical Center. PARTICIPANTS:243 veterans age ≥65 with newly diagnosed pulmonary nodules in 2008-09 (followed for four years through 2012 or 2013) and 446 older veterans diagnosed in 2013-14. MEASUREMENTS:The primary outcome was receipt of any follow-up nodule imaging and/or biopsy within four years after nodule diagnosis. Primary predictor variables included age, Charlson-Deyo Comorbidity Index (CCI), and life expectancy. Favorable life expectancy was defined as age 65-74 with CCI 0 while limited life expectancy was defined as age ≥85 with CCI ≥1 or age ≥65 with CCI ≥4. Interaction by nodule size was also examined. RESULTS:From 2008-09 to 2013-14, the number of older veterans diagnosed with new pulmonary nodules almost doubled, including among those with severe comorbidity and limited life expectancy. Overall among the 2008-09 subgroup, receipt of nodule follow-up decreased with increasing comorbidity (CCI ≥4 versus 0: adjusted RR 0.61, 95% CI 0.39-0.95) with a trend towards decreased follow-up among those with limited life expectancy (adjusted RR 0.69, 95% CI 0.48-1.01). However, we detected an interaction effect with nodule size such that comorbidity and life expectancy were associated with decreased follow-up only among those with nodules ≤6 mm. CONCLUSIONS:We found some individualization of pulmonary nodule follow-up according to comorbidity and life expectancy in older veterans with smaller nodules only. As increased imaging detects nodules in sicker patients, guidelines need to be more explicit about how to best incorporate comorbidity and life expectancy to maximize benefits and minimize harms for patients with nodules of all sizes.
10.1371/journal.pone.0200496
Pulmonary Sclerosing Pneumocytoma: An Essential Differential Diagnosis for a Lung Nodule.
Manickam Rajapriya,Mechineni Ashesha
Cureus
Pulmonary sclerosing pneumocytoma, previously known as pulmonary sclerosing hemangioma, is a rare benign lung tumor with a low prevalence. We present this condition in a 26-year-old, young, non-smoking female with a slow-growing pulmonary nodule incidentally noted on imaging. Serial computed tomography(CT) scans revealed slow growth, and invasive testing was recommended. The patient underwent a left lateral thoracotomy and based on frozen section findings. A left lower lobectomy was performed. The final pathological diagnosis revealed sclerosing pneumocytoma. This is an atypical patient demographic considering the propensity of the disease for middle-aged Asian women. The case presentation and work-up highlight this critical differential diagnosis for incidental pulmonary nodules increasingly being noted due to widespread use of imaging for screening and routine care in the current medical climate. There are no specific imaging criteria to diagnose this condition. The final diagnosis is made only after surgical biopsy and histopathology. No additional treatment is needed following the diagnosis.
10.7759/cureus.21081
Validation of a deep learning computer aided system for CT based lung nodule detection, classification, and growth rate estimation in a routine clinical population.
PloS one
OBJECTIVE:In this study, we evaluated a commercially available computer assisted diagnosis system (CAD). The deep learning algorithm of the CAD was trained with a lung cancer screening cohort and developed for detection, classification, quantification, and growth of actionable pulmonary nodules on chest CT scans. Here, we evaluated the CAD in a retrospective cohort of a routine clinical population. MATERIALS AND METHODS:In total, a number of 337 scans of 314 different subjects with reported nodules of 3-30 mm in size were included into the evaluation. Two independent thoracic radiologists alternately reviewed scans with or without CAD assistance to detect, classify, segment, and register pulmonary nodules. A third, more experienced, radiologist served as an adjudicator. In addition, the cohort was analyzed by the CAD alone. The study cohort was divided into five different groups: 1) 178 CT studies without reported pulmonary nodules, 2) 95 studies with 1-10 pulmonary nodules, 23 studies from the same patients with 3) baseline and 4) follow-up studies, and 5) 18 CT studies with subsolid nodules. A reference standard for nodules was based on majority consensus with the third thoracic radiologist as required. Sensitivity, false positive (FP) rate and Dice inter-reader coefficient were calculated. RESULTS:After analysis of 470 pulmonary nodules, the sensitivity readings for radiologists without CAD and radiologist with CAD, were 71.9% (95% CI: 66.0%, 77.0%) and 80.3% (95% CI: 75.2%, 85.0%) (p < 0.01), with average FP rate of 0.11 and 0.16 per CT scan, respectively. Accuracy and kappa of CAD for classifying solid vs sub-solid nodules was 94.2% and 0.77, respectively. Average inter-reader Dice coefficient for nodule segmentation was 0.83 (95% CI: 0.39, 0.96) and 0.86 (95% CI: 0.51, 0.95) for CAD versus readers. Mean growth percentage discrepancy of readers and CAD alone was 1.30 (95% CI: 1.02, 2.21) and 1.35 (95% CI: 1.01, 4.99), respectively. CONCLUSION:The applied CAD significantly increased radiologist's detection of actionable nodules yet also minimally increasing the false positive rate. The CAD can automatically classify and quantify nodules and calculate nodule growth rate in a cohort of a routine clinical population. Results suggest this Deep Learning software has the potential to assist chest radiologists in the tasks of pulmonary nodule detection and management within their routine clinical practice.
10.1371/journal.pone.0266799
Pulmonary arteriovenous malformation and inherent complications with solitary lung nodule biopsy-literature overview and case report.
Radiology case reports
Pulmonary arteriovenous malformation, also known as an arteriovenous fistula, is typically a congenital disease caused by structural deficiencies, particularly the lack of capillary wall development, leading to the abnormal dilation of the pulmonary capillaries. The majority of pulmonary arteriovenous malformation cases are associated with Rendu-Osler-Weber syndrome, also known as hereditary hemorrhagic telangiectasia. Pulmonary arteriovenous malformation rarely occurs due to chest trauma. Pulmonary arteriovenous malformations are long-lasting and often first diagnosed in adults. More than two-thirds of pulmonary arteriovenous malformation lesions are found in the lower lung lobe and the subpleural area, and the vast majority of cases present with the monofocal form. The initial diagnosis is often based on the identification of a solitary pulmonary nodule. However, a solitary nodule detected on chest computed tomography that is not correctly diagnosed as pulmonary arteriovenous malformation, even after intravenous contrast injection, can lead to the performance of a transthoracic biopsy. Biopsy of pulmonary arteriovenous malformations can lead to stroke occurrence, during which the patient often presents with severe pleural bleeding, which can have lifelong consequences if not immediately treated. We report a case of pulmonary arteriovenous malformation that was discovered incidentally in an adult patient who underwent non-contrast computed tomography. Misdiagnosis occurred, and transthoracic lung biopsy was performed. Complications were discovered late, and the patient underwent surgical pulmonary arteriovenous malformation removal and was treated for hemothorax.
10.1016/j.radcr.2022.04.003
Deep convolutional neural networks for multiplanar lung nodule detection: Improvement in small nodule identification.
Medical physics
PURPOSE:Early detection of lung cancer is of importance since it can increase patients' chances of survival. To detect nodules accurately during screening, radiologists would commonly take the axial, coronal, and sagittal planes into account, rather than solely the axial plane in clinical evaluation. Inspired by clinical work, the paper aims to develop an accurate deep learning framework for nodule detection by a combination of multiple planes. METHODS:The nodule detection system is designed in two stages, multiplanar nodule candidate detection, multiscale false positive (FP) reduction. At the first stage, a deeply supervised encoder-decoder network is trained by axial, coronal, and sagittal slices for the candidate detection task. All possible nodule candidates from the three different planes are merged. To further refine results, a three-dimensional multiscale dense convolutional neural network that extracts multiscale contextual information is applied to remove non-nodules. In the public LIDC-IDRI dataset, 888 computed tomography scans with 1186 nodules accepted by at least three of four radiologists are selected to train and evaluate our proposed system via a tenfold cross-validation scheme. The free-response receiver operating characteristic curve is used for performance assessment. RESULTS:The proposed system achieves a sensitivity of 94.2% with 1.0 FP/scan and a sensitivity of 96.0% with 2.0 FPs/scan. Although it is difficult to detect small nodules (i.e., <6 mm), our designed CAD system reaches a sensitivity of 93.4% (95.0%) of these small nodules at an overall FP rate of 1.0 (2.0) FPs/scan. At the nodule candidate detection stage, results show that the system with a multiplanar method is capable to detect more nodules compared to using a single plane. CONCLUSION:Our approach achieves good performance not only for small nodules but also for large lesions on this dataset. This demonstrates the effectiveness of our developed CAD system for lung nodule detection.
10.1002/mp.14648
Coexistence of nontuberculous mycobacterium and IgG4-related disease in a solitary pulmonary nodule: A case report.
Medicine
RATIONALE:Immunoglobulin G4-related disease (IgG4-RD) is regarded as an immune-mediated systemic fibroinflammatory disease. Several studies have linked IgG4-RD to infections such as tuberculosis and actinomycosis. However, the coexistence of IgG4-RD and non-tuberculous mycobacterium (NTM) in a single pulmonary nodule has not been reported yet. PATIENT CONCERNS:A 76-year-old male patient presented with cough and sputum. A solitary pulmonary nodule suspicious of lung cancer was found on chest CT. DIAGNOSIS:Through video-assisted thoracoscopic biopsy, a diagnosis of co-existing NTM and IgG4-RD in a single nodule was made. INTERVENTIONS:Antibiotic treatment was applied for pneumonia developed after surgery. The patient was also supported by extracorporeal membrane oxygenation and mechanical ventilation since his pneumonia was refractory to medical treatment. OUTCOMES:The patient expired on the 60th postoperative day due to multiple organ failure. LESSONS:IgG4-RD can occur singularly or accompanied by other diseases. We report a solitary pulmonary nodule caused by NTM and concurrent IgG4-RD, suggesting a possible association between these 2 entities. Immunologic relations between IgG4-RD and accompanying infection should be further investigated.
10.1097/MD.0000000000018179
Lung Nodule Detection from Feature Engineering to Deep Learning in Thoracic CT Images: a Comprehensive Review.
Halder Amitava,Dey Debangshu,Sadhu Anup K
Journal of digital imaging
This paper presents a systematic review of the literature focused on the lung nodule detection in chest computed tomography (CT) images. Manual detection of lung nodules by the radiologist is a sequential and time-consuming process. The detection is subjective and depends on the radiologist's experiences. Owing to the variation in shapes and appearances of a lung nodule, it is very difficult to identify the proper location of the nodule from a huge number of slices generated by the CT scanner. Small nodules (< 10 mm in diameter) may be missed by this manual detection process. Therefore, computer-aided diagnosis (CAD) system acts as a "second opinion" for the radiologists, by making final decision quickly with higher accuracy and greater confidence. The goal of this survey work is to present the current state of the artworks and their progress towards lung nodule detection to the researchers and readers in this domain. This review paper has covered the published works from 2009 to April 2018. Different nodule detection approaches are described elaborately in this work. Recently, it is observed that deep learning (DL)-based approaches are applied extensively for nodule detection and characterization. Therefore, emphasis has been given to convolutional neural network (CNN)-based DL approaches by describing different CNN-based networks.
10.1007/s10278-020-00320-6
Quantification of Perinodular Emphysema in High-risk Patients Offers No Benefit in Lung Nodule Risk-Stratification of Malignancy Potential.
Amundson William H,Swanson Eric J,Petersen Ashley,Bell Brian J,Hatt Charles,Wendt Chris H
Journal of thoracic imaging
PURPOSE:Pulmonary nodules, found either incidentally or on lung cancer screening, are common. Evaluating the benign or malignant nature of these nodules is costly in terms of patient risk and expense. The presence of both global and regional emphysema has been linked to increased lung cancer risk. We sought to determine whether the measurement of emphysema directly adjacent to a lung nodule could inform the likelihood of a nodule being malignant. MATERIALS AND METHODS:Within a population of Veterans at high risk for lung cancer, 58 subjects with malignant nodules found on computerized tomographic chest scans were matched by lobe and nodule size to 58 controls. Lung densitometry was measured via determination of the low attenuation area percentage at -950 Hounsfield units (LAA950) and the Hounsfield unit (HU) value at which 15% of lung voxels have a lower lung density (Perc15), at predefined lung volumes that encompassed the nodule to evaluate both perinodular and regional lung fields. The association between measured lung density and malignancy was investigated using conditional logistic regression models, with densitometry measurements used as the primary predictor, adjusting for age alone, or age and computerized tomographic scan characteristics. RESULTS:No significant differences in emphysema measurements between malignant and benign nodules were identified at lung volumes encompassing both perinodular and regional emphysema. Furthermore, emphysema quantification remained stable across lung volumes within individuals. CONCLUSIONS:In this study, quantifying the degree of perinodular or regional emphysema did not offer any benefit in the risk stratification of lung nodules.
10.1097/RTI.0000000000000465
Development and Validation of a Radiomics Nomogram for Differentiating Pulmonary Cryptococcosis and Lung Adenocarcinoma in Solitary Pulmonary Solid Nodule.
Frontiers in oncology
OBJECTIVE:To establish a CT-based radiomics nomogram model for classifying pulmonary cryptococcosis (PC) and lung adenocarcinoma (LAC) in patients with a solitary pulmonary solid nodule (SPSN) and assess its differentiation ability. MATERIALS AND METHODS:A total of 213 patients with PC and 213 cases of LAC (matched based on age and gender) were recruited into this retrospective research with their clinical characteristics and radiological features. High-dimensional radiomics features were acquired from each mask delineated by radiologists manually. We adopted the max-relevance and min-redundancy (mRMR) approach to filter the redundant features and retained the relevant features at first. Then, we used the least absolute shrinkage and operator (LASSO) algorithms as an analysis tool to calculate the coefficients of features and remove the low-weight features. After multivariable logistic regression analysis, a radiomics nomogram model was constructed with clinical characteristics, radiological signs, and radiomics score. We calculated the performance assessment parameters, such as sensitivity, specificity, accuracy, negative predictive value (NPV), and positive predictive value (PPV), in various models. The receiver operating characteristic (ROC) curve analysis and the decision curve analysis (DCA) were drawn to visualize the diagnostic ability and the clinical benefit. RESULTS:We extracted 1,130 radiomics features from each CT image. The 24 most significant radiomics features in distinguishing PC and LAC were retained, and the radiomics signature was constructed through a three-step feature selection process. Three factors-maximum diameter, lobulation, and pleural retraction-were still statistically significant in multivariate analysis and incorporated into a combined model with radiomics signature to develop the predictive nomogram, which showed excellent classification ability. The area under curve (AUC) yielded 0.91 (sensitivity, 80%; specificity, 83%; accuracy, 82%; NPV, 80%; PPV, 83%) and 0.89 (sensitivity, 81%; specificity, 83%; accuracy, 82%; NPV, 81%; PPV, 82%) in training and test cohorts, respectively. The net reclassification indexes (NRIs) were greater than zero ( < 0.05). The Delong test showed a significant difference ( < 0.0001) between the AUCs from the clinical model and the nomogram. CONCLUSIONS:The radiomics technology can preoperatively differentiate PC and lung adenocarcinoma. The nomogram-integrated CT findings and radiomics feature can provide more clinical benefits in solitary pulmonary solid nodule diagnosis.
10.3389/fonc.2021.759840
Deep Deconvolutional Residual Network Based Automatic Lung Nodule Segmentation.
Singadkar Ganesh,Mahajan Abhishek,Thakur Meenakshi,Talbar Sanjay
Journal of digital imaging
Accurate and automatic lung nodule segmentation is of prime importance for the lung cancer analysis and its fundamental step in computer-aided diagnosis (CAD) systems. However, various types of nodule and visual similarity with its surrounding chest region make it challenging to develop lung nodule segmentation algorithm. In this paper, we proposed the Deep Deconvolutional Residual Network (DDRN) based approach for the lung nodule segmentation from the CT images. Our approach is based on two key insights. Proposed deep deconvolutional residual network trained end to end and captures the diverse variety of the nodules from the 2D set of the CT images. Summation-based long skip connection from convolutional to deconvolutional part of the network preserves the spatial information lost during the pooling operation and captures the full resolution features. The proposed method is evaluated on the publicly available Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI) dataset. Results indicate that our proposed method can successfully segment nodules and achieve the average Dice scores of 94.97%, and Jaccard index of 88.68%.
10.1007/s10278-019-00301-4
Amyloid nodule and primary pulmonary lymphoma in the same lung: Radiologic-pathologic correlation of a rare combination.
Jipa Andrei,Glaab Jonathan
Radiology case reports
A 61-year-old man presented for lung cancer screening with low dose CT. A spiculated right apical nodule suspicious for primary lung malignancy and an indeterminate small basilar consolidation were identified. PET/CT was notable for increased FDG uptake in the basilar consolidation. Transthoracic needle biopsy of both lesions was performed which lead to pathologic diagnoses of pulmonary amyloid nodule for the apical nodule and pulmonary extramarginal zone lymphoma of the mucosa associate lymphoid tissue for the basilar consolidation. While incidental findings are common in lung cancer screening CT, exceedingly rare diagnoses or combinations or diagnoses may also be encountered. This case also underscores the value of pathologic diagnosis in cases of indeterminate lung nodules.
10.1016/j.radcr.2020.04.016
Isolated Solitary Lung Nodule in a Patient With Idiopathic Pulmonary Fibrosis and Concomitant Prostate Cancer: A Challenging Diagnosis.
Tarabaih Mohamad,Degheili Jad A,Nasser Mouhamad
Cureus
Prostate cancer is the most commonly diagnosed malignancy and the second most common cause of death in men after lung cancer. Isolated pulmonary metastasis from prostate cancer, without bone or lymph node involvement, is rare and accounts for less than 1% of cases. The diagnosis of solitary lung metastasis is even more challenging in patients with concomitant pulmonary disease and often mandates tissue biopsy from the lung nodule. We herein present a case of an elderly man with idiopathic pulmonary fibrosis who presented with a solitary lung nodule three years after a laparoscopic radical prostatectomy for localized prostate cancer. Initially thought as a primary lung lesion secondary to his pulmonary fibrosis, further workup and ultimately a lung segmentectomy proved a metastatic prostatic adenocarcinoma. The serum prostatic specific antigen dropped to nadir following resection, and he remained stable six months later.
10.7759/cureus.14218
Incidence of Radiation Therapy Among Patients Enrolled in a Multidisciplinary Pulmonary Nodule and Lung Cancer Screening Clinic.
JAMA network open
Importance:The number of pulmonary nodules discovered incidentally or through screening programs has increased markedly. Multidisciplinary review and management are recommended, but the involvement of radiation oncologists in this context has not been defined. Objective:To assess the role of stereotactic body radiation therapy among patients enrolled in a lung cancer screening program. Design, Setting, and Participants:This prospective cohort study was performed at a pulmonary nodule and lung cancer screening clinic from October 1, 2012, to September 31, 2019. Referrals were based on chest computed tomography with Lung Imaging Reporting and Data System category 4 finding or an incidental nodule 6 mm or larger. A multidisciplinary team of practitioners from radiology, thoracic surgery, pulmonology, medical oncology, and radiation oncology reviewed all nodules and coordinated workup and treatment as indicated. Exposures:Patients referred to the pulmonary nodule and lung cancer screening clinic with an incidental or screen-detected pulmonary nodule. Main Outcomes and Measures:The primary outcome was the proportion of patients undergoing therapeutic intervention with radiation therapy, stratified by the route of detection of their pulmonary nodules (incidental vs screen detected). Secondary outcomes were 2-year local control and metastasis-free survival. Results:Among 1150 total patients (median [IQR] age, 66.5 [59.3-73.7] years; 665 [57.8%] female; 1024 [89.0%] non-Hispanic White; 841 [73.1%] current or former smokers), 234 (20.3%) presented with screen-detected nodules and 916 (79.7%) with incidental nodules. For patients with screen-detected nodules requiring treatment, 41 (17.5%) received treatment, with 31 (75.6%) undergoing surgery and 10 (24.4%) receiving radiation therapy. Patients treated with radiation therapy were older (median [IQR] age, 73.8 [67.1 to 82.1] vs 67.6 [61.0 to 72.9] years; P < .001) and more likely to have history of tobacco use (67 [95.7%] vs 128 [76.6%]; P = .001) than those treated with surgery. Fifty-eight patients treated with radiation therapy (82.9%) were considered high risk for biopsy, and treatment recommendations were based on a clinical diagnosis of lung cancer after multidisciplinary review. All screened patients who received radiation therapy had stage I disease and were treated with stereotactic body radiation therapy. For all patients receiving stereotactic body radiation therapy, 2-year local control was 96.3% (95% CI, 91.1%-100%) and metastasis-free survival was 94.2% (95% CI, 87.7%-100%). Conclusions and Relevance:In this unique prospective cohort, 1 in 4 patients with screen-detected pulmonary nodules requiring intervention were treated with stereotactic body radiation therapy. This finding highlights the role of radiation therapy in a lung cancer screening population and the importance of including radiation oncologists in the multidisciplinary management of pulmonary nodules.
10.1001/jamanetworkopen.2022.4840
Computed Tomography Image under Convolutional Neural Network Deep Learning Algorithm in Pulmonary Nodule Detection and Lung Function Examination.
Zhang Chan,Li Jing,Huang Jian,Wu Shangjie
Journal of healthcare engineering
The objective of this study was to perform segmentation and extraction of CT images of pulmonary nodules based on convolutional neural networks (CNNs). The Mask-RCNN algorithm model is a typical end-to-end image segmentation model, which uses the R-FCN structure for nodule detection. The effect of applying the two algorithm models to the computed tomography (CT) diagnosis of pulmonary nodules was analyzed, and different indexes of pulmonary nodule CT images in lung function examination after algorithm optimization were compared. A total of 56 patients diagnosed with pulmonary nodules by surgery or puncture were taken as the research objects. Based on the Mask-RCNN algorithm, a model for CT image segmentation processing of pulmonary nodules was proposed. Subsequently, the 3D Faster-RCNN model was used to label the nodules in the pulmonary nodules. The experimental results showed that the trained Mask-RCNN algorithm model can effectively complete the segmentation task of lung CT images, but there was a little jitter at the boundary. The speed of R-FCN algorithm for nodular detection was 0.172 seconds/picture, and the accuracy was 88.9%. CT scans were performed on the 56 patients based on a deep learning algorithm. The results showed that 30 cases of malignant pulmonary nodules were confirmed, and the diagnostic accuracy was 93.75%. There were 22 benign lesions, the diagnostic accuracy was 91.67%, and the overall diagnostic accuracy was 92.85%. This study effectively improved the diagnostic efficiency of CT images of pulmonary nodules, and the accuracy of CT images in the diagnosis of pulmonary nodules was analyzed and evaluated. It provided theoretical support for the follow-up diagnosis of pulmonary nodules and the treatment of lung cancer. It also significantly improved the diagnostic effect and detection efficiency of pulmonary nodules.
10.1155/2021/3417285
Clinical Utility of Circulating Tumor Cells in the Early Detection of Lung Cancer in Patients with a Solitary Pulmonary Nodule.
Zhong Manhua,Zhang Yi,Pan Zuguang,Wang Wei,Zhang Yuxin,Weng Yuqing,Huang Haile,He Yanju,Liu Ouqi
Technology in cancer research & treatment
Lung cancer is the most common cancer and can appear as a solitary pulmonary nodule. Early detection of lung cancer in this patient population would be beneficial for the disease management. In this study, the potential application of circulating tumor cells (CTCs) on early detection of lung cancer in this population was investigated. The number of CTCs in bronchoalveolar lavage fluid and serum levels of tumor-related markers, cancer antigen 125 (CA125), carcinoembryonic antigen (CEA) and neuron-specific enolase (NSE) were measured in patients with a solitary pulmonary nodule. The association between CTCs and lung cancer was examined. The diagnosis performances of CTCs and selected tumor-related markers were compared. The CTC positivity was significantly associated with lung cancer ( = .009). The sensitivity of CTCs and CA125, CEA, NSE, and CA125/CEA/NSE was 75%, 5.6%, 0%, 25%, and 33%, respectively. The sensitivity of CTCs was improved from 75% to 83% by the combination with CA125 or NSE. CTCs may be helpful for the early detection of lung cancer in patients with a solitary pulmonary nodule.
10.1177/15330338211041465
Multivariate Analysis on Development of Lung Adenocarcinoma Lesion from Solitary Pulmonary Nodule.
Contrast media & molecular imaging
Objective:To analyze multiple factors developing lung adenocarcinoma lesion from solitary pulmonary nodule (SPN). Methods:A total of 70 patients diagnosed with lung adenocarcinoma after finding SPN by chest CT and treated in our hospital (01, 2018-01, 2021) were selected as the malignant lesion group, and another 70 patients diagnosed with benign lesion after finding SPN by CT in the same period were included in the benign lesion group. All patients had complete medical records. With univariate analysis and multivariate logistic regression, the independent risk factors for developing lung adenocarcinoma lesions from SPN were analyzed. Results:By conducting univariate analysis of patients' general information (age, course of disease, BMI, nodule diameter, and gender), smoking status (smoking history and number of cigarettes smoked per year), medical history (family history of lung cancer, history of extrapulmonary malignant tumor, and history of autoimmune diseases), basic complications (hypertension and diabetes), and laboratory examinations (CEA, NSE, CYFRA21-1, SCC-Ag, and CA125), it was concluded that age, course of disease, nodule diameter, CEA positive, CYFRA21-1 positive, and CA125 positive were significantly different between the two groups ( < 0.05); the logistic regression results showed that high age, increased nodule diameter, and CYFRA21-1 positive were the independent risk factors developing lung adenocarcinoma from SPN ( < 0.05). Conclusion:In patients with SPN, higher age, longer course of disease, greater nodule diameter, and CYFRA21-1 positive imply increased risk for triggering lung adenocarcinoma lesion. Therefore, high attention should be paid in the clinic to such SPN patients for early diagnosis and treatment.
10.1155/2022/8330111
Quantitative Pectoralis Muscle Area is Associated with the Development of Lung Cancer in a Large Lung Cancer Screening Cohort.
Gazourian Lee,Durgana Chantal S,Huntley Devon,Rizzo Giulia S,Thedinger William B,Regis Shawn M,Price Lori Lyn,Pagura Elizabeth J,Lamb Carla,Rieger-Christ Kimberly,Thomson Carey C,Stefanescu Cristina F,Sanayei Ava,Long William P,McKee Andrea B,Washko George R,Estépar Raul San José,Wald Christoph,Liesching Timothy N,McKee Brady J
Lung
BACKGROUND:Studies have demonstrated an inverse relationship between body mass index (BMI) and the risk of developing lung cancer. We conducted a retrospective cohort study evaluating baseline quantitative computed tomography (CT) measurements of body composition, specifically muscle and fat area in a large CT lung screening cohort (CTLS). We hypothesized that quantitative measurements of baseline body composition may aid in risk stratification for lung cancer. METHODS:Patients who underwent baseline CTLS between January 1st, 2012 and September 30th, 2014 and who had an in-network primary care physician were included. All patients met NCCN Guidelines eligibility criteria for CTLS. Quantitative measurements of pectoralis muscle area (PMA) and subcutaneous fat area (SFA) were performed on a single axial slice of the CT above the aortic arch with the Chest Imaging Platform Workstation software. Cox multivariable proportional hazards model for cancer was adjusted for variables with a univariate p < 0.2. Data were dichotomized by sex and then combined to account for baseline differences between sexes. RESULTS:One thousand six hundred and ninety six patients were included in this study. A total of 79 (4.7%) patients developed lung cancer. There was an association between the 25th percentile of PMA and the development of lung cancer [HR 1.71 (1.07, 2.75), p < 0.025] after adjusting for age, BMI, qualitative emphysema, qualitative coronary artery calcification, and baseline Lung-RADS® score. CONCLUSIONS:Quantitative assessment of PMA on baseline CTLS was associated with the development of lung cancer. Quantitative PMA has the potential to be incorporated as a variable in future lung cancer risk models.
10.1007/s00408-020-00388-5