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[Value of CT Features on Differential Diagnosis of Pulmonary Subsolid Nodules and Degree of invasion Prediction in Pulmonary Adenocarcinoma]. Guo Fangfang,Li Xinling,Wang Xinyue,Zheng Wensong,Wang Qing,Song Wenjing,Yu Tielian,Fan Yaguang,Wang Ying Zhongguo fei ai za zhi = Chinese journal of lung cancer BACKGROUND:Subsolid pulmonary nodules are common computed tomography (CT) findings of primary lung adenocarcinoma. It is of clinical value to determine the clinical treatment strategies based on CT features. The aim of this study is to find the valuable CT characteristics on differential diagnosis and the degree of invasion prediction by a retrospectively analysis of three groups subsolid nodules, including benign, and invasive adenocarcinoma. METHODS:The CT findings of 106 cases of resected sub-solid nodules were retrospectively analyzed. The nodules were firstly divided into benign and malignant groups and the malignant group was further divided into non/micro-invasive group (atypical adenomatous hyperplasia/adenocarcinoma in situ/minimally invasive adenocarcinoma) and invasive adenocarcinoma group. The nodule size, proportion of solid components, tumor-lung interface, shape, margin, pleural traction, air bronchus sign, vascular abnormalities inside the nodule were evaluated. The univariate analysis (χ2 test, non-parametric test Mann-Whitney U test) was performed to screen statistically significant variables and then enrolled in further multivariate Logistic regression analysis. RESULTS:Multivariate logistic regression analysis showed that a clear tumor-lung interface, air bronchus sign, and pulmonary vascular abnormalities were important indicators of malignant nodules with hazard ratios of 38.1 (95%CI: 5.0-287.7; P<0.01), 7.9 (95%CI: 1.3-49.3; P=0.03), 7.2 (95%CI: 1.4-37.0; P=0.02), respectively. The proportion of solid components was the only significant indicator for identifying invasive adenocarcinoma from AAH/AIS/MIA , with a risk ratio of 1.04 (95%CI: 1.01-1.06, P=0.01). CONCLUSIONS:SSNs with clear tumor-lung interface, air bronchus sign, and pulmonary vascular abnormality inside nodule are more likely to be malignant. A higher percentage of solid components indicates a higher likelihood to be an invasive lesion in malignant SPNs. 10.3779/j.issn.1009-3419.2018.06.05
Computer-aided Volumetry of Part-Solid Lung Cancers by Using CT: Solid Component Size Predicts Prognosis. Kamiya Shinichiro,Iwano Shingo,Umakoshi Hiroyasu,Ito Rintaro,Shimamoto Hironori,Nakamura Shota,Naganawa Shinji Radiology Purpose To investigate the relationship between the postoperative prognosis of patients with part-solid non-small cell lung cancer and the solid component size acquired by using three-dimensional (3D) volumetry software on multidetector computed tomographic (CT) images. Materials and Methods A retrospective study by using preoperative multidetector CT data with 0.5-mm section thickness, clinical records, and pathologic reports of 96 patients with primary subsolid non-small cell lung cancer (47 men and 49 women; mean age ± standard deviation, 66 years ± 8) were reviewed. Two radiologists measured the two-dimensional (2D) maximal solid size of each nodule on an axial image (hereafter, 2D MSSA), the 3D maximal solid size on multiplanar reconstructed images (hereafter, 3D MSSMPR), and the 3D solid volume of greater than 0 HU (hereafter, 3D SV) within each nodule. The correlations between the postoperative recurrence and the effects of clinical and pathologic characteristics, 2D MSSA, 3D MSSMPR, and 3D SV as prognostic imaging biomarkers were assessed by using a Cox proportional hazards model. Results For the prediction of postoperative recurrence, the area under the receiver operating characteristics curve was 0.796 (95% confidence interval: 0.692, 0.900) for 2D MSSA, 0.776 (95% confidence interval: 0.667, 0.886) for 3D MSSMPR, and 0.835 (95% confidence interval: 0.749, 0.922) for 3D SV. The optimal cutoff value for 3D SV for predicting tumor recurrence was 0.54 cm, with a sensitivity of 0.933 (95% confidence interval: 0.679, 0.998) and a specificity of 0.716 (95% confidence interval: 0.605, 0.811) for the recurrence. Significant predictive factors for disease-free survival were 3D SV greater than or equal to 0.54 cm (hazard ratio, 6.61; P = .001) and lymphatic and/or vascular invasion derived from histopathologic analysis (hazard ratio, 2.96; P = .040). Conclusion The measurement of 3D SV predicted the postoperative prognosis of patients with part-solid lung cancer more accurately than did 2D MSSA and 3D MSSMPR. RSNA, 2018. 10.1148/radiol.2018172319
2D and 3D texture analysis to predict lymphovascular invasion in lung adenocarcinoma. Yang Guangjie,Nie Pei,Zhao Lianzi,Guo Jian,Xue Wei,Yan Lei,Cui Jingjing,Wang Zhenguang European journal of radiology PURPOSE:Lymphovascular invasion (LVI) impairs surgical outcomes in lung adenocarcinoma (LAC) patients. Preoperative prediction of LVI is challenging by using traditional clinical and imaging factors. The purpose of this study was to evaluate the value of two-dimensional (2D) and three-dimensional (3D) CT texture analysis (CTTA) in predicting LVI in LAC. METHODS:A total of 149 LAC patients (50 LVI-present LACs and 99 LVI-absent LACs) were retrospectively enrolled. Clinical data and CT findings were analyzed to select independent clinical predictors. Texture features were extracted from 2D and 3D regions of interest (ROI) in 1.25 mm slice CT images. The 2D and 3D CTTA signatures were constructed with the least absolute shrinkage and selection operator algorithm and texture scores were calculated. The optimized CTTA signature was selected by comparing the predicting efficacy and clinical usefulness of 2D and 3D CTTA signatures. A CTTA nomogram was developed by integrating the optimized CTTA signature and clinical predictors, and its calibration, discrimination and clinical usefulness were evaluated. RESULTS:Maximum diametre and spiculation were independent clinical predictors. 1125 texture features were extracted from 2D and 3D ROIs and reduced to 11 features to build 2D and 3D CTTA signatures. There was significant difference (P < 0.001) in AUC (area under the curve) between 2D signature (AUC, 0.938) and 3D signature (AUC, 0.753) in the training set. There was no significant difference (P = 0.056) in AUC between 2D signature (AUC, 0.856) and 3D signature (AUC, 0.701) in the test set. Decision curve analysis showed the 2D signature outperformed the 3D signature in terms of clinical usefulness. The 2D CTTA nomogram (AUC, 0.938 and 0.861, in the training and test sets), which incorporated the 2D signature and clinical predictors, showed a similar discrimination capability (P = 1.000 and 0.430, in the training and test sets) and clinical usefulness as the 2D signature, and outperformed the clinical model (AUC, 0.678 and 0.776, in the training and test sets). CONCLUSIONS:2D CTTA signature performs better than 3D CTTA signature. The 2D CTTA nomogram with the 2D signature and clinical predictors incorporated provides the similar performance as the 2D signature for individual LVI prediction in LAC. 10.1016/j.ejrad.2020.109111