Pathological correlation with diffusion restriction on diffusion-weighted imaging in patients with pathological complete response after neoadjuvant chemoradiation therapy for locally advanced rectal cancer: preliminary results.
Jang K M,Kim S H,Choi D,Lee S J,Park M J,Min K
The British journal of radiology
OBJECTIVE:The objective of this study was to assess causative pathological factors associated with diffusion restriction on diffusion-weighted imaging (DWI) in patients who achieved pathological complete response (pCR) after treatment with neoadjuvant chemoradiation therapy (CRT) for locally advanced rectal cancer. METHODS:In total, 43 patients with locally advanced rectal cancer (≥T3 or lymph node positive) who underwent neoadjuvant CRT, subsequent surgery and ultimately achieved pCR were enrolled. All patients underwent pre- and post-CRT 3.0 T rectal MRI with DWI. Two radiologists blinded to pathological staging reviewed pre- and post-CRT 3.0 T rectal MRI for the presence of diffusion restriction in the corresponding tumour areas on post-CRT DWI, with a third radiologist arbitrating any disagreement. The consensus of these findings was then correlated with pathological data such as intramural mucin and the degree of proctitis and mural fibrosis seen on surgical specimen. Additionally, the pre-CRT tumour volume was measured to define the effect of this variable on the degree of radiation proctitis and fibrosis, as well as the presence of intramural mucin. RESULTS:Diffusion restriction occurred in 18 subjects (41.9%), while 25 subjects remained diffusion restriction-free (58.1%). The diffusion restriction group tended to have more severe proctitis and mural fibrosis when compared with non-diffusion restriction group (p<0.001). Intramural mucin was also more common in the diffusion restriction group (p=0.052). Higher pre-CRT tumour volumes were significantly predictive of the degree of proctitis (p=0.0247) and fibrosis (p=0.0445), but not the presence of intramural mucin (p=0.0944). Proctitis and mural fibrosis severity were also identified as independent pathological risk factors for diffusion restriction on multivariate analysis (p=0.0073 and 0.0011, respectively). CONCLUSION:Both radiation-induced proctitis and fibrosis were significant and independent predictors of diffusion restriction in patients achieving pCR after treatment with neoadjuvant CRT for locally advanced rectal cancer, and pre-CRT tumour volume significantly affects both variables.
Diffusion-weighted imaging in assessing pathological response of tumor in breast cancer subtype to neoadjuvant chemotherapy.
Liu Shangang,Ren Ruimei,Chen Zhaoqiu,Wang Yongsheng,Fan Tingyong,Li Chengli,Zhang Pinliang
Journal of magnetic resonance imaging : JMRI
PURPOSE:To investigate the efficacy of diffusion-weighted imaging (DWI) for reflecting and predicting pathological tumor response in breast cancer subtype to neoadjuvant chemotherapy (NAC). MATERIALS AND METHODS:The retrospective study included 176 patients with breast cancer who underwent magnetic resonance imaging (MRI) examinations before and after NAC prior to surgery. The pre- and post-NAC apparent diffusion coefficient (ADC) values of tumor were measured respectively on DWI. The pathological response was classified into either a complete response (pCR) or as a noncomplete response (pNCR) to NAC with the Miller & Payne system. The relationship between the ADC value and the pathological response was assessed according to intrinsic subtypes (Luminal A, Luminal B, HER2-enriched, and triple negative) defined by immunohistochemical features. RESULTS:Multiple comparisons respectively showed that pre-NAC and post-NAC ADC were significantly different among four subtypes (P < 0.001). After the comparison between two different subtypes, the pre-NAC ADC value of the triple-negative and HER2-enriched subtypes were significantly higher than Luminal A (P < 0.001 and P < 0.001) and Luminal B subtype (P < 0.001 and P = 0.009), and the post-NAC ADC of triple-negative subtype was significantly higher than the others (P < 0.001). The pre-NAC ADC of pCRs was significantly lower than that of pNCRs only in the triple-negative subtype among four subtypes (P < 0.001), and the post-NAC ADC of pCRs was significantly higher than that of pNCRs in each subtype (P < 0.001). CONCLUSION:DWI appears to be a promising tool to determine the association of pathological response to NAC in breast cancer subtypes.
Role of the Apparent Diffusion Coefficient in the Prediction of Response to Neoadjuvant Chemotherapy in Patients With Locally Advanced Breast Cancer.
Bufi Enida,Belli Paolo,Costantini Melania,Cipriani Antonio,Di Matteo Marialuisa,Bonatesta Angelo,Franceschini Gianluca,Terribile Daniela,Mulé Antonino,Nardone Luigia,Bonomo Lorenzo
Clinical breast cancer
BACKGROUND:We evaluated the diagnostic performance of the baseline diffusion weighted imaging (DWI) and the apparent diffusion coefficient (ADC) in the prediction of a complete pathologic response (pCR) to neoadjuvant chemotherapy (NAC) in patients with breast cancer stratified according to the tumor phenotype. PATIENTS AND METHODS:We retrospectively studied 225 patients with stage II, III, and IV breast cancer who had undergone contrast-enhanced magnetic resonance imaging (MRI) and DWI before and after NAC, followed by breast surgery. RESULTS:The tumor phenotypes were luminal (n = 143; 63.6%), triple-negative (TN) (n = 37; 16.4%), human epidermal growth factor receptor 2 (HER2)-enriched (n = 17; 7.6%), and hybrid (hormone receptor-positive/HER2(+); n = 28; 12.4%). After NAC, a pCR was observed in 39 patients (17.3%). No statistically significant difference was observed in the mean ADC value between a pCR and no pCR in the general population (1.132 ± 0.191 × 10(-3) mm(2)/s vs. 1.092 ± 0.189 × 10(-3) mm(2)/s, respectively; P = .23). The optimal ADC cutoff value in the general population was 0.975 × 10(-3) mm(2)/s (receiver operating characteristic [ROC] area under the curve [AUC], 0.587 for the prediction of a pCR). After splitting the population into subgroups according to tumor phenotype, we observed a significant or nearly significant difference in the mean ADC value among the responders versus the nonresponders in the TN (P = .06) and HER2(+) subgroups (P = .05). No meaningful difference was seen in the luminal and hybrid subgroups (P = .59 and P = .53, respectively). In contrast, in the TN and HER2(+) subgroups (cutoff value, 0.995 × 10(-3) mm(2)/s and 0.971 × 10(-3) mm(2)/s, respectively), we observed adequate ROC AUCs (0.766 and 0.813, respectively). CONCLUSION:The pretreatment ADC value is not capable of predicting the pCR in the overall population of patients with locally advanced breast cancer. Nonetheless, an ameliorated diagnostic performance was observed in specific phenotype subgroups (ie, TN and HER2(+) tumors).
Role of the Intravoxel Incoherent Motion Diffusion Weighted Imaging in the Pre-treatment Prediction and Early Response Monitoring to Neoadjuvant Chemotherapy in Locally Advanced Breast Cancer.
Che Shunan,Zhao Xinming,Ou Yanghan,Li Jing,Wang Meng,Wu Bing,Zhou Chunwu
The aim of this study was to explore whether intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) can probe pre-treatment differences or monitor early response in patients with locally advanced breast cancer receiving neoadjuvant chemotherapy (NAC). Thirty-six patients with locally advanced breast cancer were imaged using multiple-b DWI with 12 b values ranging from 0 to 1000 s/mm(2) at the baseline, and 28 patients were repeatedly scanned after the second cycle of NAC. Subjects were divided into pathologic complete response (pCR) and nonpathologic complete response (non-pCR) groups according to the surgical pathologic specimen. Parameters (D, D*, f, maximum diameter [MD] and volume [V]) before and after 2 cycles of NAC and their corresponding change (Δparameter) between pCR and non-pCR groups were compared using the Student t test or nonparametric test. The diagnostic performance of different parameters was judged by the receiver-operating characteristic curve analysis. Before NAC, the f value of pCR group was significantly higher than that of non-pCR (32.40% vs 24.40%, P = 0.048). At the end of the second cycle of NAC, the D value was significantly higher and the f value was significantly lower in pCR than that in non-pCR (P = 0.001; P = 0.015, respectively), whereas the D* value and V of the pCR group was slightly lower than that of the non-pCR group (P = 0.507; P = 0.676, respectively). ΔD was higher in pCR (-0.45 × 10(-3) mm(2)/s) than that in non-pCR (-0.07 × 10(-3) mm(2)/s) after 2 cycles of NAC (P < 0.001). Δf value in the pCR group was significantly higher than that in the non-pCR group (17.30% vs 5.30%, P = 0.001). There was no significant difference in ΔD* between the pCR and non-pCR group (P = 0.456). The prediction performance of ΔD value was the highest (AUC [area under the curve] = 0.924, 95% CI [95% confidence interval] = 0.759-0.990). When the optimal cut-off was set at -0.163 × 10(-3) mm(2)/s, the values for sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were up to 100% (95% CI = 66.4-100), 73.7% (95% CI = 48.8-90.9), 64.3% (95% CI = 35.6-86.0), and 100% (95% CI = 73.2-99.3), respectively. IVIM-derived parameters, especially the D and f value, showed potential value in the pre-treatment prediction and early response monitoring to NAC in locally advanced breast cancer. ΔD value had the best prediction performance for pathologic response after NAC.
Diffusion-weighted magnetic resonance imaging in locally advanced rectal cancer treated with neoadjuvant chemoradiotherapy.
De Felice F,Magnante A L,Musio D,Ciolina M,De Cecco C N,Rengo M,Laghi A,Tombolini V
European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
PURPOSE:To analyze diffusion-weighted magnetic resonance imaging (DW-MRI) for treatment response assessment in locally advanced rectal cancer (LARC). PATIENTS AND METHODS:Patients with histologically proven rectal adenocarcinoma, stage II-III disease, were enrolled and underwent surgery following neoadjuvant chemoradiotherapy (nCRT). All patients were referred for a DW-MRI protocol on a 3 Tesla MR-system, consisting of axial T2-weighted and DWI sequences prior (I), during (II) and after (III) nCRT. Corresponding apparent diffusion coefficient (ADC) values were calculated. RESULTS:Between February 2011 and June 2015, 37 patients participated in the study. All patients completed programmed treatment. Overall, 11 patients (29.7%) had pathologic complete response (pCR). No correlation between the mean pre- (ADC-I), during (ADC-II), post- (ADC-III) ADC and the reduction in tumor size after nCRT was recorded. No substantial difference in the ADC distribution was found between pCR and no-pCR patients. The ADC-II level significantly increased in the pCR cases (T = 1.675; p < 0.05). CONCLUSION:ADC value could be useful for discriminating between the pCR patients and the no-pCR patients. Further studies are necessary to identify the optimal MRI parameters combination to predict tumor response to nCRT. It is hoped that these data will provide the basis for a more solid scientific evidence.
Apparent Diffusion Coefficient Predicts Pathology Complete Response of Rectal Cancer Treated with Neoadjuvant Chemoradiotherapy.
Chen Yuan-Gui,Chen Ming-Qiu,Guo Yu-Yan,Li Si-Cong,Wu Jun-Xin,Xu Ben-Hua
OBJECTIVE:To evaluate the predictive value of the apparent diffusion coefficient (ADC) for pathologic complete response (pCR) to neoadjuvant chemoradiotherapy (NCRT) in locally advanced rectal cancer. METHODS:A total of 265 patients with rectal adenocarcinoma, whole Diffusion-Weighted MRI (DWI-MRI) images, clinically stage II to III (cT3-4 and/or cN+) and treated with NCRT followed by TME were screened. Fifty patients with pCR and another 50 patients without pCR with similar clinical charcacters and treatment regimens were selected for statistical analysis. All the patients' pre-CRT and post-CRT average ADC values were calculated from the coefficient maps created by DWI-MRI and recorded independently. The difference in the ADC values between the pCR and non-pCR was analyzed by the Mann-Whitney U test. The cut-off ADC value of the receiver operating characteristic (ROC) curve with pCR was then established. RESULTS:The mean pre- and post-ADC values in all patients, and in pCR patients and non-pCR patients were 0.879±0.06 and 1.383±0.11, 0.859±0.04 and 1.440±0.10, 0.899±0.07 and 1.325±0.09 (×10(-3) mm(2)/s), respectively. The difference between the pre- and post-ADC values in all patients, pCR patients, and non-pCR patients were considered to be statistically significant. The pre-ADC value was significantly lower in the pCR patients than in the non-pCR patients (p = 0.003), whereas the post-ADC values were significantly higher in the pCR patients than in the non-pCR patients. The percentage increase of the ADC value (ΔADC%) in the pCR and non-pCR patients were 68% and 48% respectively (p<0.001). The ROC curves of the cut-off value of the pre-CRT patient ADC value was 0.866×10(-3) mm(2)/s. The AUC, sensitivity, specificity, PPV, NPV, and accuracy of diagnosing pCR were 0.670 (95% CI 0.563-0.777), 0.600, 0.640, 60%, 60%, and 60%, respectively. The cut-off value of ΔADC% was 58%. The corresponding AUC, sensitivity, specificity, PPV, NPV, and accuracy of diagnosing pCR were 0.856 (95% CI 0.783-0.930), 0.800, 0.760, 76.9%, 79.2%, and 78%, respectively. CONCLUSIONS:DWI-MRI technology can be efficient for predicting pCR for LARC after NCRT. Although the mean pre-CRT ADC value and the ΔADC% are moderate predictors for pCR, the latter would be more accurate.
Effect of breast cancer phenotype on diagnostic performance of MRI in the prediction to response to neoadjuvant treatment.
Bufi Enida,Belli Paolo,Di Matteo Marialuisa,Terribile Daniela,Franceschini Gianluca,Nardone Luigia,Petrone Gianluigi,Bonomo Lorenzo
European journal of radiology
AIM:The estimation of response to neoadjuvant chemotherapy (NAC) is useful in the surgical decision in breast cancer. We addressed the diagnostic reliability of conventional MRI, of diffusion weighted imaging (DWI) and of a merged criterion coupling morphological MRI and DWI. Diagnostic performance was analysed separately in different tumor subtypes, including HER2+ (human epidermal growth factor receptor 2)/HR+ (hormone receptor) (hybrid phenotype). MATERIALS AND METHODS:Two-hundred and twenty-five patients underwent MRI before and after NAC. The response to treatment was defined according to the RECIST classification and the evaluation of DWI with apparent diffusion coefficient (ADC). The complete pathological response - pCR was assessed (Mandard classification). RESULTS:Tumor phenotypes were Luminal (63.6%), Triple Negative (16.4%), HER2+ (7.6%) or Hybrid (12.4%). After NAC, pCR was observed in 17.3% of cases. Average ADC was statistically higher after NAC (p<0.001) among patients showing pCR vs. those who had not pCR. The RECIST classification showed adequate performance in predicting the pCR in Triple Negative (area under the receiver operating characteristic curve, ROC AUC=0.9) and in the HER2+ subgroup (AUC=0.826). Lower performance was found in the Luminal and Hybrid subgroups (AUC 0.693 and 0.611, respectively), where the ADC criterion yielded an improved performance (AUC=0.787 and 0.722). The coupling of morphological and DWI criteria yielded maximally improved performance in the Luminal and Hybrid subgroups (AUC=0.797 and 0.761). CONCLUSION:The diagnostic reliability of MRI in predicting the pCR to NAC depends on the tumor phenotype, particularly in the Luminal and Hybrid subgroups. In these cases, the coupling of morphological MRI evaluation and DWI assessment may facilitate the diagnosis.
Exploring MR regression patterns in rectal cancer during neoadjuvant radiochemotherapy with daily T2- and diffusion-weighted MRI.
Bostel T,Dreher C,Wollschläger D,Mayer A,König F,Bickelhaupt S,Schlemmer H P,Huber P E,Sterzing F,Bäumer P,Debus J,Nicolay N H
Radiation oncology (London, England)
BACKGROUND:To date, only limited magnetic resonance imaging (MRI) data are available concerning tumor regression during neoadjuvant radiochemotherapy (RCT) of rectal cancer patients, which is a prerequisite for adaptive radiotherapy (RT) concepts. This exploratory study prospectively evaluated daily fractional MRI during neoadjuvant treatment to analyze the predictive value of MR biomarkers for treatment response. METHODS:Locally advanced rectal cancer patients were examined with daily MRI during neoadjuvant RCT. Contouring of the tumor volume was performed for each MRI scan by using T2- and diffusion-weighted-imaging (DWI)-sequences. The daily apparent-diffusion coefficient (ADC) was calculated. Volumetric and functional tumor changes during RCT were analyzed and correlated with the pathological response after surgical resection. RESULTS:In total, 171 MRI scans of eight patients were analyzed regarding anatomical and functional dynamics during RCT. Pathological complete response (pCR) could be achieved in four patients, and four patients had a pathological partial response (pPR) following neoadjuvant treatment. T2- and DWI-based volumetry proved to be statistically significant in terms of therapeutic response, and volumetric thresholds at week two and week four during RCT were defined for the prediction of pCR. In contrast, the average tumor ADC values widely overlapped between both response groups during RCT and appeared inadequate to predict treatment response in our patient cohort. CONCLUSION:This prospective exploratory study supports the hypothesis that MRI may be able to predict pCR of rectal cancers early during neoadjuvant RCT. Our data therefore provide a useful template to tailor future MR-guided adaptive treatment concepts.
Investigating the prediction value of multiparametric magnetic resonance imaging at 3 T in response to neoadjuvant chemotherapy in breast cancer.
Minarikova Lenka,Bogner Wolfgang,Pinker Katja,Valkovič Ladislav,Zaric Olgica,Bago-Horvath Zsuzsanna,Bartsch Rupert,Helbich Thomas H,Trattnig Siegfried,Gruber Stephan
OBJECTIVE:To explore the predictive value of parameters derived from diffusion-weighted imaging (DWI) and contrast-enhanced (CE)-MRI at different time-points during neoadjuvant chemotherapy (NACT) in breast cancer. METHODS:Institutional review board approval and written, informed consent from 42 breast cancer patients were obtained. The patients were investigated before and at three different time-points during neoadjuvant chemotherapy (NACT) using tumour diameter and volume from CE-MRI and ADC values obtained from drawn 2D and segmented 3D regions of interest. Prediction of pathologic complete response (pCR) was evaluated using the area under the curve (AUC) of receiver operating characteristic analysis. RESULTS:There was no significant difference between pathologic complete response and non-pCR in baseline size measures (p > 0.39). Diameter change was significantly different in pCR (p < 0.02) before the mid-therapy point. The best predictor was lesion diameter change observed before mid-therapy (AUC = 0.93). Segmented volume was not able to differentiate between pCR and non-pCR at any time-point. The ADC values from 3D-ROI were not significantly different from 2D data (p = 0.06). The best AUC (0.79) for pCR prediction using DWI was median ADC measured before mid-therapy of NACT. CONCLUSIONS:The results of this study should be considered in NACT monitoring planning, especially in MRI protocol designing and time point selection. KEY POINTS:• Mid-therapy diameter changes are the best predictors of pCR in neoadjuvant chemotherapy. • Volumetric measures are not strictly superior in therapy monitoring to lesion diameter. • Size measures perform as a better predictor than ADC values.
Diffusion-weighted imaging in monitoring the pathological response to neoadjuvant chemotherapy in patients with breast cancer: a meta-analysis.
Gao Wen,Guo Ning,Dong Ting
World journal of surgical oncology
BACKGROUND:Diffusion-weighted imaging (DWI) is suggested as an non-invasive and non-radioactive imaging modality in the identification of pathological complete response (pCR) in breast cancer patients receiving neoadjuvant chemotherapy (NACT). A growing number of trials have been investigating in this aspect and some studies found a superior performance of DWI compared with conventional imaging techniques. However, the efficiency of DWI is still in dispute. This meta-analysis aims at evaluating the accuracy of DWI in the detection of pCR to NACT in patients with breast cancer. METHODS:Pooled sensitivity, specificity, and diagnostic odds ratio (DOR) were drawn to estimate the diagnostic effect of DWI to NACT. Summary receiver operating characteristic curve (SROC), the area under the SROC curve (AUC), and Youden index (*Q) were also calculated. The possible sources of heterogeneity among the included studies were explored using single-factor meta-regression analyses. Publication bias and quality assessment were assessed using Deek's funnel plot and QUADAS-2 form respectively. RESULTS:Twenty studies incorporated 1490 participants were enrolled in our analysis. Pooled estimates revealed a sensitivity of 0.89 (95% CI, 0.86-0.91), a specificity of 0.72 (95% CI, 0.68-0.75), and a DOR of 27.00 (95% CI, 15.60-46.73). The AUC of SROC curve and *Q index were 0.9088 and 0.8408, respectively. The results of meta-regression analyses showed that pCR rate, time duration of study population, and study design were not the sources of heterogeneity. CONCLUSION:A relatively high sensitivity and specificity of DWI in diagnosing pCP for patients with breast cancer underwent NACT treatment was found in our meta-analysis. This finding indicated that the use of DWI might provide an accurate and precise assessment of pCR to NACT.
The role of readout-segmented echo-planar imaging-based diffusion-weighted imaging in evaluating tumor response of locally advanced rectal cancer after neoadjuvant chemoradiotherapy.
Yang Lanqing,Xia Chunchao,Liu Dan,Fang Xin,Pan Xuelin,Ma Ling,Wu Bing
Acta radiologica (Stockholm, Sweden : 1987)
BACKGROUND:Accurate assessment of tumor response in rectal cancer could help individualize treatment. PURPOSE:To evaluate the role of diffusion-weighted imaging (DWI) based on readout-segmented echo-planar imaging (rs-EPI) in assessing tumor response after neoadjuvant chemoradiotherapy (CRT) in locally advanced rectal cancer (LARC). MATERIAL AND METHODS:Sixty-three patients with LARC who received neoadjuvant CRT and surgery were enrolled retrospectively. They all underwent pre- and post-CRT magnetic resonance examinations, including DWI using rs-EPI. According to pathological results, patients were grouped as pathological complete responder (pCR, n = 16) and non-pCR (n = 47). Visual assessment of residual tumor and whole-tumor histogram analysis of pre- and post-CRT apparent diffusion coefficient (ADC) map was performed by two radiologists; tumor volume on ADC map was also recorded. RESULTS:Overall inter-observer agreement was good for histogram analysis (ICC = 0.543-0.999). Tumor volume reduction rate on ADC map showed no significant difference between the two groups ( = 0.468). Post-CRT mean, quantile values, and their percentage changes were higher in the pCR group (all < 0.001). Post-CRT mean value had a good diagnostic power in selecting pCR (AUC = 0.855), with a cut-off value of 1.345 × 10 mm/s, yielding a sensitivity of 83%, specificity of 81.3%. Post-CRT 95% quantile value had the highest AUC (AUC = 0.868) among quantile values, and a higher specificity (87.5% vs. 81.3%) than mean value with comparable overall diagnostic performance ( = 0.563). Visual assessment showed a sensitivity of 85.1%, specificity of 68.8% in selecting pCR. CONCLUSION:Quantitative ADC value of rs-EPI DWI could reliably evaluate tumor response in patients with LARC. Post-CRT 95% quantile ADC value could help mean value to more accurately identify pCR.
[Clinical value of MR diffusion weighted imaging in prediction of pathological complete response of rectal cancer after neoadjuvant therapy].
Cao Wu-teng,Zhou Zhi-yang,Deng Yan-hong,Kang Liang,Lian Yan-bang,Qiu Jian-ping,Gong Jia-ying,Xiong Fei,Li Wen-ru,Zhu Pan
Zhonghua wei chang wai ke za zhi = Chinese journal of gastrointestinal surgery
OBJECTIVE:To evaluate the application value of magnetic resonance diffusion-weighted imaging (DWI) combined with routine T2WI sequence in the determination of pathological complete response (pCR) after neoadjuvant therapy for rectal cancer. METHODS:Clinical data of 51 cases with locally advanced mid-low rectal cancer undergoing neoadjuvant therapy plus radical resection in the Rectal Cancer Center at The Sixth Affiliated Hospital of Sun Yat-sen University from June 2012 to April 2013 were analyzed retrospectively. Magnetic resonance DWI and T2WI sequences scanning were performed within 1 week before neoadjuvant therapy and within 1 week before operation. Routine single T2WI sequence and DWI combined with T2WI sequence were used separately to predict the residual tumor and to compare with postoperative pathological examination. The prediction values of two methods were compared. RESULTS:Of 51 patients, 12 cases had pathological complete response (pCR). Prediction of DWI combined T2WI sequence was correct in 8 cases of pCR, whose sensitivity and specificity were higher than those of routine single T2WI sequence (66.7%, 94.9% vs. 33.3%, 84.6%). Prediction value of DWI combined T2WI sequence for pCR was significantly higher as compared to routine single T2WI sequence (AUC, 0.808 vs. 0.590, P=0.001). CONCLUSION:Compared with the routine single T2WI sequence, DWI combined with T2WI sequence can improve the prediction accuracy of pathological complete response.
Diffusion weighted imaging improves diagnostic ability of MRI for determining complete response to neoadjuvant therapy in locally advanced rectal cancer.
Chandramohan Anuradha,Siddiqi Umar M,Mittal Rohin,Eapen Anu,Jesudason Mark R,Ram Thomas S,Singh Ashish,Masih Dipti
European journal of radiology open
Purpose:To assess the diagnostic performance, interobserver agreement and confidence level for determining response to neoadjuvant chemoradiotherapy (NACRT) using morphology based MR-tumour regression grade (MR TRG), diffusion weighted imaging (DWI) patterns and their combination in patients with locally advanced rectal cancer. Methods:This was a retrospective study including patients with locally advanced rectal cancer treated with NACRT and subsequent surgery. Two independent radiologists blinded to the histopathology reviewed staging and restaging MRI. Diagnostic performance of morphology based MR-TRG, DWI patterns and their combination for determining complete (CR) and incomplete (IR) response was assessed with pathological response as the reference. Likert's scale was used to assess the radiologist's level of confidence. Interobserver agreement was determined using Kappa statistics. Results:The study included 251 patients (mean age of 47.9+/-14 (range 19-86) years, M:F = 164:87). Rate of pathological CR was 14.7 % (n = 37). Pattern based interpretation of DWI and combined approach (DWI + T2-HR) had superior diagnostic performance than morphology based assessment alone with area under curve (AUC) for T2HR, DWI and their combination being 0.531, 0.887, 0.874 respectively for observer 1 and 0.558, 0.653, 0.678 respectively for observer 2, p < 0.001. Interobserver agreement was substantial (k = 0.688) for combined approach, moderate (k = 0.402) for DWI patterns and fair (k = 0.265) for T2 HR MRI with both observers exhibiting highest level of confidence for determining response with the combined approach. Conclusion:Complete response to neoadjuvant chemoradiotherapy can be determined with excellent accuracy, substantial interobserver agreement and high level of confidence by combined interpretation of DWI and T2 high resolution MRI.
ADC as a predictor of pathologic response to neoadjuvant therapy in esophageal cancer: a systematic review and meta-analysis.
Maffazzioli Leticia,Zilio Mariana B,Klamt Alexandre L,Duarte Juliana A,Mazzini Guilherme S,Campos Vinicius J,Chedid Marcio F,Gurski Richard R
OBJECTIVE:Diffusion-weighted magnetic resonance imaging (DWI) is part of clinical practice. The aim of this study was to evaluate the role of apparent diffusion coefficient (ADC) as a predictor of pathologic response to neoadjuvant therapy (nCRT) in patients with esophageal cancer (EC). METHODS:The MEDLINE, Embase, and Google Scholar databases were systematically searched for studies using ADC to evaluate response to neoadjuvant therapy in patients with EC. Methodological quality of the studies was evaluated with the QUADAS tool. Data from eligible studies were extracted and evaluated by two independent reviewers. Meta-analyses were performed comparing mean ADC values between responders and non-responders to nCRT in three different scenarios: baseline (BL) absolute values; percent change between intermediate (IM) values and BL; and percent change between final follow-up (FU) value and baseline BL. RESULTS:Seven studies (n = 158 patients) were included. Responders exhibited a statistically significant percent increase in ADC during nCRT (mean difference [MD] 21.06%, 95%CI = 13.04-29.09; I = 49%; p = 0.12). A similar increase was identified in the complete pathologic response (pCR) versus non-complete pathologic response (npCR) subgroup (MD = 25.68%, 95%CI = 18.87-32.48; I = 0%; p = 0.60). At the end of treatment, responders also exhibited a statistically significant percent increase in ADC (MD = 22.49%, 95%CI = 9.94-35.05; I = 0%; p = 0.46). BL ADC was not associated with any definition of pathologic response (MD = 0.11%, 95%CI = - 0.21-0.42; I = 85%; p < 0.01). CONCLUSION:These results suggest that ADC can be used as a predictor of pathologic response, with a statistically significant association between percent ADC increase during and after treatment and pCR. ADC may serve as a tool to help in guiding clinical decisions. KEY POINTS:• DWI is routinely included in MRI oncological protocols. • ADC can be used as a predictor of pathologic response, with a statistically significant association between percent ADC increase during and after treatment and pCR.
Accuracy of multi-parametric breast MR imaging for predicting pathological complete response of operable breast cancer prior to neoadjuvant systemic therapy.
Tsukada Hiroko,Tsukada Jitsuro,Schrading Simone,Strobel Kevin,Okamoto Takahiro,Kuhl Christiane K
Magnetic resonance imaging
OBJECTIVES:To evaluate whether multiparametric breast-MRI, obtained before the initiation of neoadjuvant systemic therapy (NST) for operable breast cancer, predicts which cancer will achieve a pathological complete response (pCR) after the completion of NST. METHODS:This was an IRB-approved retrospective study on 31 consecutive patients (median age, 56 years) with operable invasive breast cancer (median size: 22 mm; triple-negative: 11/31 [35%], HER2-positive: 7/31 [23%], triple-positive: 13/31 [42%]) who underwent multiparametric DCE-MRI before the initiation of NST. The MRI protocol consisted of high-resolution dynamic contrast-enhanced MRI (DCE-MRI), T2-TSE, and DWI (b-values 0, 100, 800 s/mm). The results of surgical pathology after the completion of NST served as a standard of reference. Patient characteristics (age and menopausal status), pathological tumor characteristics (type, stage, nuclear grade, ER/PR and HER2 receptor status, and Ki-67 staining), and MRI characteristics (size, morphology, T2 signal intensity, enhancement kinetics, and ADC values) before NST were evaluated and compared between patients achieving pCR vs. non-pCR. RESULTS:Among 31 patients, 17 achieved pCR (55%) and 14 non-pCR (45%). No correlation was observed between patient- or tumor pathology-derived characteristics and pCR vs. non-pCR. Among MRI-derived tumor characteristics, tumor growth orientation parallel to Cooper's ligaments (p = 0.002) and wash-out rates (p = 0.019) correlated with pCR. Pre-NST ADC values were lower in patients achieving pCR (P = 0.086). CONCLUSIONS:A tumor growth pattern parallel with Cooper's ligaments and a fast wash-out rate on pre-treatment multiparametric MRI are predictive of pCR and more closely associated with pCR than ADC values.
Early prediction of neoadjuvant treatment outcome in locally advanced breast cancer using parametric response mapping and radial heterogeneity from breast MRI.
Drisis Stylianos,El Adoui Mohammed,Flamen Patrick,Benjelloun Mohammed,Dewind Roland,Paesmans Mariane,Ignatiadis Michail,Bali Maria,Lemort Marc
Journal of magnetic resonance imaging : JMRI
BACKGROUND:Early prediction of nonresponse is essential in order to avoid inefficient treatments. PURPOSE:To evaluate if parametrical response mapping (PRM)-derived biomarkers could predict early morphological response (EMR) and pathological complete response (pCR) 24-72 hours after initiation of chemotherapy treatment and whether concentric analysis of nonresponding PRM regions could better predict response. STUDY TYPE:This was a retrospective analysis of prospectively acquired cohort, nonrandomized, monocentric, diagnostic study. POPULATION:Sixty patients were initially recruited, with 39 women participating in the final cohort. FIELD STRENGTH/SEQUENCE:A 1.5T scanner was used for MRI examinations. ASSESSMENT:Dynamic contrast-enhanced (DCE)-MR images were acquired at baseline (timepoint 1, TP1), 24-72 hours after the first chemotherapy (TP2), and after the end of anthracycline treatment (TP3). PRM was performed after fusion of T subtraction images from TP1 and TP2 using an affine registration algorithm. Pixels with an increase of more than 10% of their value (PRMdce+) were corresponding nonresponding regions of the tumor. Patients with a decrease of maximum diameter (%dDmax) between TP1 and TP3 of more than 30% were defined as EMR responders. pCR patients achieved a residual cancer burden score of 0. STATISTICAL TESTS:T-test, receiver operating characteristic (ROC) curves, and logistic regression were used for the analysis. RESULTS:PRM showed a statistical difference between pCR response groups (P < 0.01) and AUC of 0.88 for the prediction of non-pCR. Logistic regression analysis demonstrated that PRMdce+ and Grade II were significant (P < 0.01) for non-pCR prediction (AUC = 0.94). Peripheral tumor region demonstrated higher performance for the prediction of non-pCR (AUC = 0.85) than intermediate and central zones; however, statistical comparison showed no significant difference. DATA CONCLUSION:PRM could be predictive of non-pCR 24-72 hours after initiation of chemotherapy treatment. Moreover, the peripheral region showed increased AUC for non-pCR prediction and increased signal intensity during treatment for non-pCR tumors, information that could be used for optimal tissue sampling. LEVEL OF EVIDENCE:1 Technical Efficacy Stage: 4 J. Magn. Reson. Imaging 2020;51:1403-1411.
Nomogram for Early Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using Dynamic Contrast-enhanced and Diffusion-weighted MRI.
Zhao Rui,Lu Hong,Li Yan-Bo,Shao Zhen-Zhen,Ma Wen-Juan,Liu Pei-Fang
RATIONALE AND OBJECTIVES:The study investigated the potential of the combined use of dynamic contrast-enhanced MRI and diffusion-weighted imaging in predicting the pathological complete response (pCR) of neoadjuvant chemotherapy (NAC) after two cycles of NAC. MATERIALS AND METHODS:Eighty-seven patients with breast cancer who underwent MR examination before and after two cycles of NAC were enrolled. The patients were randomly assigned to a training cohort and a validation cohort (3:1 ratio). MRI parameters including tumor longest diameter, time-signal intensity curve, early enhanced ratio (E), maximal enhanced ratio and ADC value were measured, and percentage change in MRI parameters were calculated. Univariate analysis and multivariate logistic regression analysis were used to evaluate independent predictors of pCR in the training cohort. The validation cohort was used to test the prediction model, and the nomogram was created based on the prediction model. RESULTS:This study demonstrated that the ADC value after two cycles of NAC (OR = 1.041, 95% CI (1.002, 1.081); p = 0.037), percentage decrease in E (OR = 0.927, 95% CI (0.881, 0.977); p =0.004) and percentage decrease in tumor size (OR = 0.948, 95% CI (0.909, 0.988); p = 0.011) were significantly important for independently predicting pCR. The prediction model yielded AUC of 0.939 and 0.944 in the training cohort and the validation cohort, respectively. CONCLUSION:The combined use of dynamic contrast-enhanced MRI and diffusion-weighted imaging could accurately predict pCR after two cycles of NAC. The prediction model and the nomogram had strong predictive value to NAC.
[Preoperative evaluation of pathologic response in patients with breast cancer by dynamic contrast-enhanced magnetic resonance imaging].
Zhang Xiao-Peng,Li Jie,Sun Ying-Shi,Cao Kun,Tang Lei
Zhongguo yi xue ke xue yuan xue bao. Acta Academiae Medicinae Sinicae
OBJECTIVE:To investigate the clinical value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in assessing pathologic responses in breast cancer treated with neoadjuvant chemotherapy. METHODS:Forty-five patients with pathologically confirmed breast carcinoma who finished last course of neoadjuvant chemotherapy underwent preoperative breast MRI. All the specimen slices were blindly reviewed by one pathologist. Pathologic response was assessed according Miller & Payne five-point classification, of which grade 5 defined as pathological complete response (pCR) , and grade 5 or 4 defined as major histological response (MHR). DCE-MRI images were blindly reviewed by two radiologists retrospectively on workstation with Functool software. Any non-vessel enhancement in previous tumor bed in any phase of postcontrast acquisition was defined as residual disease. The diagnostic results of two radiologists were correlated to pathological gold standard. Inter-observer consistency was analyzed by Kappa statistics. RESULTS:DCE-MRI for pathological invasive (pINV) residual disease detection in two radiologists had sensitivities of 94.7% and 97.4%, specificities of 42.8% and 57.1%, and accuracy of 86.6% and 91.1%, respectively, while MHR evaluation had sensitivities of 95.5% and 81.8%, specificities of 73.9% and 82.6%, and accuracies of 84.4% and 82.2%, respectively. K values in determine pINV and MHR were 0.728 and 0.778, respectively, showing good inter-observer consistency. CONCLUSION:DCE-MRI is sensitive in detecting residual breast cancer after neoadjuvant chemotherapy, and can be used to predict the postoperative pathologic response.
Prediction of Tumor Shrinkage Pattern to Neoadjuvant Chemotherapy Using a Multiparametric MRI-Based Machine Learning Model in Patients With Breast Cancer.
Huang Yuhong,Chen Wenben,Zhang Xiaoling,He Shaofu,Shao Nan,Shi Huijuan,Lin Zhenzhe,Wu Xueting,Li Tongkeng,Lin Haotian,Lin Ying
Frontiers in bioengineering and biotechnology
After neoadjuvant chemotherapy (NACT), tumor shrinkage pattern is a more reasonable outcome to decide a possible breast-conserving surgery (BCS) than pathological complete response (pCR). The aim of this article was to establish a machine learning model combining radiomics features from multiparametric MRI (mpMRI) and clinicopathologic characteristics, for early prediction of tumor shrinkage pattern prior to NACT in breast cancer. This study included 199 patients with breast cancer who successfully completed NACT and underwent following breast surgery. For each patient, 4,198 radiomics features were extracted from the segmented 3D regions of interest (ROI) in mpMRI sequences such as T1-weighted dynamic contrast-enhanced imaging (T1-DCE), fat-suppressed T2-weighted imaging (T2WI), and apparent diffusion coefficient (ADC) map. The feature selection and supervised machine learning algorithms were used to identify the predictors correlated with tumor shrinkage pattern as follows: (1) reducing the feature dimension by using ANOVA and the least absolute shrinkage and selection operator (LASSO) with 10-fold cross-validation, (2) splitting the dataset into a training dataset and testing dataset, and constructing prediction models using 12 classification algorithms, and (3) assessing the model performance through an area under the curve (AUC), accuracy, sensitivity, and specificity. We also compared the most discriminative model in different molecular subtypes of breast cancer. The Multilayer Perception (MLP) neural network achieved higher AUC and accuracy than other classifiers. The radiomics model achieved a mean AUC of 0.975 (accuracy = 0.912) on the training dataset and 0.900 (accuracy = 0.828) on the testing dataset with 30-round 6-fold cross-validation. When incorporating clinicopathologic characteristics, the mean AUC was 0.985 (accuracy = 0.930) on the training dataset and 0.939 (accuracy = 0.870) on the testing dataset. The model further achieved good AUC on the testing dataset with 30-round 5-fold cross-validation in three molecular subtypes of breast cancer as following: (1) HR+/HER2-: 0.901 (accuracy = 0.816), (2) HER2+: 0.940 (accuracy = 0.865), and (3) TN: 0.837 (accuracy = 0.811). It is feasible that our machine learning model combining radiomics features and clinical characteristics could provide a potential tool to predict tumor shrinkage patterns prior to NACT. Our prediction model will be valuable in guiding NACT and surgical treatment in breast cancer.
Rectal Cancer: Assessment of Neoadjuvant Chemoradiation Outcome based on Radiomics of Multiparametric MRI.
Nie Ke,Shi Liming,Chen Qin,Hu Xi,Jabbour Salma K,Yue Ning,Niu Tianye,Sun Xiaonan
Clinical cancer research : an official journal of the American Association for Cancer Research
PURPOSE:To evaluate multiparametric MRI features in predicting pathologic response after preoperative chemoradiation therapy (CRT) for locally advanced rectal cancer (LARC). EXPERIMENTAL DESIGN:Forty-eight consecutive patients (January 2012-November 2014) receiving neoadjuvant CRT were enrolled. All underwent anatomical T1/T2, diffusion-weighted MRI (DWI) and dynamic contrast-enhanced (DCE) MRI before CRT. A total of 103 imaging features, analyzed using both volume-averaged and voxelized methods, were extracted for each patient. Univariate analyses were performed to evaluate the capability of each individual parameter in predicting pathologic complete response (pCR) or good response (GR) evaluated based on tumor regression grade. Artificial neural network with 4-fold validation technique was further utilized to select the best predictor sets to classify different response groups and the predictive performance was calculated using receiver operating characteristic (ROC) curves. RESULTS:The conventional volume-averaged analysis could provide an area under ROC curve (AUC) ranging from 0.54 to 0.73 in predicting pCR. While if the models were replaced by voxelized heterogeneity analysis, the prediction accuracy measured by AUC could be improved to 0.71-0.79. Similar results were found for GR prediction. In addition, each subcategory images could generate moderate power in predicting the response, which if combining all information together, the AUC could be further improved to 0.84 for pCR and 0.89 for GR prediction, respectively. CONCLUSIONS:Through a systematic analysis of multiparametric MR imaging features, we are able to build models with improved predictive value over conventional imaging metrics. The results are encouraging, suggesting the wealth of imaging radiomics should be further explored to help tailoring the treatment into the era of personalized medicine. Clin Cancer Res; 22(21); 5256-64. ©2016 AACR.
Predicting breast cancer response to neoadjuvant treatment using multi-feature MRI: results from the I-SPY 2 TRIAL.
Li Wen,Newitt David C,Gibbs Jessica,Wilmes Lisa J,Jones Ella F,Arasu Vignesh A,Strand Fredrik,Onishi Natsuko,Nguyen Alex Anh-Tu,Kornak John,Joe Bonnie N,Price Elissa R,Ojeda-Fournier Haydee,Eghtedari Mohammad,Zamora Kathryn W,Woodard Stefanie A,Umphrey Heidi,Bernreuter Wanda,Nelson Michael,Church An Ly,Bolan Patrick,Kuritza Theresa,Ward Kathleen,Morley Kevin,Wolverton Dulcy,Fountain Kelly,Lopez-Paniagua Dan,Hardesty Lara,Brandt Kathy,McDonald Elizabeth S,Rosen Mark,Kontos Despina,Abe Hiroyuki,Sheth Deepa,Crane Erin P,Dillis Charlotte,Sheth Pulin,Hovanessian-Larsen Linda,Bang Dae Hee,Porter Bruce,Oh Karen Y,Jafarian Neda,Tudorica Alina,Niell Bethany L,Drukteinis Jennifer,Newell Mary S,Cohen Michael A,Giurescu Marina,Berman Elise,Lehman Constance,Partridge Savannah C,Fitzpatrick Kimberly A,Borders Marisa H,Yang Wei T,Dogan Basak,Goudreau Sally,Chenevert Thomas,Yau Christina,DeMichele Angela,Berry Don,Esserman Laura J,Hylton Nola M
NPJ breast cancer
Dynamic contrast-enhanced (DCE) MRI provides both morphological and functional information regarding breast tumor response to neoadjuvant chemotherapy (NAC). The purpose of this retrospective study is to test if prediction models combining multiple MRI features outperform models with single features. Four features were quantitatively calculated in each MRI exam: functional tumor volume, longest diameter, sphericity, and contralateral background parenchymal enhancement. Logistic regression analysis was used to study the relationship between MRI variables and pathologic complete response (pCR). Predictive performance was estimated using the area under the receiver operating characteristic curve (AUC). The full cohort was stratified by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status (positive or negative). A total of 384 patients (median age: 49 y/o) were included. Results showed analysis with combined features achieved higher AUCs than analysis with any feature alone. AUCs estimated for the combined versus highest AUCs among single features were 0.81 (95% confidence interval [CI]: 0.76, 0.86) versus 0.79 (95% CI: 0.73, 0.85) in the full cohort, 0.83 (95% CI: 0.77, 0.92) versus 0.73 (95% CI: 0.61, 0.84) in HR-positive/HER2-negative, 0.88 (95% CI: 0.79, 0.97) versus 0.78 (95% CI: 0.63, 0.89) in HR-positive/HER2-positive, 0.83 (95% CI not available) versus 0.75 (95% CI: 0.46, 0.81) in HR-negative/HER2-positive, and 0.82 (95% CI: 0.74, 0.91) versus 0.75 (95% CI: 0.64, 0.83) in triple negatives. Multi-feature MRI analysis improved pCR prediction over analysis of any individual feature that we examined. Additionally, the improvements in prediction were more notable when analysis was conducted according to cancer subtype.
Assessing treatment response in triple-negative breast cancer from quantitative image analysis in perfusion magnetic resonance imaging.
Banerjee Imon,Malladi Sadhika,Lee Daniela,Depeursinge Adrien,Telli Melinda,Lipson Jafi,Golden Daniel,Rubin Daniel L
Journal of medical imaging (Bellingham, Wash.)
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is sensitive but not specific to determining treatment response in early stage triple-negative breast cancer (TNBC) patients. We propose an efficient computerized technique for assessing treatment response, specifically the residual tumor (RT) status and pathological complete response (pCR), in response to neoadjuvant chemotherapy. The proposed approach is based on Riesz wavelet analysis of pharmacokinetic maps derived from noninvasive DCE-MRI scans, obtained before and after treatment. We compared the performance of Riesz features with the traditional gray level co-occurrence matrices and a comprehensive characterization of the lesion that includes a wide range of quantitative features (e.g., shape and boundary). We investigated a set of predictive models ([Formula: see text]) incorporating distinct combinations of quantitative characterizations and statistical models at different time points of the treatment and some area under the receiver operating characteristic curve (AUC) values we reported are above 0.8. The most efficient models are based on first-order statistics and Riesz wavelets, which predicted RT with an AUC value of 0.85 and pCR with an AUC value of 0.83, improving results reported in a previous study by [Formula: see text]. Our findings suggest that Riesz texture analysis of TNBC lesions can be considered a potential framework for optimizing TNBC patient care.
A computer-aided diagnosis (CAD) scheme for pretreatment prediction of pathological response to neoadjuvant therapy using dynamic contrast-enhanced MRI texture features.
Giannini Valentina,Mazzetti Simone,Marmo Agnese,Montemurro Filippo,Regge Daniele,Martincich Laura
The British journal of radiology
OBJECTIVE:To assess whether a computer-aided, diagnosis (CAD) system can predict pathological Complete Response (pCR) to neoadjuvant chemotherapy (NAC) prior to treatment using texture features. METHODS:Response to treatment of 44 patients was defined according to the histopatology of resected tumour and extracted axillary nodes in two ways: (a) pCR+ (Smith's Grade = 5) vs pCR- (Smith's Grade < 5); (b) pCRN+ (pCR+ and absence of residual lymph node metastases) vs pCRN - . A CAD system was developed to: (i) segment the breasts; (ii) register the DCE-MRI sequence; (iii) detect the lesion and (iv) extract 27 3D texture features. The role of individual texture features, multiparametric models and Bayesian classifiers in predicting patients' response to NAC were evaluated. RESULTS:A cross-validated Bayesian classifier fed with 6 features was able to predict pCR with a specificity of 72% and a sensitivity of 67%. Conversely, 2 features were used by the Bayesian classifier to predict pCRN, obtaining a sensitivity of 69% and a specificity of 61%. CONCLUSION:A CAD scheme, that extracts texture features from an automatically segmented 3D mask of the tumour, could predict pathological response to NAC. Additional research should be performed to validate these promising results on a larger cohort of patients and using different classification strategies. Advances in knowledge: This is the first study assessing the role of an automatic CAD system in predicting the pathological response to NAC before treatment. Fully automatic methods represent the backbone of standardized analysis and may help in timely managing patients candidate to NAC.
Breast cancer: early prediction of response to neoadjuvant chemotherapy using parametric response maps for MR imaging.
Cho Nariya,Im Seock-Ah,Park In-Ae,Lee Kyung-Hun,Li Mulan,Han Wonshik,Noh Dong-Young,Moon Woo Kyung
PURPOSE:To prospectively compare the performance of dynamic contrast material-enhanced (DCE) magnetic resonance (MR) imaging using parametric response map (PRM) analysis with that using pharmacokinetic parameters (transfer constant [K(trans)], rate constant [kep ], and relative extravascular extracellular space [ve]) in the early prediction of pathologic responses to neoadjuvant chemotherapy (NAC) in breast cancer patients. MATERIALS AND METHODS:The institutional review board approved this study; informed consent was obtained. Between August 2010 and December 2012, 48 women (mean age, 46.4 years; range, 29-65 years) with breast cancer were enrolled and treated with an anthracycline-taxane regimen. DCE MR imaging was performed before and after the first cycle of chemotherapy, and the pathologic response was assessed after surgery. Tumor size and volume, PRM characteristics, and pharmacokinetic parameters (K(trans), kep, and ve) on MR images were assessed and compared according to the pathologic responses by using the Fisher exact test or the independent-sample t test. RESULTS:Six of 48 (12%) patients showed pathologic complete response (CR) (pCR) and 42 (88%) showed nonpathologic CR (npCR). Thirty-eight (79%) patients showed a good response (Miller-Payne score of 3, 4, or 5), and 10 (21%) showed a minor response (Miller-Payne score of 1 or 2). The mean proportion of voxels with increased signal intensity (PRMSI+) in the pCR or good response group was significantly lower than that in the npCR or minor response group (14.0% ± 6.5 vs 40.7% ± 27.2, P < .001; 34.3% ± 26.4 vs 52.8% ± 24.9, P = .041). Area under the receiver operating characteristic curve for PRMSI+ in the pCR group was 0.770 (95% confidence interval: 0.626, 0.879), and that for the good response group was 0.716 (95% confidence interval: 0.567, 0.837). No difference in tumor size, tumor volume, or pharmacokinetic parameters was found between groups. CONCLUSION:PRM analysis of DCE MR images may enable the early identification of the pathologic response to NAC after the first cycle of chemotherapy, whereas pharmacokinetic parameters (K(trans), kep, and ve) do not.
Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set.
Cain Elizabeth Hope,Saha Ashirbani,Harowicz Michael R,Marks Jeffrey R,Marcom P Kelly,Mazurowski Maciej A
Breast cancer research and treatment
PURPOSE:To determine whether a multivariate machine learning-based model using computer-extracted features of pre-treatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict pathologic complete response (pCR) to neoadjuvant therapy (NAT) in breast cancer patients. METHODS:Institutional review board approval was obtained for this retrospective study of 288 breast cancer patients at our institution who received NAT and had a pre-treatment breast MRI. A comprehensive set of 529 radiomic features was extracted from each patient's pre-treatment MRI. The patients were divided into equal groups to form a training set and an independent test set. Two multivariate machine learning models (logistic regression and a support vector machine) based on imaging features were trained to predict pCR in (a) all patients with NAT, (b) patients with neoadjuvant chemotherapy (NACT), and (c) triple-negative or human epidermal growth factor receptor 2-positive (TN/HER2+) patients who had NAT. The multivariate models were tested using the independent test set, and the area under the receiver operating characteristics (ROC) curve (AUC) was calculated. RESULTS:Out of the 288 patients, 64 achieved pCR. The AUC values for predicting pCR in TN/HER+ patients who received NAT were significant (0.707, 95% CI 0.582-0.833, p < 0.002). CONCLUSIONS:The multivariate models based on pre-treatment MRI features were able to predict pCR in TN/HER2+ patients.
[Role of magnetic resonance imaging for response evaluation and predictive value of tumor biomarkers in the neoadjuvant chemotherapy for breast cancer:a multi-center prospective study].
Xin Ling,Liu Qian,Xu Ling,Jiang Zefei,Jiang Hongchuan,Qin Naishan,Li Ting,Duan Xuening,Liu Yinhua
Zhonghua yi xue za zhi
OBJECTIVE:To explore the value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in monitoring therapy responses and analyze the predictive value of tumor biomarkers in neoadjuvant chemotherapy for breast cancer. METHODS:From August 2010 to August 2013, the patients diagnosed as primary invasive breast cancer were admitted into this multi-center study. All of them received 6 cycles of neoadjuvant chemotherapy and DCE-MRI during the procedure and underwent surgery. The associations between clinical therapy response and pathologic response as well as predictive factors were analyzed. RESULTS:As for evaluating neoadjuvant treatment response, DCE-MRI had statistically significant correlations with histopathology. PR negativity, HER-2 over-expression and high Ki-67 index were statistically correlated with pathologic complete response (pCR) (P < 0.05). CONCLUSION:DCE-MRI is a reliable method of assessing the response of neoadjuvant therapy for breast cancer. And the immunohistochemistry status of PR, HER-2 and Ki-67 were related with pCR.
Comparison of Segmentation Methods in Assessing Background Parenchymal Enhancement as a Biomarker for Response to Neoadjuvant Therapy.
Nguyen Alex Anh-Tu,Arasu Vignesh A,Strand Fredrik,Li Wen,Onishi Natsuko,Gibbs Jessica,Jones Ella F,Joe Bonnie N,Esserman Laura J,Newitt David C,Hylton Nola M
Tomography (Ann Arbor, Mich.)
Breast parenchymal enhancement (BPE) has shown association with breast cancer risk and response to neoadjuvant treatment. However, BPE quantification is challenging, and there is no standardized segmentation method for measurement. We investigated the use of a fully automated breast fibroglandular tissue segmentation method to calculate BPE from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for use as a predictor of pathologic complete response (pCR) following neoadjuvant treatment in the I-SPY 2 TRIAL. In this trial, patients had DCE-MRI at baseline (T0), after 3 weeks of treatment (T1), after 12 weeks of treatment and between drug regimens (T2), and after completion of treatment (T3). A retrospective analysis of 2 cohorts was performed: one with 735 patients and another with a final cohort of 340 patients, meeting a high-quality benchmark for segmentation. We evaluated 3 subvolumes of interest segmented from bilateral T1-weighted axial breast DCE-MRI: full stack (all axial slices), half stack (center 50% of slices), and center 5 slices. The differences between methods were assessed, and a univariate logistic regression model was implemented to determine the predictive performance of each segmentation method. The results showed that the half stack method provided the best compromise between sampling error from too little tissue and inclusion of incorrectly segmented tissues from extreme superior and inferior regions. Our results indicate that BPE calculated using the half stack segmentation approach has potential as an early biomarker for response to treatment in the hormone receptor-negative and human epidermal growth factor receptor 2-positive subtype.
Pretreatment prediction of pathologic complete response to neoadjuvant chemotherapy in breast cancer: Perfusion metrics of dynamic contrast enhanced MRI.
Lee Jeongmin,Kim Sung Hun,Kang Bong Joo
The purpose of this study was to investigate imaging parameters predicting pathologic complete response (pCR) in pretreatment dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) in breast cancer patients who were treated with neoadjuvant chemotherapy (NAC). A total of 74 patients who received NAC followed by surgery were retrospectively reviewed. All patients underwent breast MRI before NAC. Perfusion parameters including Ktrans, Kep and Ve of tumor were measured three-dimensionally. These perfusion parameters of background parenchyma of contralateral breasts were analyzed two-dimensionally. Receiver-operating characteristic (ROC) analysis and multivariable logistic regression analysis were performed to compare the ability of perfusion parameters to predict pCR. Of 74 patients, 13 achieved pCR in final pathology. The fiftieth percentile and skewness of each perfusion parameter - Ktrans, Kep, and Ve of tumor were associated with pCR. Perfusion parameters of contralateral breast parenchyma in 2D analysis also showed predictive ability for pCR. The model combining perfusion parameters of contralateral breast background parenchyma and those of the tumor had higher predictive value than each single parameter. Thus, perfusion parameters of tumor, background parenchyma of contralateral breast and their combinations in pretreatment breast MRI allow early prediction for pCR of breast cancer.
Machine learning for prediction of chemoradiation therapy response in rectal cancer using pre-treatment and mid-radiation multi-parametric MRI.
Shi Liming,Zhang Yang,Nie Ke,Sun Xiaonan,Niu Tianye,Yue Ning,Kwong Tiffany,Chang Peter,Chow Daniel,Chen Jeon-Hor,Su Min-Ying
Magnetic resonance imaging
PURPOSE:To predict the neoadjuvant chemoradiation therapy (CRT) response in patients with locally advanced rectal cancer (LARC) using radiomics and deep learning based on pre-treatment MRI and a mid-radiation follow-up MRI taken 3-4 weeks after the start of CRT. METHODS:A total of 51 patients were included, 45 with pre-treatment, 41 with mid-radiation therapy (RT), and 35 with both MRI sets. The multi-parametric MRI protocol included T2, diffusion weighted imaging (DWI) with b-values of 0 and 800 s/mm, and dynamic-contrast-enhanced (DCE) MRI. After completing CRT and surgery, the specimen was examined to determine the pathological response based on the tumor regression grade. The tumor ROI was manually drawn on the post-contrast image and mapped to other sequences. The total tumor volume and mean apparent diffusion coefficient (ADC) were measured. Radiomics using GLCM texture and histogram parameters, and deep learning using a convolutional neural network (CNN), were performed to differentiate pathologic complete response (pCR) vs. non-pCR, and good response (GR) vs. non-GR. RESULTS:Tumor volume decreased and ADC increased significantly in the mid-RT MRI compared to the pre-treatment MRI. For predicting pCR vs. non-pCR, combining ROI and radiomics features achieved an AUC of 0.80 for pre-treatment, 0.82 for mid-RT, and 0.86 for both MRI together. For predicting GR vs. non-GR, the AUC was 0.91 for pre-treatment, 0.92 for mid-RT, and 0.93 for both MRI together. In deep learning using CNN, combining pre-treatment and mid-RT MRI achieved a higher accuracy compared to using either dataset alone, with AUC of 0.83 for predicting pCR vs. non-pCR. CONCLUSION:Radiomics based on pre-treatment and early follow-up multi-parametric MRI in LARC patients receiving CRT could extract comprehensive quantitative information to predict final pathologic response.
Multiparametric magnetic resonance imaging for predicting pathological response after the first cycle of neoadjuvant chemotherapy in breast cancer.
Li Xia,Abramson Richard G,Arlinghaus Lori R,Kang Hakmook,Chakravarthy Anuradha Bapsi,Abramson Vandana G,Farley Jaime,Mayer Ingrid A,Kelley Mark C,Meszoely Ingrid M,Means-Powell Julie,Grau Ana M,Sanders Melinda,Yankeelov Thomas E
OBJECTIVES:The purpose of this study was to determine whether multiparametric magnetic resonance imaging (MRI) using dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted MRI (DWI), obtained before and after the first cycle of neoadjuvant chemotherapy (NAC), is superior to single-parameter measurements for predicting pathologic complete response (pCR) in patients with breast cancer. MATERIALS AND METHODS:Patients with stage II/III breast cancer were enrolled in an institutional review board-approved study in which 3-T DCE-MRI and DWI data were acquired before (n = 42) and after 1 cycle (n = 36) of NAC. Estimates of the volume transfer rate (K), extravascular extracellular volume fraction (ve), blood plasma volume fraction (vp), and the efflux rate constant (kep = K/ve) were generated from the DCE-MRI data using the Extended Tofts-Kety model. The apparent diffusion coefficient (ADC) was estimated from the DWI data. The derived parameter kep/ADC was compared with single-parameter measurements for its ability to predict pCR after the first cycle of NAC. RESULTS:The kep/ADC after the first cycle of NAC discriminated patients who went on to achieve a pCR (P < 0.001) and achieved a sensitivity, specificity, positive predictive value, and area under the receiver operator curve (AUC) of 0.92, 0.78, 0.69, and 0.88, respectively. These values were superior to the single parameters kep (AUC, 0.76) and ADC (AUC, 0.82). The AUCs between kep/ADC and kep were significantly different on the basis of the bootstrapped 95% confidence intervals (0.018-0.23), whereas the AUCs between kep/ADC and ADC trended toward significance (-0.11 to 0.24). CONCLUSIONS:The multiparametric analysis of DCE-MRI and DWI was superior to the single-parameter measurements for predicting pCR after the first cycle of NAC.
Dynamic contrast-enhanced magnetic resonance imaging and diffusion-weighted magnetic resonance imaging for predicting the response of locally advanced breast cancer to neoadjuvant therapy: a meta-analysis.
Virostko John,Hainline Allison,Kang Hakmook,Arlinghaus Lori R,Abramson Richard G,Barnes Stephanie L,Blume Jeffrey D,Avery Sarah,Patt Debra,Goodgame Boone,Yankeelov Thomas E,Sorace Anna G
Journal of medical imaging (Bellingham, Wash.)
This meta-analysis assesses the prognostic value of quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted MRI (DW-MRI) performed during neoadjuvant therapy (NAT) of locally advanced breast cancer. A systematic literature search was conducted to identify studies of quantitative DCE-MRI and DW-MRI performed during breast cancer NAT that report the sensitivity and specificity for predicting pathological complete response (pCR). Details of the study population and imaging parameters were extracted from each study for subsequent meta-analysis. Metaregression analysis, subgroup analysis, study heterogeneity, and publication bias were assessed. Across 10 studies that met the stringent inclusion criteria for this meta-analysis (out of 325 initially identified studies), we find that MRI had a pooled sensitivity of 0.91 [95% confidence interval (CI), 0.80 to 0.96] and specificity of 0.81(95% CI, 0.68 to 0.89) when adjusted for covariates. Quantitative DCE-MRI exhibits greater specificity for predicting pCR than semiquantitative DCE-MRI ([Formula: see text]). Quantitative DCE-MRI and DW-MRI are able to predict, early in the course of NAT, the eventual response of breast tumors, with a high level of specificity and sensitivity. However, there is a high degree of heterogeneity in published studies highlighting the lack of standardization in the field.
Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using a deep learning (DL) method.
Qu Yu-Hong,Zhu Hai-Tao,Cao Kun,Li Xiao-Ting,Ye Meng,Sun Ying-Shi
BACKGROUND:The aim of the study was to develop a deep learning (DL) algorithm to evaluate the pathological complete response (pCR) to neoadjuvant chemotherapy in breast cancer. METHODS:A total of 302 breast cancer patients in this retrospective study were randomly divided into a training set (n = 244) and a validation set (n = 58). Tumor regions were manually delineated on each slice by two expert radiologists on enhanced T1-weighted images. Pathological results were used as ground truth. Deep learning network contained five repetitions of convolution and max-pooling layers and ended with three dense layers. The pre-NAC model and post-NAC model inputted six phases of pre-NAC and post-NAC images, respectively. The combined model used 12 channels from six phases of pre-NAC and six phases of post-NAC images. All models above included three indexes of molecular type as one additional input channel. RESULTS:The training set contained 137 non-pCR and 107 pCR participants. The validation set contained 33 non-pCR and 25 pCR participants. The area under the receiver operating characteristic (ROC) curve (AUC) of three models was 0.553 for pre-NAC, 0.968 for post-NAC and 0.970 for the combined data, respectively. A significant difference was found in AUC between using pre-NAC data alone and combined data (P < 0.001). The positive predictive value of the combined model was greater than that of the post-NAC model (100% vs. 82.8%, P = 0.033). CONCLUSION:This study established a deep learning model to predict PCR status after neoadjuvant therapy by combining pre-NAC and post-NAC MRI data. The model performed better than using pre-NAC data only, and also performed better than using post-NAC data only. KEY POINTS:Significant findings of the study. It achieved an AUC of 0.968 for pCR prediction. It showed a significantly greater AUC than using pre-NAC data only. What this study adds This study established a deep learning model to predict PCR status after neoadjuvant therapy by combining pre-NAC and post-NAC MRI data.
Combining Dynamic Contrast-Enhanced Magnetic Resonance Imaging and Apparent Diffusion Coefficient Maps for a Radiomics Nomogram to Predict Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Patients.
Chen Xiangguang,Chen Xiaofeng,Yang Jiada,Li Yulin,Fan Weixiong,Yang Zhiqi
Journal of computer assisted tomography
OBJECTIVE:The objective of this study was to develop a nomogrom for prediction of pathological complete response (PCR) to neoadjuvant chemotherapy in breast cancer patients. METHODS:Ninety-one patients were analyzed. A total of 396 radiomics features were extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and apparent diffusion coefficient (ADC) maps. The least absolute shrinkage and selection operator was selected for data dimension reduction to build a radiomics signature. Finally, the nomogram was built to predict PCR. RESULTS:The radiomics signature of the model that combined DCE-MRI and ADC maps showed a higher performance (area under the receiver operating characteristic curve [AUC], 0.848) than the models with DCE-MRI (AUC, 0.750) or ADC maps (AUC, 0.785) alone in the training set. The proposed model, which included combined radiomics signature, estrogen receptor, and progesterone receptor, yielded a maximum AUC of 0.837 in the testing set. CONCLUSIONS:The combined radiomics features from DCE-MRI and ADC data may serve as potential predictor markers for predicting PCR. The nomogram could be used as a quantitative tool to predict PCR.
Response to neoadjuvant chemoradiotherapy for locally advanced rectum cancer: Texture analysis of dynamic contrast-enhanced MRI.
Zou Hai-Hua,Yu Jing,Wei Yun,Wu Jiang-Fen,Xu Qing
Journal of magnetic resonance imaging : JMRI
BACKGROUND:Tumor heterogeneity can be assessed by texture analysis (TA). TA has been applied using diffusion-weighted imaging and apparent diffusion coefficient maps to predict pathological responses to preoperative chemoradiation therapy (CRT) in patients with locally advanced rectal cancer (LARC). PURPOSE:To evaluate the texture parameters obtained from K maps derived from dynamic contrast-enhanced (DCE)-MRI for predicting pathological responses to preoperative CRT for LARCs. STUDY TYPE:Retrospective. POPULATION:Altogether, 83 patients (26 women, 57 men) with rectal cancer met the inclusion criteria. FIELD STRENGTH/SEQUENCE:3.0T/T -weighted DCE-MRI sequence. ASSESSMENT:After CRT, each tumor was assessed by a pathologist who assigned a tumor regression grade (TRG), thereby identifying pathologically complete responders (pCR; TRG 1) and good responders (GR; TRG1 + TRG2). TA was then applied to the DCE-MRI K maps. The K value, several TA parameters, and tumor volumes were calculated. STATISTICAL TESTS:The Shapiro-Wilk test was used to verify that the data had normal distribution. Results of parameters measured before and after CRT were compared using paired-sample t-tests. Value changes of each parameter in the combined pCR/GR group were compared using independent sample t-tests. Receiver operating characteristic curves and areas under the curve (AUC) were calculated to assess the diagnostic performance of each parameter related to CRT effectiveness. RESULTS:There were 15 pCR (16.9%) and 21 GR (25.3%) patients. Tumor volume, mean K , entropy, and correlation decreased and energy values increased significantly in these groups compared with those of the non-PCR and non-GR groups. ΔCorrelation (Δcorrelation = postcorrelation - precorrelation) was found to be a valuable parameter for identifying pCR/GR patients (AUC 0.895, sensitivity 86.7%, specificity 81.8%). DATA CONCLUSION:TA parameters from the DCE-MRI K map can predict the efficacy of CRT for treating LARCs. Also, Δcorrelation may be useful for identifying patients who will be responsive to CRT. LEVEL OF EVIDENCE:4 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;49:885-893.
Dynamic contrast-enhanced MRI: Use in predicting pathological complete response to neoadjuvant chemoradiation in locally advanced rectal cancer.
Tong Tong,Sun Yiqun,Gollub Marc J,Peng Weijun,Cai Sanjun,Zhang Zhen,Gu Yajia
Journal of magnetic resonance imaging : JMRI
PURPOSE:To determine the ability of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict pathological complete response (pCR) before preoperative chemoradiotherapy (CRT) in locally advanced rectal cancer. MATERIALS AND METHODS:In a prospective clinical trial, 38 enrolled patients underwent pre- and post-CRT DCE-MRI at 3.0T. The tumor length and the following perfusion parameters (K(trans) , kep , ve ) were measured for the tumor and compared between the pCR group and the non-pCR group, as well as before and after CRT. For categorical variable comparison, the Kruskal-Wallis test was used. P < 0.05 was considered significant. RESULTS:No difference in tumor length was found between the pCR and non-pCR group pre- and post-CRT (P = 0.26 (0.15,0.45), 0.35 (0.21,0.52), respectively). Before CRT, the mean tumor K(trans) in the pCR group was significantly higher than in the non-pCR group (P = 0.01). A K(trans) of 0.66 emerged as the best cutoff for distinguishing pCR from non-pCR. Regarding kep and ve , significant differences were also observed between the pCR and non-pCR groups (P = 0.02, 0.01, respectively). The mean K(trans) , kep , and ve values post-CRT were lower in the pCR group than in the non-pCR group, although there was no significant difference (P = 0.10 (0.04,0.16), 0.11 (0.07,0.26), 0.10 (0.06,0.23), respectively). CONCLUSION:Before neoadjuvant chemoradiotherapy in rectal cancer, DCE-MRI can distinguish between complete and incomplete response using a K(trans) threshold of 0.66 with a sensitivity of 100%.
Multi-input deep learning architecture for predicting breast tumor response to chemotherapy using quantitative MR images.
El Adoui Mohammed,Drisis Stylianos,Benjelloun Mohammed
International journal of computer assisted radiology and surgery
PURPOSE:Neoadjuvant chemotherapy (NAC) aims to minimize the tumor size before surgery. Predicting response to NAC could reduce toxicity and delays to effective intervention. Computational analysis of dynamic contrast-enhanced magnetic resonance images (DCE-MRI) through deep convolution neural network (CNN) has shown a significant performance to distinguish responders and no responder's patients. This study intends to present a new deep learning (DL) model predicting the breast cancer response to NAC based on multiple MRI inputs. METHODS:A cohort of 723 axial slices extracted from 42 breast cancer patients who underwent NAC therapy was used to train and validate the developed DL model. This dataset was provided by our collaborator institute of radiology in Brussels. Fourteen external cases were used to validate the best obtained model to predict pCR based on pre- and post-chemotherapy DCE-MRI. The model performance was assessed by area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and feature map visualization. RESULTS:The developed multi-inputs deep learning architecture was able to predict the pCR to NAC treatment in the validation dataset with an AUC of 0.91 using combined pre- and post-NAC images. The visual results showed that the most important extracted features from non-pCR tumors are in the peripheral region. The proposed method was more productive than the previous ones. CONCLUSION:Even with a limited training dataset size, the proposed and developed CNN model using DCE-MR images acquired before and after the first chemotherapy was able to classify pCR and non-pCR patients with substantial accuracy. This model could be used hereafter in clinical analysis after its evaluation based on more extra data.
Diagnostic performance of contrast-enhanced dynamic and diffusion-weighted MR imaging in the assessment of tumor response to neoadjuvant therapy in muscle-invasive bladder cancer.
Ahmed Shimaa Abdalla,Taher Mohamed Gamal Ameen,Ali Wageeh A,Ebrahem Mohamed Abd El Salam
Abdominal radiology (New York)
OBJECTIVES:To evaluate the diagnostic performance of DCE MRI and DWI in the assessment of pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in patients with muscle-invasive bladder cancer (MIBC). METHODS:This prospective study included 90 patients with MIBC who finished NAC. Two radiologists independently assessed MRI for the determination of semi-quantitative parameters (wash-in rate and wash-out rate) and apparent diffusion coefficient (ADC) value. The correlation between pCR and wash-in rate, wash-out rate, ADC value were analyzed. The area under the ROC curve (AUC) was used to evaluate the diagnostic performance for detecting pCR. Inter-reader agreement was assessed using the ICC statistics. RESULTS:On cystectomy specimens, pCR was confirmed in (43.3%, 39/90). pCR is negatively correlated with wash-out rate (r = - 0.701, p = 0.01) and ADC value (r = - 0.621, p = 0.01). ADC value is positively correlated with wash-out rate (r = 0.631, p = 0.001). The diagnostic accuracy of ADC value (cut-off value: 0.911 × 10mm/s) and wash-out rate (cut-off value: 0.677 min) in the identification of pCR was (92% for reader 1, 91% for reader 2), and (90% for reader 1, 88% for reader 2), respectively. The sensitivity, specificity for predicting pCR using ADC value + washout rate cut off values were 95.4%, 97.7% for reader 1, and 96%, 97% for reader 2, respectively. AUC was 0.981 for reader 1, 0.971 for reader 2. The overall reproducibility of the mean ADC value and wash out rate was excellent (ICC = 0.83-0.90). The ICC values for the mean ADC value, washout rate was 0.89 (95% CI 0.84-0.89) and 0.87 (95% CI 0.86-0.91), respectively. CONCLUSION:Semi-quantitative parameter (wash-out) derived from DCE-MRI and ADC has the potential to assess the tumor's complete pathologic response. The two parameters using together can offer the best possibility to identify complete response to NAC in MIBC.
Prediction of pathological complete response after neoadjuvant chemotherapy in breast cancer: comparison of diagnostic performances of dedicated breast PET, whole-body PET, and dynamic contrast-enhanced MRI.
Tokuda Yukiko,Yanagawa Masahiro,Fujita Yuka,Honma Keiichiro,Tanei Tomonori,Shimoda Masafumi,Miyake Tomohiro,Naoi Yasuto,Kim Seung Jin,Shimazu Kenzo,Hamada Seiki,Tomiyama Noriyuki
Breast cancer research and treatment
PURPOSE:To compare the diagnostic performance of ring-type dedicated breast PET (dbPET), whole-body PET (WBPET), and DCE-MRI for predicting pathological complete response (pCR) after neoadjuvant chemotherapy (NAC). METHODS:This prospective study included 29 women with histologically proven breast cancer on needle biopsy between July 2016 and July 2019 (age: mean 55 years; range 35-78). Patients underwent WBPET followed by ring-type dbPET and DCE-MRI pre- and post-NAC for preoperative evaluation. pCR was defined as an invasive tumor that disappeared in the breast. Standardized uptake values corrected for lean body mass (SULpeak) were calculated for dbPET and WBPET scans. Maximum tumor length was measured in DCE-MRI images. Reduction rates were calculated for quantitative evaluation. Two radiologists independently evaluated the qualitative findings. Reduction rates and qualitative findings were compared between the pCR (n = 7) and non-pCR (n = 22) groups for each modality. Differences in quantitative and qualitative data between the two groups were analyzed statistically. RESULTS:Significant differences were observed in the reduction rates of dbPET and DCE-MRI (P = 0.01 and 0.03, respectively) between the two groups. Univariate and multiple logistic regression analyses revealed that SULpeak reduction rates in WBPET and dbPET (P = 0.02 and P = 0.01, respectively) and in dbPET (odds ratio, 16.00; 95% CI 1.57-162.10; P = 0.01) were significant indicators associated with pCR, respectively. No between-group differences were observed in qualitative findings in the three modalities. CONCLUSION:SULpeak reduction rate of dbPET > 82% was an independent indicator associated with pCR after NAC in breast cancer.
Impact of Machine Learning With Multiparametric Magnetic Resonance Imaging of the Breast for Early Prediction of Response to Neoadjuvant Chemotherapy and Survival Outcomes in Breast Cancer Patients.
Tahmassebi Amirhessam,Wengert Georg J,Helbich Thomas H,Bago-Horvath Zsuzsanna,Alaei Sousan,Bartsch Rupert,Dubsky Peter,Baltzer Pascal,Clauser Paola,Kapetas Panagiotis,Morris Elizabeth A,Meyer-Baese Anke,Pinker Katja
PURPOSE:The aim of this study was to assess the potential of machine learning with multiparametric magnetic resonance imaging (mpMRI) for the early prediction of pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) and of survival outcomes in breast cancer patients. MATERIALS AND METHODS:This institutional review board-approved prospective study included 38 women (median age, 46.5 years; range, 25-70 years) with breast cancer who were scheduled for NAC and underwent mpMRI of the breast at 3 T with dynamic contrast-enhanced (DCE), diffusion-weighted imaging (DWI), and T2-weighted imaging before and after 2 cycles of NAC. For each lesion, 23 features were extracted: qualitative T2-weighted and DCE-MRI features according to BI-RADS (Breast Imaging Reporting and Data System), quantitative pharmacokinetic DCE features (mean plasma flow, volume distribution, mean transit time), and DWI apparent diffusion coefficient (ADC) values. To apply machine learning to mpMRI, 8 classifiers including linear support vector machine, linear discriminant analysis, logistic regression, random forests, stochastic gradient descent, decision tree, adaptive boosting, and extreme gradient boosting (XGBoost) were used to rank the features. Histopathologic residual cancer burden (RCB) class (with RCB 0 being a pCR), recurrence-free survival (RFS), and disease-specific survival (DSS) were used as the standards of reference. Classification accuracy with area under the receiving operating characteristic curve (AUC) was assessed using all the extracted qualitative and quantitative features for pCR as defined by RCB class, RFS, and DSS using recursive feature elimination. To overcome overfitting, 4-fold cross-validation was used. RESULTS:Machine learning with mpMRI achieved stable performance as shown by mean classification accuracies for the prediction of RCB class (AUC, 0.86) and DSS (AUC, 0.92) based on XGBoost and the prediction of RFS (AUC, 0.83) with logistic regression. The XGBoost classifier achieved the most stable performance with high accuracies compared with other classifiers. The most relevant features for the prediction of RCB class were as follows: changes in lesion size, complete pattern of shrinkage, and mean transit time on DCE-MRI; minimum ADC on DWI; and peritumoral edema on T2-weighted imaging. The most relevant features for prediction of RFS were as follows: volume distribution, mean plasma flow, and mean transit time; DCE-MRI lesion size; minimum, maximum, and mean ADC with DWI. The most relevant features for prediction of DSS were as follows: lesion size, volume distribution, and mean plasma flow on DCE-MRI, and maximum ADC with DWI. CONCLUSIONS:Machine learning with mpMRI of the breast enables early prediction of pCR to NAC as well as survival outcomes in breast cancer patients with high accuracy and thus may provide valuable predictive information to guide treatment decisions.
Functional Tumor Volume by Fast Dynamic Contrast-Enhanced MRI for Predicting Neoadjuvant Systemic Therapy Response in Triple-Negative Breast Cancer.
Musall Benjamin C,Abdelhafez Abeer H,Adrada Beatriz E,Candelaria Rosalind P,Mohamed Rania M M,Boge Medine,Le-Petross Huong,Arribas Elsa,Lane Deanna L,Spak David A,Leung Jessica W T,Hwang Ken-Pin,Son Jong Bum,Elshafeey Nabil A,Mahmoud Hagar S,Wei Peng,Sun Jia,Zhang Shu,White Jason B,Ravenberg Elizabeth E,Litton Jennifer K,Damodaran Senthil,Thompson Alastair M,Moulder Stacy L,Yang Wei T,Pagel Mark D,Rauch Gaiane M,Ma Jingfei
Journal of magnetic resonance imaging : JMRI
BACKGROUND:Dynamic contrast-enhanced (DCE) MRI is useful for diagnosis and assessment of treatment response in breast cancer. Fast DCE MRI offers a higher sampling rate of contrast enhancement curves in comparison to conventional DCE MRI, potentially characterizing tumor perfusion kinetics more accurately for measurement of functional tumor volume (FTV) as a predictor of treatment response. PURPOSE:To investigate FTV by fast DCE MRI as a predictor of neoadjuvant systemic therapy (NAST) response in triple-negative breast cancer (TNBC). STUDY TYPE:Prospective. POPULATION/SUBJECTS:Sixty patients with biopsy-confirmed TNBC between December 2016 and September 2020. FIELD STRENGTH/SEQUENCE:A 3.0 T/3D fast spoiled gradient echo-based DCE MRI ASSESSMENT: Patients underwent MRI at baseline and after four cycles (C4) of NAST, followed by definitive surgery. DCE subtraction images were analyzed in consensus by two breast radiologists with 5 (A.H.A.) and 2 (H.S.M.) years of experience. Tumor volumes (TV) were measured on early and late subtractions. Tumors were segmented on 1 and 2.5-minute early phases subtractions and FTV was determined using optimized signal enhancement thresholds. Interpolated enhancement curves from segmented voxels were used to determine optimal early phase timing. STATISTICAL TESTS:Tumor volumes were compared between patients who had a pathologic complete response (pCR) and those who did not using the area under the receiver operating curve (AUC) and Mann-Whitney U test. RESULTS:About 26 of 60 patients (43%) had pCR. FTV at 1 minute after injection at C4 provided the best discrimination between pCR and non-pCR, with AUC (95% confidence interval [CI]) = 0.85 (0.74,0.95) (P < 0.05). The 1-minute timing was optimal for FTV measurements at C4 and for the change between C4 and baseline. TV from the early phase at C4 also yielded a good AUC (95%CI) of 0.82 (0.71,0.93) (P < 0.05). DATA CONCLUSION:FTV and TV measured at 1 minute after injection can predict response to NAST in TNBC. LEVEL OF EVIDENCE:1 TECHNICAL EFFICACY: 4.
Analyzing Spatial Heterogeneity in DCE- and DW-MRI Parametric Maps to Optimize Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer.
Li Xia,Kang Hakmook,Arlinghaus Lori R,Abramson Richard G,Chakravarthy A Bapsi,Abramson Vandana G,Farley Jaime,Sanders Melinda,Yankeelov Thomas E
The purpose of this study is to investigate the ability of multivariate analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted MRI (DW-MRI) parametric maps, obtained early in the course of therapy, to predict which patients will achieve pathologic complete response (pCR) at the time of surgery. Thirty-three patients underwent DCE-MRI (to estimate K (trans), v e, k ep, and v p) and DW-MRI [to estimate the apparent diffusion coefficient (ADC)] at baseline (t 1) and after the first cycle of neoadjuvant chemotherapy (t 2). Four analyses were performed and evaluated using receiver-operating characteristic (ROC) analysis to test their ability to predict pCR. First, a region of interest (ROI) level analysis input the mean K (trans), v e, k ep, v p, and ADC into the logistic model. Second, a voxel-based analysis was performed in which a longitudinal registration algorithm aligned serial parameters to a common space for each patient. The voxels with an increase in k ep, K (trans), and v p or a decrease in ADC or v e were then detected and input into the regression model. In the third analysis, both the ROI and voxel level data were included in the regression model. In the fourth analysis, the ROI and voxel level data were combined with selected clinical data in the regression model. The overfitting-corrected area under the ROC curve (AUC) with 95% confidence intervals (CIs) was then calculated to evaluate the performance of the four analyses. The combination of k ep, ADC ROI, and voxel level data achieved the best AUC (95% CI) of 0.87 (0.77-0.98).
Texture Analysis of Dynamic Contrast-Enhanced MRI in Evaluating Pathologic Complete Response (pCR) of Mass-Like Breast Cancer after Neoadjuvant Therapy.
Cao Kun,Zhao Bo,Li Xiao-Ting,Li Yan-Ling,Sun Ying-Shi
Journal of oncology
Objectives:MRI is the standard imaging method in evaluating treatment response of breast cancer after neoadjuvant therapy (NAT), while identification of pathologic complete response (pCR) remains challenging. Texture analysis (TA) on post-NAT dynamic contrast-enhanced (DCE) MRI was explored to assess the existence of pCR in mass-like cancer. Materials and Methods:A primary cohort of 112 consecutive patients (40 pCR and 72 non-pCR) with mass-like breast cancers who received preoperative NAT were retrospectively enrolled. On post-NAT MRI, volumes of the residual-enhanced areas and TA first-order features (19 for each sequence) of the corresponding areas were achieved for both early- and late-phase DCE using an in-house radiomics software. Groups were divided according to the operational pathology. Receiver operating characteristic curves and binary logistic regression analysis were used to select features and achieve a predicting formula. Overall diagnostic abilities were compared between TA and radiologists' subjective judgments. Validation was performed on a time-independent cohort of 39 consecutive patients. Results:TA features with high consistency (Cronbach's alpha >0.9) between 2 observers showed significant differences between pCR and non-pCR groups. Logistic regression using features selected by ROC curves generated a synthesized formula containing 3 variables (volume of residual enhancement, entropy, and robust mean absolute deviation from early-phase) to yield AUC = 0.81, higher than that of using radiologists' subjective judgment (AUC = 0.72), and entropy was an independent risk factor ( < 0.001). Accuracy and sensitivity for identifying pCR were 83.93% and 70.00%. AUC of the validation cohort was 0.80. Conclusions:TA may help to improve the diagnostic ability of post-NAT MRI in identifying pCR in mass-like breast cancer. Entropy, as a first-order feature to depict residual tumor heterogeneity, is an important factor.
Combined T2w volumetry, DW-MRI and DCE-MRI for response assessment after neo-adjuvant chemoradiation in locally advanced rectal cancer.
Intven Martijn,Monninkhof Evelyn M,Reerink Onne,Philippens Marielle E P
Acta oncologica (Stockholm, Sweden)
BACKGROUND:To assess the value of combined T2-weighted magnetic resonance imaging (MRI) (T2w) volumetry, diffusion-weighted (DW)-MRI and dynamic contrast enhanced (DCE)-MRI for pathological response prediction after neo-adjuvant chemoradiation (CRT) in locally advanced rectal cancer (LARC). MATERIAL AND METHODS:MRI with DW-MRI and DCE-MRI sequences was performed before start of CRT and before surgery. After surgery, the tumor regression grade (TRG) was obtained based on the score by Mandard et al. Pathological complete responders (pCR, TRG 1), and pathological good responders (GR, TRG 1 + 2) were compared to non-pCR and non-GR patients, respectively. RESULTS:In total 55 patients were analyzed, six had a pCR (10.9%) and 10 a GR (18.2%). Favorable responders had a larger decrease in tumor volume and Ktrans and a larger increase in apparent diffusion coefficient (ADC) values compared to non-responders. ADC change showed the best diagnostic accuracy for pCR. For GR, the model including ADC change and volume change showed the best diagnostic performance. However, this performance was not statistically better compared to the model with ADC change alone. Inclusion of Ktrans change did not increase the diagnostic accuracy for pathological favorable response. CONCLUSIONS:This explorative study showed that ADC change is a promising diagnostic tool for pCR and GR. Volume decrease showed potential limited additional diagnostic value for GR while Ktrans change showed no additional diagnostic value for pCR and GR.
Predictive value of DCE-MRI for early evaluation of pathological complete response to neoadjuvant chemotherapy in resectable primary breast cancer: A single-center prospective study.
Sun Ying-Shi,He Ying-Jian,Li Jie,Li Yan-Ling,Li Xiao-Ting,Lu Ai-Ping,Fan Zhao-Qing,Cao Kun,Ouyang Tao
Breast (Edinburgh, Scotland)
OBJECTIVE:This study proposed to establish a predictive model using dynamic enhanced MRI multi-parameters for early predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer. METHODS:In this prospective cohort study, 170 breast cancer patients treated with NAC were enrolled and were randomly grouped into training sample (136 patients) and validation sample (34 patients). DCE-MRI parameters achieved at the end of the first cycle of NAC were screened to establish the predictive model by using multivariate logistic regression model according to pCR status. Receiver operating characteristic curves were conducted to assess the predictive capability. The association between MRI-predicted pCR and actual pCR in survival outcomes was estimated by using the Kaplan-Meier method with log-rank test. RESULTS:Multivariate analysis showed ΔAreamax and ΔSlopemax were independent predictors for pCR, odds ratio were 0.939 (95%CI, 0.915 to 0.964), and 0.966 (95%CI, 0.947 to 0.986), respectively. A predictive model was established using training sample as "Y = -0.063*ΔAreamax - 0.034*ΔSlopemax", a cut-off point of 3.0 was determined. The AUC for training and validation sample were 0.931 (95%CI, 0.890-0.971) and 0.971 (95%CI, 0.923-1.000), respectively. MRI-predicted pCR patients showed similar RFS (p = 0.347), DDFS (p = 0.25) and OS (p = 0.423) with pCR patients. CONCLUSION:The multi-parameter MRI model can be potentially used for early prediction of pCR status at the end of the first NAC cycle, which might allow timely regimen refinement before definitive surgical treatment.
Early assessment of breast cancer response to neoadjuvant chemotherapy by semi-quantitative analysis of high-temporal resolution DCE-MRI: preliminary results.
Abramson Richard G,Li Xia,Hoyt Tamarya Lea,Su Pei-Fang,Arlinghaus Lori R,Wilson Kevin J,Abramson Vandana G,Chakravarthy A Bapsi,Yankeelov Thomas E
Magnetic resonance imaging
PURPOSE:To evaluate whether semi-quantitative analysis of high temporal resolution dynamic contrast-enhanced MRI (DCE-MRI) acquired early in treatment can predict the response of locally advanced breast cancer (LABC) to neoadjuvant chemotherapy (NAC). MATERIALS AND METHODS:As part of an IRB-approved prospective study, 21 patients with LABC provided informed consent and underwent high temporal resolution 3T DCE-MRI before and after 1cycle of NAC. Using measurements performed by two radiologists, the following parameters were extracted for lesions at both examinations: lesion size (short and long axes, in both early and late phases of enhancement), radiologist's subjective assessment of lesion enhancement, and percentages of voxels within the lesion demonstrating progressive, plateau, or washout kinetics. The latter data were calculated using two filters, one selecting for voxels enhancing ≥50% over baseline and one for voxels enhancing ≥100% over baseline. Pretreatment imaging parameters and parameter changes following cycle 1 of NAC were evaluated for their ability to discriminate patients with an eventual pathological complete response (pCR). RESULTS:All 21 patients completed NAC followed by surgery, with 9 patients achieving a pCR. No pretreatment imaging parameters were predictive of pCR. However, change after cycle 1 of NAC in percentage of voxels demonstrating washout kinetics with a 100% enhancement filter discriminated patients with an eventual pCR with an area under the receiver operating characteristic curve (AUC) of 0.77. Changes in other parameters, including lesion size, did not predict pCR. CONCLUSION:Semi-quantitative analysis of high temporal resolution DCE-MRI in patients with LABC can discriminate patients with an eventual pCR after one cycle of NAC.
Predictors of tumor response after preoperative chemoradiotherapy for rectal adenocarcinomas.
Guedj Nathalie,Bretagnol Frédéric,Rautou Pierre-Emmanuel,Deschamps Lydia,Cazals-Hatem Dominique,Bedossa Pierre,Panis Yves,Couvelard Anne
The ability to predict response after chemoradiotherapy in rectal adenocarcinoma may allow selecting patients to whom less invasive surgical treatment could be proposed. Tumor hypoxia has been implicated in the mechanisms of resistance to chemoradiotherapy in several malignancies. The aim was to identify morphological criteria and molecular markers of hypoxia associated with chemoradiotherapy response. Clinicopathologic data from 61 patients (35 male, 60.5 ± 10 years) undergoing rectal cancer resection after neoadjuvant chemoradiotherapy were collected. Pretreatment biopsies, available for 40 patients, were immunostained for hypoxia markers (carbonic anhydrase 9, glucose transporter 1, chemokine receptor 4) and microvascular density determination. Mean tumor size was 2.7 ± 1.6 cm. Twenty-one patients (34%) were considered as responders, that is, having significant or complete primary tumor regression without lymph node metastasis. Compared to other patients, responders had significantly more often flat tumors with or without ulceration (57% versus 18%, P = .01) and less vascular and/or neural invasions (9% versus 65%, P < .0001) or tumor necrosis (9% versus 41%, P < .01), respectively. Regarding pretreatment biopsies, carbonic anhydrase 9 expression was significantly lower in responders (7% versus 46%, P = .012). This study showed that tumor necrosis as an overexpression of carbonic anhydrase 9 was an effective molecular marker of postchemoradiotherapy response. This might suggest a key role of hypoxia in resistance mechanisms of chemoradiotherapy in rectal adenocarcinoma. This study highlighted the importance of predictive criteria to chemoradiotherapy response in proposing to selected patients an alternative treatment (eg, local resection) to more radical surgery.
The relationship of the neo-angiogenic marker, endoglin, with response to neoadjuvant chemotherapy in breast cancer.
Beresford M J,Harris A L,Ah-See M,Daley F,Padhani A R,Makris A
British journal of cancer
Endoglin (CD105) is upregulated in endothelial cells of tissues undergoing neovascularisation. A greater number of CD105-positive vessels predicts poor survival in breast cancer. We examine whether CD105 expression predicts response to neoadjuvant chemotherapy. Fifty-seven women (median age 50 years, range 29-70) received neoadjuvant chemotherapy for operable breast cancer. Immunohistochemical staining using monoclonal antibodies to CD105 and CD34 was performed on pretreatment biopsies and post-treatment surgical specimens. Individual microvessels were counted in 10 random fields at x 200 magnification. Median counts were correlated with clinical and pathological response using the Mann-Whitney U-test. Forty-five out of fifty-seven patients (79%) responded clinically, 22 (39%) responded pathologically. On pretreatment biopsies, clinical responders had significantly lower median CD105-positive vessel counts than nonresponders (median counts 5 and 9.3/high-power field (hpf), median difference=4.0/hpf, 95% CI 0.5-8.0/hpf, P=0.02). For pathological responders and nonresponders, median counts were 4.8 and 5.5/hpf (median difference -0.5/hpf, 95% CI=-2.5-2.0/hpf, P=0.77). CD34 expression (total microvessel density) did not correlate with response. Pretreatment CD105 expression predicts for clinical response to chemotherapy, with a lower initial count being favourable. Patients with high baseline new vessel counts or increased counts after conventional therapy may benefit from additional antiangiogenic therapy.
Microvessel density in advanced head and neck squamous cell carcinoma before and after chemotherapy.
Dunphy Frank,Stack Brendan C,Boyd James H,Dunleavy Teresa L,Kim Han J,Dunphy Cherie H
OBJECTIVE:Studies of tumor angiogenesis in head and neck squamous cell carcinoma (HNSCC) in regard to correlation with prognostic significance have yielded inconclusive results. To determine whether the microvessel density (MVD) within the tumor of advanced (Stages III and IV) HNSCC has any impact on tumor response to 2-3 courses of paclitaxel (Taxol) and carboplatin, we prospectively studied pre-chemotherapy specimens from patients with previously untreated, advanced stage HNSCC. We also attempted to study residual tumors after chemotherapy to determine if the MVD within the tumor had changed. STUDY DESIGN:The MVD within the tumor was obtained by immunohistochemical staining of the tumors with Q-Bend 10 (CD34). The "hot-spot" areas of each tumor (ie., areas with most intense blood neovascularization) were considered for evaluation. Results were expressed as the Average number of microvessels identified in 5-400x microscope fields (ie., the number of microvessels counted in 5-400x microscope fields divided by 5). SETTING:Tertiary University Medical Center. INTERVENTION:Two to 3 courses of chemotherapy with paclitaxel and carboplatin. MAIN OUTCOME MEASURES:Progression-free and overall survival with 5 years follow-up, RESULTS:The tumoral MVD in 32 HNSCC specimens before chemotherapy ranged from 4.0-39.0 (mean, 14.8; median, 14.2). Eight out of 32 patients achieved pathologically complete remission; their tumoral MVD revealed a mean of 10.9. The 24 remaining patients had pathologically-confirmed residual tumor post-chemotherapy; their tumoral MVD revealed a mean of 16.5. CONCLUSION:Statistical analyses revealed no evidence of a relationship between remission and a MVD > or < 10 or > or < 16.5. There was no correlation of tumoral MVD with overall or progression-free survival. In 15 patients, tumoral MVD results were also available on the post-chemotherapy specimens. The greater the difference of the tumoral MVD between the pre- and post-chemotherapy specimens, the shorter the patients overall and progression-free survival (p = 0.042).
CT perfusion for the monitoring of neoadjuvant chemotherapy and radiation therapy in rectal carcinoma: initial experience.
Bellomi Massimo,Petralia Giuseppe,Sonzogni Angelica,Zampino Maria Giulia,Rocca Andrea
PURPOSE:To prospectively monitor changes in rectal cancer perfusion after combined neoadjuvant chemotherapy and radiation therapy with perfusion computed tomography (CT) and to evaluate whether perfusion CT findings correlate with response to therapy. MATERIALS AND METHODS:The study was approved by the institutional ethics committee of the European Institute of Oncology; written informed consent was obtained from all participants before the study. Twenty-five patients with rectal adenocarcinoma (18 men, seven women; age range, 42-72 years; mean age, 61.3 years) underwent perfusion CT; all of them underwent neoadjuvant chemotherapy and radiation therapy, followed by surgery. In 19 patients, perfusion CT was repeated after chemotherapy and radiation therapy. Dynamic perfusion CT was performed for 50 seconds after intravenous injection of contrast medium (40 mL, 370 mg iodine per milliliter, 4 mL/sec). Blood flow (BF), blood volume (BV), mean transit time, and permeability-surface area product (PS) were computed in the tumor and in normal rectal wall by two independent blinded radiologists. Microvessel density was evaluated in pretreatment biopsy specimens in nine patients and in surgical specimens in seven patients. Wilcoxon signed-rank and rank sum tests were used for paired and independent comparisons, respectively. RESULTS:BF, BV, and PS were significantly higher in rectal cancer than in normal rectal wall (P < .001). BF, BV, and PS significantly decreased after combined chemotherapy and radiation therapy (P < .009). No correlation was found between perfusion parameters and microvessel density, neither in baseline values nor in posttherapy changes. Baseline BF and BV in the seven patients who failed to respond to treatment were significantly lower than in the 17 responders (P = .02 for BF and < .001 for BV). CONCLUSION:Perfusion CT has potential for monitoring the effects of combined neoadjuvant chemotherapy and radiation therapy and predicting the response of rectal cancer to such therapy.
High expression of endoglin in primary breast cancer may predict response to neoadjuvant chemotherapy.
Rau Kun-Ming,Su Yu-Li,Li Shan-Hsuan,Hsieh Meng-Che,Wu Shis-Chung,Chou Fong-Fu,Chiu Tai-Jan,Chen Yen-Hao,Liu Chien-Ting
Molecular medicine reports
Neoadjuvant chemotherapy (NAC) is a widely‑used treatment for breast cancer, as it may render unresecta-ble breast tumors to become resectable. In addition, NAC provides the unique opportunity to assess response to treatments within months rather than years of follow‑up. However, predictive markers of tumor response to NAC are lacking. Therefore, the present study aimed to investigate the expression of endoglin, a marker of angiogenesis, and its association with pathologic responses to NAC. Samples from 34 breast cancer patients were obtained prior to and following NAC treatment. Immunohistochemical staining for endoglin and the mechanistic target of rapamycin (mTOR) was performed, and the correlation between the expression of these markers and pathologic response was examined. The overall response rate to NAC of these 34 patients was 67.6%. A mean microvascular density value of 14 served as a threshold score for the increased expression of endoglin. Increased expression of endoglin in primary tumors prior to NAC correlated with improved response in primary tumors (P=0.019) or in primary tumors and regional lymph nodes (P=0.014), when compared with reduced expression of endoglin. Increased expression of mTOR following NAC was additionally correlated with improved response to NAC. The results of the present study demonstrated that the expression of endoglin in breast tumor samples prior to NAC may be a predictor of treatment response. Long‑term follow‑up of clinical outcome is required to explain the elevation of mTOR expression levels following NAC treatment in responsive tumors.
[Evolution of angiogenesis following anthracycline-based neoadjuvant chemotherapy in breast cancer].
Baena-Cañada José M,Palomo González María J,Arriola Arellano Esperanza,Añón Requena María J,Benítez Rodríguez Encarnación
BACKGROUND AND OBJECTIVE:The impact of chemotherapy on the extent of breast cancer angiogenesis is unknown. The aim of this study was to investigate the effect of primary chemotherapy on tumor microvessel density and vascular endothelial growth factor (VEGF), and correlate this changes with tumor response, post-chemotherapy changes and other biological variables. PATIENTS AND METHOD:In 41 consecutive patients with breast cancer stages II and III, treated with anthracycline-based neoadjuvant chemotherapy, immunohistochemical analysis of microvessel density and VEGF were performed before and after the administration of neoadjuvant chemotherapy. RESULTS:Microvessel density was the same in post-chemotherapy that in pre-chemotherapy samples (p = 0.29). There were no changes in the expression of VEGF (p = 0.23). The expression of VEGF and microvessel density did not show any relationship with the response in the pre-chemotherapy analysis (p = 0.60 and p = 0.30 respectively), nor in the post-chemotherapy analysis (p = 0.50 and p = 0.65 respectively). Changes post-chemotherapy were not associated with VEGF expression (p = 0.53 in the pre-chemotherapy samples and p = 0.43 in the post-chemotherapy samples) nor microvessel density (p = 0.72 in the pre-chemotherapy samples and p = 0.65 in the post-chemotherapy samples). CONCLUSIONS:Anthracycline-based neoadjuvant chemotherapy does not cause a reduction of the density of microvessel nor of the expression of VEGF in breast cancer.
Prognostic significance of VEGF expression in correlation with COX-2, microvessel density, and clinicopathological characteristics in human gastric carcinoma.
Kolev Yanislav,Uetake Hiroyuki,Iida Satoru,Ishikawa Toshiaki,Kawano Tatsuyuki,Sugihara Kenichi
Annals of surgical oncology
BACKGROUND:Many studies have shown that angiogenesis plays an important role in the process of cancer development and progression. Vascular endothelial growth factor (VEGF) has a potent angiogenic activity, and cyclooxygenase-2 (COX-2) supports angiogenesis by regulated production of angiogenic factors, including VEGF. The purpose of this study was to examine the expression of VEGF in combination with COX-2 and CD34, their correlation with various clinicopathological factors, and their prognostic significance in human gastric carcinoma. METHODS:Specimens from 169 patients with different grade and stage gastric carcinoma were investigated by immunohistochemistry for COX-2 and VEGF expression. Tumor microvessel density was assessed with CD34 immunostaining. Correlations between the expression of VEGF, COX-2, CD34, and various clinicopathological factors were studied. The effect of these proteins on patient survival was determined. RESULTS:COX-2 and VEGF were positively expressed in 36.7% and 50.3% of the patients, respectively. Positive correlation was found between VEGF and COX-2 and between VEGF and CD34. VEGF expression was correlated with depth of invasion; metastatic lymph nodes; lymphatic and venous invasion; and tumor, node, metastasis system stage. Patients with positive staining for VEGF showed far lower disease-free (64.9% vs. 81.3%) and overall (58.3% vs. 76.9%) survival rates than VEGF-negative patients. In multivariate analysis, only tumor location, depth of invasion, and lymph node metastasis were shown to be independent prognostic factors. CONCLUSIONS:VEGF expression correlates with angiogenesis and tumor progression and is a valuable prognostic factor in patients with gastric carcinoma.
Role of vascular density and normalization in response to neoadjuvant bevacizumab and chemotherapy in breast cancer patients.
Tolaney Sara M,Boucher Yves,Duda Dan G,Martin John D,Seano Giorgio,Ancukiewicz Marek,Barry William T,Goel Shom,Lahdenrata Johanna,Isakoff Steven J,Yeh Eren D,Jain Saloni R,Golshan Mehra,Brock Jane,Snuderl Matija,Winer Eric P,Krop Ian E,Jain Rakesh K
Proceedings of the National Academy of Sciences of the United States of America
Preoperative bevacizumab and chemotherapy may benefit a subset of breast cancer (BC) patients. To explore potential mechanisms of this benefit, we conducted a phase II study of neoadjuvant bevacizumab (single dose) followed by combined bevacizumab and adriamycin/cyclophosphamide/paclitaxel chemotherapy in HER2-negative BC. The regimen was well-tolerated and showed a higher rate of pathologic complete response (pCR) in triple-negative (TN)BC (11/21 patients or 52%, [95% confidence interval (CI): 30,74]) than in hormone receptor-positive (HR)BC [5/78 patients or 6% (95%CI: 2,14)]. Within the HRBCs, basal-like subtype was significantly associated with pCR (P = 0.007; Fisher exact test). We assessed interstitial fluid pressure (IFP) and tissue biopsies before and after bevacizumab monotherapy and circulating plasma biomarkers at baseline and before and after combination therapy. Bevacizumab alone lowered IFP, but to a smaller extent than previously observed in other tumor types. Pathologic response to therapy correlated with sVEGFR1 postbevacizumab alone in TNBC (Spearman correlation 0.610, P = 0.0033) and pretreatment microvascular density (MVD) in all patients (Spearman correlation 0.465, P = 0.0005). Moreover, increased pericyte-covered MVD, a marker of extent of vascular normalization, after bevacizumab monotherapy was associated with improved pathologic response to treatment, especially in patients with a high pretreatment MVD. These data suggest that bevacizumab prunes vessels while normalizing those remaining, and thus is beneficial only when sufficient numbers of vessels are initially present. This study implicates pretreatment MVD as a potential predictive biomarker of response to bevacizumab in BC and suggests that new therapies are needed to normalize vessels without pruning.
Effects of conventional neoadjuvant chemotherapy for breast cancer on tumor angiogenesis.
Luengo-Gil Ginés,González-Billalabeitia Enrique,Chaves-Benito Asunción,García Martínez Elena,García Garre Elisa,Vicente Vicente,Ayala de la Peña Francisco
Breast cancer research and treatment
The effects of breast cancer conventional chemotherapy on tumor angiogenesis need to be further characterized. Neoadjuvant chemotherapy is an ideal model to evaluate the results of chemotherapy, allowing intra-patient direct comparison of antitumor and antiangiogenic effects. We sought to analyze the effect of neoadjuvant chemotherapy on tumor angiogenesis and its clinical significance in breast cancer. Breast cancer patients (n = 108) treated with neoadjuvant sequential anthracyclines and taxanes were studied. Pre- and post-chemotherapy microvessel density (MVD) and mean vessel size (MVS) were analyzed after CD34 immunohistochemistry and correlated with tumor expression of pro- and antiangiogenic factors (VEGFA, THBS1, HIF1A, CTGF, and PDGFA) by qRT-PCR. Angiogenic measures at diagnosis varied among breast cancer subtypes. Pre-treatment higher MVS was associated with triple-negative subtype and more advanced disease. Higher MVS was correlated with higher VEGFA (p = 0.003), while higher MVD was correlated with lower antiangiogenic factors expression (THBS1, p < 0.0001; CTGF, p = 0.001). Increased angiogenesis at diagnosis (high MVS and glomeruloid microvascular proliferation) and higher VEGFA expression were associated with tumor recurrence (p = 0.048 and 0.009, respectively). Chemotherapy-induced angiogenic response (defined as decreased MVD) was present in 35.2 % of patients. This response correlated with an increase in antiangiogenic factors (THBS1) without changes in VEGFA expression, and it was associated with tumor downstaging, but not with clinical response, pathologic complete response, or prognosis. Global effects of chemotherapy mainly consisted in an increased expression of antiangiogenic factors (THBS1, CTGF), with significant changes neither of tumor VEGFA nor of MVS. Conventionally scheduled neoadjuvant chemotherapy exerts antiangiogenic effects, through an increase in antiangiogenic factors, THBS1 and CTGF, but the expression of VEGFA is maintained after treatment. Better markers of angiogenic response and a better understanding of the cooperation of chemotherapy and antiangiogenic therapy in the neoadjuvant clinical scenario are needed.