Radiomic Nomogram for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Therapy in Breast Cancer: Predictive Value of Staging Contrast-enhanced CT

2020 ◽  
Author(s):  
Xiaomei Huang ◽  
Jinhai Mai ◽  
Yanqi Huang ◽  
Lan He ◽  
Xin Chen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neoadjuvant Therapy ◽  
Predictive Value ◽  
Pathologic Complete Response ◽  
Complete Response ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct

scholarly journals Texture Analysis of Dynamic Contrast-Enhanced MRI in Evaluating Pathologic Complete Response (pCR) of Mass-Like Breast Cancer after Neoadjuvant Therapy

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Kun Cao ◽  
Bo Zhao ◽  
Xiao-Ting Li ◽  
Yan-Ling Li ◽  
Ying-Shi Sun
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Neoadjuvant Therapy ◽  
Texture Analysis ◽  
Pathologic Complete Response ◽  
Complete Response ◽  
Imaging Method ◽  
First Order ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced

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 (P<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.

Laboratory indicators predict axillary nodal pathologic complete response after neoadjuvant therapy in breast cancer

2021 ◽  
Author(s):  
Peng Chen ◽  
Tong Zhao ◽  
Zhao Bi ◽  
Zhao-Peng Zhang ◽  
Li Xie ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Multivariate Analysis ◽  
Neoadjuvant Therapy ◽  
Predictive Factors ◽  
Molecular Subtype ◽  
Treatment Options ◽  
Pathologic Complete Response ◽  
Complete Response ◽  
Breast Cancer Patients ◽  
Logistic Analysis

 The purpose was to integrate clinicopathological and laboratory indicators to predict axillary nodal pathologic complete response (apCR) after neoadjuvant therapy (NAT). The pretreatment clinicopathological and laboratory indicators of 416 clinical nodal-positive breast cancer patients who underwent surgery after NAT were analyzed from April 2015 to 2020. Predictive factors of apCR were examined by logistic analysis. A nomogram was built according to logistic analysis. Among the 416 patients, 37.3% achieved apCR. Multivariate analysis showed that age, pathological grading, molecular subtype and neutrophil-to-lymphocyte ratio were independent predictors of apCR. A nomogram was established based on these four factors. The area under the curve (AUC) was 0.758 in the training set. The validation set showed good discrimination, with AUC of 0.732. In subtype analysis, apCR was 23.8, 47.1 and 50.8% in hormone receptor-positive/HER2-, HER2+ and triple-negative subgroups, respectively. According to the results of the multivariate analysis, pathological grade and fibrinogen level were independent predictors of apCR after NAT in HER2+ patients. Except for traditional clinicopathological factors, laboratory indicators could also be identified as predictive factors of apCR after NAT. The nomogram integrating pretreatment indicators demonstrated its distinguishing capability, with a high AUC, and could help to guide individualized treatment options.

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