Prediction of Early Neoadjuvant Chemotherapy Response of Breast Cancer through Deep Learning-based Pharmacokinetic Quantification of DCE MRI.

Purpose To improve the generalizability of pathologic complete response prediction following neoadjuvant chemotherapy using deep learning-based retrospective pharmacokinetic quantification of early treatment dynamic contrast-enhanced MRI. Materials and Methods This multicenter retrospective study included breast MRI data from four publicly available datasets of patients with breast cancer acquired from May 2002 to November 2016. Pharmacokinetic quantification was performed using a previously developed deep learning model for clinical multiphasic dynamic contrast-enhanced MRI datasets. Radiomic analysis was performed on pharmacokinetic quantification maps and conventional enhancement maps. These data, together with clinicopathologic variables and shape-based radiomic analysis, were subsequently applied for pathologic complete response prediction using logistic regression. Prediction performance was evaluated by area under the receiver operating characteristic curve (AUC). Results A total of 1073 female patients with breast cancer were included. The proposed method showed improved consistency and generalizability compared with the reference method, achieving higher AUC values across external datasets (0.82 [95% CI: 0.72, 0.91], 0.75 [95% CI: 0.71, 0.79], and 0.77 [95% CI: 0.66, 0.86] for datasets A2, B, and C, respectively). For dataset A2 (from the same study as the training dataset), there was no significant difference in performance between the proposed method and reference method (