Radiomics-based Machine Learning Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer Using Physiologically Decomposed Diffusion-weighted MRI.

Purpose To evaluate the performance of a machine learning model developed using radiomics data derived from physiologically decomposed diffusion-weighted MRI data for predicting pathologic complete response (pCR) following neoadjuvant chemotherapy for breast cancer compared with baseline and benchmark models. Materials and Methods This retrospective study included data from the Breast Multiparametric MRI for prediction of neoadjuvant chemotherapy Response (BMMR2) challenge dataset, comprising longitudinal multiparametric breast MRI studies (diffusion-weighted imaging [DWI] and dynamic contrast-enhanced MRI) from participants enrolled in the I-SPY 2/ACRIN 6698 trial (ClinicalTrials.gov: NCT01042379). Piecewise linear physiologic decomposition was applied to DWI data (PD DWI) to isolate pseudo-diffusion, pure-diffusion, and pseudo-diffusion fraction components for radiomics feature extraction. These features were used to develop a boosted decision tree model to predict pCR following neoadjuvant chemotherapy. Model performance was compared with performance of baseline models, including data on tumor size and mean apparent diffusion coefficient, and the BMMR2 challenge benchmark model using area under the receiver operating characteristic curve, F1 score, and positive and negative predictive values. Model calibration was assessed via the Brier score, and a decision curve analysis was performed to estimate the potential reduction in unnecessary interventions when using the proposed model. Results The study included multiparametric MRI scans from 190 female participants (mean age ± SD, 48.4 years ± 10.5). PD DWI achieved the highest area under the receiver operating characteristic curve (0.89, 95% CI: 0.81, 0.96) among all evaluated models, demonstrating statistically significant improvements over baseline approaches (all P < .04). Decision curve analysis showed that the PD DWI model provided a greater net benefit compared with the BMMR2 challenge benchmark model (0.17, 95% CI: 0.13, 0.21 vs 0.09, 95% CI: 0.05, 0.13; P < .001). Conclusion A machine learning model using radiomics data derived from PD DWI achieved higher performance than baseline and benchmark models in predicting pCR following neoadjuvant chemotherapy for breast cancer. Keywords: Image Postprocessing, MR-Diffusion Weighted Imaging, Breast, Tumor Response, Experimental Investigations ClinicalTrials.gov: NCT01042379 © RSNA, 2025.