Fully Automated Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using Deep Learning

Predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer is crucial for effective surgical planning and treatment optimization. While radiomics-based methods have been explored for pCR prediction using diffusion-weighted MRI (DWI), they rely on manual tumor segmentation - a laborious and error-prone task. Our study introduces a deep learning model that automates tumor segmentation from DWI, enhancing the accuracy and efficiency of radiomics-based pCR predictions and eliminating the need for manual intervention. We evaluated our approach on the publicly BMMR2 challenge data using a k-fold crossvalidation experimental setup, comparing the radiomics-based pCR predictions from manual and automatic segmentations. Our approach demonstrated a human-level performance for pre-treatment radiomics-based pCR prediction from the DWI data.Clinical relevance: This study presents a fully automated method for predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients. Our model combines diffusion-weighted imaging (DWI) with deep-learning algorithms for breast lesion segmentation and radiomics-based machine learning for pCR prediction. By fully automating the segmentation process, our approach offers the potential for objective, early pCR prediction based on DWI in breast cancer.