A Physiologically-Decomposed DWI machine-learning model improves prediction of response to NAC treatment in invasive breast cancer

Early prediction of pathological complete response (pCR) following neoadjuvant chemotherapy for breast cancer plays a critical role in surgical planning and optimizing treatment strategies. Recently, machine and deep-learning based methods were suggested for early pCR prediction from multi-parametric MRI data with moderate success. We introduce PD-DWI, a physiologically-decomposed DWI machine-learning model to predict pCR from DWI and clinical data. Our model first decomposes the raw DWI data into the various physiological cues that are influencing the DWI signal and then uses the decomposed data, in addition to clinical variables, as the input features of a radiomics-based XGBoost model.