  Early prediction of pathological complete response (pCR) following
neoadjuvant chemotherapy (NAC) 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 (mp-MRI) data including dynamic contrast-enhanced MRI and
diffusion-weighted MRI (DWI) 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. We demonstrated the added-value of our PD-DWI
model over conventional machine-learning approaches for pCR prediction from
mp-MRI data using the publicly available Breast Multi-parametric MRI for
prediction of NAC Response (BMMR2) challenge. Our model substantially improves
the area under the curve (AUC), compared to the current best result on the
leaderboard (0.8849 vs. 0.8397) for the challenge test set. PD-DWI has the
potential to improve prediction of pCR following NAC for breast cancer, reduce
overall mp-MRI acquisition times and eliminate the need for contrast-agent
injection.
