  Effective surgical planning for breast cancer hinges on accurately predicting
pathological complete response (pCR) to neoadjuvant chemotherapy (NAC).
Diffusion-weighted MRI (DWI) and machine learning offer a non-invasive approach
for early pCR assessment. However, most machine-learning models require manual
tumor segmentation, a cumbersome and error-prone task. We propose a deep
learning model employing "Size-Adaptive Lesion Weighting" for automatic DWI
tumor segmentation to enhance pCR prediction accuracy. Despite
histopathological changes during NAC complicating DWI image segmentation, our
model demonstrates robust performance. Utilizing the BMMR2 challenge dataset,
it matches human experts in pCR prediction pre-NAC with an area under the curve
(AUC) of 0.76 vs. 0.796, and surpasses standard automated methods mid-NAC, with
an AUC of 0.729 vs. 0.654 and 0.576. Our approach represents a significant
advancement in automating breast cancer treatment planning, enabling more
reliable pCR predictions without manual segmentation.
