  In 2020, 685,000 deaths across the world were attributed to breast cancer,
underscoring the critical need for innovative and effective breast cancer
treatment. Neoadjuvant chemotherapy has recently gained popularity as a
promising treatment strategy for breast cancer, attributed to its efficacy in
shrinking large tumors and leading to pathologic complete response. However,
the current process to recommend neoadjuvant chemotherapy relies on the
subjective evaluation of medical experts which contain inherent biases and
significant uncertainty. A recent study, utilizing volumetric deep radiomic
features extracted from synthetic correlated diffusion imaging (CDI$^s$),
demonstrated significant potential in noninvasive breast cancer pathologic
complete response prediction. Inspired by the positive outcomes of optimizing
CDI$^s$ for prostate cancer delineation, this research investigates the
application of optimized CDI$^s$ to enhance breast cancer pathologic complete
response prediction. Using multiparametric MRI that fuses optimized CDI$^s$
with diffusion-weighted imaging (DWI), we obtain a leave-one-out
cross-validation accuracy of 93.28%, over 5.5% higher than that previously
reported.
