  The prevalence of breast cancer continues to grow, affecting about 300,000
females in the United States in 2023. However, there are different levels of
severity of breast cancer requiring different treatment strategies, and hence,
grading breast cancer has become a vital component of breast cancer diagnosis
and treatment planning. Specifically, the gold-standard Scarff-Bloom-Richardson
(SBR) grade has been shown to consistently indicate a patient's response to
chemotherapy. Unfortunately, the current method to determine the SBR grade
requires removal of some cancer cells from the patient which can lead to stress
and discomfort along with costly expenses. In this paper, we study the efficacy
of deep learning for breast cancer grading based on synthetic correlated
diffusion (CDI$^s$) imaging, a new magnetic resonance imaging (MRI) modality
and found that it achieves better performance on SBR grade prediction compared
to those learnt using gold-standard imaging modalities. Hence, we introduce
Cancer-Net BCa-S, a volumetric deep radiomics approach for predicting SBR grade
based on volumetric CDI$^s$ data. Given the promising results, this proposed
method to identify the severity of the cancer would allow for better treatment
decisions without the need for a biopsy. Cancer-Net BCa-S has been made
publicly available as part of a global open-source initiative for advancing
machine learning for cancer care.
