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

Cancer-Net BCa-S: Breast Cancer Grade Prediction using Volumetric Deep Radiomic Features from Synthetic Correlated Diffusion Imaging