  Breast cancer is the second most common type of cancer in women in Canada and
the United States, representing over 25\% of all new female cancer cases. As
such, there has been immense research and progress on improving screening and
clinical support for breast cancer. In this paper, we investigate enhancing
clinical support for breast cancer with deep learning models using a newly
introduced magnetic resonance imaging (MRI) modality called synthetic
correlated diffusion imaging (CDI$^s$). More specifically, we leverage a
volumetric convolutional neural network to learn volumetric deep radiomic
features from a pre-treatment cohort and construct a predictor based on the
learnt features for grade and post-treatment response prediction. As the first
study to learn CDI$^s$-centric radiomic sequences within a deep learning
perspective for clinical decision support, we evaluated the proposed approach
using the ACRIN-6698 study against those learnt using gold-standard imaging
modalities. We find that the proposed approach can achieve better performance
for both grade and post-treatment response prediction and thus may be a useful
tool to aid oncologists in improving recommendation of treatment of patients.
Subsequently, the approach to leverage volumetric deep radiomic features for
breast cancer can be further extended to other applications of CDI$^s$ in the
cancer domain to further improve clinical support.
