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

Enhancing Clinical Support for Breast Cancer with Deep Learning Models using Synthetic Correlated Diffusion Imaging