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Recently, a new form of magnetic resonance imaging (MRI) called syntheticcorrelated diffusion (CDI$^s$) imaging was introduced and showed considerablepromise for clinical decision support for cancers such as prostate cancer whencompared to current gold-standard MRI techniques. However, the efficacy forCDI$^s$ for other forms of cancers such as breast cancer has not been aswell-explored nor have CDI$^s$ data been previously made publicly available.Motivated to advance efforts in the development of computer-aided clinicaldecision support for breast cancer using CDI$^s$, we introduce Cancer-Net BCa,a multi-institutional open-source benchmark dataset of volumetric CDI$^s$imaging data of breast cancer patients. Cancer-Net BCa contains CDI$^s$volumetric images from a pre-treatment cohort of 253 patients across teninstitutions, along with detailed annotation metadata (the lesion type, geneticsubtype, longest diameter on the MRI (MRLD), the Scarff-Bloom-Richardson (SBR)grade, and the post-treatment breast cancer pathologic complete response (pCR)to neoadjuvant chemotherapy). We further examine the demographic and tumourdiversity of the Cancer-Net BCa dataset to gain deeper insights into potentialbiases. Cancer-Net BCa is publicly available as a part of a global open-sourceinitiative dedicated to accelerating advancement in machine learning to aidclinicians in the fight against cancer.

A Multi-Institutional Open-Source Benchmark Dataset for Breast Cancer Clinical Decision Support using Synthetic Correlated Diffusion Imaging Data