  Recently, a new form of magnetic resonance imaging (MRI) called synthetic
correlated diffusion (CDI$^s$) imaging was introduced and showed considerable
promise for clinical decision support for cancers such as prostate cancer when
compared to current gold-standard MRI techniques. However, the efficacy for
CDI$^s$ for other forms of cancers such as breast cancer has not been as
well-explored nor have CDI$^s$ data been previously made publicly available.
Motivated to advance efforts in the development of computer-aided clinical
decision 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 ten
institutions, along with detailed annotation metadata (the lesion type, genetic
subtype, 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 tumour
diversity of the Cancer-Net BCa dataset to gain deeper insights into potential
biases. Cancer-Net BCa is publicly available as a part of a global open-source
initiative dedicated to accelerating advancement in machine learning to aid
clinicians in the fight against cancer.
