Recently, a new form of magnetic resonance imaging (MRI) called synthetic correlated diffusion (CDIs) 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 CDIs for other forms of cancers such as breast cancer has not been as well-explored nor have CDIs data been previously made publicly available. Motivated to advance efforts in the development of computer-aided clinical decision support for breast cancer using CDIs, we introduce Cancer-Net BCa, a multi-institutional open-source benchmark dataset of volumetric CDIs imaging data of breast cancer patients. Cancer-Net BCa contains CDIs 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.
@article{arxiv.2304.05623,
title = {A Multi-Institutional Open-Source Benchmark Dataset for Breast Cancer Clinical Decision Support using Synthetic Correlated Diffusion Imaging Data},
author = {Chi-en Amy Tai and Hayden Gunraj and Alexander Wong},
journal= {arXiv preprint arXiv:2304.05623},
year = {2023}
}