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.
翻译:近期,一种新的磁共振成像技术,称为合成相关扩散(CDI$^s$)成像被引入,并在与当前黄金标准磁共振成像技术相比的前列腺癌临床决策支持方面表现出相当的前途。然而,CDI $^s$在其他癌症,如乳腺癌方面的效果并未得到充分探索,也尚未公开使用过CDI $^s$数据。本文旨在推动使用CDI$^s$技术开发计算机辅助乳腺癌临床决策支持的研究,介绍了Cancer-Net BCa,这是一个由多个医疗机构提供的基于合成相关扩散CDI$^s$成像数据的开源基准数据集,该数据集包括了来自十个机构的253名乳腺癌患者的CDI$^s$体积图像,以及详细的注释元数据(如病变类型、遗传亚型、MRI上最长直径(MRLD)、Scarff-Bloom-Richardson(SBR)分级和新辅助化疗后乳腺癌病理学完全缓解(pCR)等)。我们还进一步研究了Cancer-Net BCa数据集的人口和肿瘤多样性,以深入了解潜在的偏见。Cancer-Net BCa作为全球开源倡议的一部分,致力于加快机器学习在对抗癌症中协助临床医师的进程。