The prevalence of breast cancer continues to grow, affecting about 300,000 females in the United States in 2023. However, there are different levels of severity of breast cancer requiring different treatment strategies, and hence, grading breast cancer has become a vital component of breast cancer diagnosis and treatment planning. Specifically, the gold-standard Scarff-Bloom-Richardson (SBR) grade has been shown to consistently indicate a patient's response to chemotherapy. Unfortunately, the current method to determine the SBR grade requires removal of some cancer cells from the patient which can lead to stress and discomfort along with costly expenses. In this paper, we study the efficacy of deep learning for breast cancer grading based on synthetic correlated diffusion (CDI$^s$) imaging, a new magnetic resonance imaging (MRI) modality and found that it achieves better performance on SBR grade prediction compared to those learnt using gold-standard imaging modalities. Hence, we introduce Cancer-Net BCa-S, a volumetric deep radiomics approach for predicting SBR grade based on volumetric CDI$^s$ data. Given the promising results, this proposed method to identify the severity of the cancer would allow for better treatment decisions without the need for a biopsy. Cancer-Net BCa-S has been made publicly available as part of a global open-source initiative for advancing machine learning for cancer care.

翻译：乳腺癌的患病率持续增长，2023年影响美国约30万名女性。然而，乳腺癌的严重程度存在不同等级，需要采取不同的治疗策略，因此乳腺癌分级已成为乳腺癌诊断和治疗规划的关键组成部分。具体而言，金标准Scarff-Bloom-Richardson（SBR）分级已被证实可一致预测患者对化疗的反应。遗憾的是，当前确定SBR分级的方法需要从患者体内取出部分癌细胞，这可能导致患者承受压力与不适，并产生高昂费用。本文研究了基于合成相关扩散（CDI$^s$）成像这一新型磁共振成像（MRI）模态进行深度学习乳腺癌分级的有效性，发现其在SBR分级预测上取得了优于基于金标准成像模态学习的性能。为此，我们提出了Cancer-Net BCa-S——一种基于容积CDI$^s$数据的容积深度影像组学方法，用于预测SBR分级。鉴于其前景可观的结果，这一旨在识别癌症严重程度的拟议方法将无需活检即可支持更优的治疗决策。Cancer-Net BCa-S已作为推动机器学习应用于癌症治疗的全球开源计划的一部分公开发布。