Deep-ODX: an efficient deep learning tool to risk stratify breast cancer patients from histopathology images
Ziyu Su, Amanda Rosen, Robert Wesolowski, Gary H. Tozbikian, Muhammad Khalid Khan Niazi, Metin N. Gürcan · 2024
Breast cancer is the most common cancer diagnosed in women and causes over 40,000 deaths annually in the United States. In early-stage, HR+, HER2- invasive breast cancer, the Oncotype DX (ODX) Breast Cancer Recurrence Score Test predicts the risk of recurrence and the benefit of chemotherapy. However, this gene assay is costly and time-consuming, making it inaccessible to many patients. This study proposes a novel deep-learning approach, Deep-ODX, which performs ODX recurrence risk prediction based on routine H&E histopathology images. Deep-ODX is a multiple-instance learning model that leverages a cross-attention neural network, for instance, aggregation. We train and evaluate Deep-ODX on a whole slide image dataset collected from 151 breast cancer patients. As a result, Deep-ODX achieves 0.862 AUC on our dataset, outperforming the existing deep learning models. This study indicates that deep learning methods can predict ODX results from histopathology images, offering a potentially cost-effective prognostic solution with broader accessibility.