Deep-ODX: an efficient deep learning tool to risk stratify breast cancer patients from histopathology images

Ziyu Su; Amanda Rosen; Robert Wesolowski; Gary Tozbikian; M. Khalid Khan Niazi; Metin N. Gurcan

doi:10.1117/12.3006272

3 April 2024 Deep-ODX: an efficient deep learning tool to risk stratify breast cancer patients from histopathology images

Ziyu Su, Amanda Rosen, Robert Wesolowski, Gary Tozbikian, M. Khalid Khan Niazi, Metin N. Gurcan

Author Affiliations +

Proceedings Volume 12933, Medical Imaging 2024: Digital and Computational Pathology; 1293307 (2024) https://doi.org/10.1117/12.3006272
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

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.

Citation Download Citation

Ziyu Su, Amanda Rosen, Robert Wesolowski, Gary Tozbikian, M. Khalid Khan Niazi, and Metin N. Gurcan "Deep-ODX: an efficient deep learning tool to risk stratify breast cancer patients from histopathology images", Proc. SPIE 12933, Medical Imaging 2024: Digital and Computational Pathology, 1293307 (3 April 2024); https://doi.org/10.1117/12.3006272

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