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- Title
Integration of clinical features and deep learning on pathology for the prediction of breast cancer recurrence assays and risk of recurrence.
- Authors
Howard, Frederick M.; Dolezal, James; Kochanny, Sara; Khramtsova, Galina; Vickery, Jasmine; Srisuwananukorn, Andrew; Woodard, Anna; Chen, Nan; Nanda, Rita; Perou, Charles M.; Olopade, Olufunmilayo I.; Huo, Dezheng; Pearson, Alexander T.
- Abstract
Gene expression-based recurrence assays are strongly recommended to guide the use of chemotherapy in hormone receptor-positive, HER2-negative breast cancer, but such testing is expensive, can contribute to delays in care, and may not be available in low-resource settings. Here, we describe the training and independent validation of a deep learning model that predicts recurrence assay result and risk of recurrence using both digital histology and clinical risk factors. We demonstrate that this approach outperforms an established clinical nomogram (area under the receiver operating characteristic curve of 0.83 versus 0.76 in an external validation cohort, p = 0.0005) and can identify a subset of patients with excellent prognoses who may not need further genomic testing.
- Subjects
CANCER relapse; HORMONE receptor positive breast cancer; DEEP learning; BREAST cancer; RECEIVER operating characteristic curves; PATHOLOGY
- Publication
NPJ Breast Cancer, 2023, Vol 9, Issue 1, p1
- ISSN
2374-4677
- Publication type
Article
- DOI
10.1038/s41523-023-00530-5