Abstract
AbstractGene 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.
Funder
U.S. Department of Health & Human Services | NIH | National Institute of Dental and Craniofacial Research
U.S. Department of Defense
Conquer Cancer Foundation
U.S. Department of Health & Human Services | NIH | National Cancer Institute
Breast Cancer Research Foundation
Susan G. Komen
Publisher
Springer Science and Business Media LLC
Subject
Pharmacology (medical),Radiology, Nuclear Medicine and imaging,Oncology
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