James M. Dolezal

Apr 14, 2023 | nature.com | Frederick Howard |James M. Dolezal |Andrew Srisuwananukorn |Rita Nanda |Charles M. Perou |Olufunmilayo I. Olopade | +2 more

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.