Abstract
In the article that accompanies this editorial, Bergstrom et al. present DeepHRD, a deep learning algorithm that predicts homologous recombination deficiency (HRD) and clinical outcomes directly from digital histopathology slides, demonstrating its accuracy and generalizability across multiple independent cohorts of breast and ovarian cancer patients. This deep learning approach has the potential to be the next wave of precision medicine in oncology care but there are many challenges to overcome before wide clinical adoption including robust validation, prospective evaluation and regulatory approval.