Multimodal deep learning model for predicting homologous recombination deficiency in prostate cancer: an international multi-cohort study

Accurate prediction of homologous recombination deficiency (HRD) status is crucial for effective management of prostate cancer (PCa). However, genetic testing is expensive and inaccessible. We propose a multimodal deep learning approach that integrates clinical information, Hematoxylin & Eosin (H&E)-stained whole-slide images (WSIs), and multi-parameter MRI images to predict the HRD status of patients with PCa. Patients from the Cancer Genome Atlas (n = 387) and three Chinese hospitals (n = 179) were used to establish and validate the prediction models. The Pathology Signature, Radiology Signature, and Patho-Radiology Signature could accurately predict the HRD status in external validation or 5-fold cross validation (AUC = 0.815, 0.833, and 0.933, respectively). Notably, four interpretative pathological features were identified in the Pathology Signature. These signatures could serve as a prescreening tool to select patients for confirmatory genetic testing. We suggest that this multi-modal approach could be applied to the prediction of molecular alterations in other malignancies.