Abstract

Motivation: Prostate cancer (PCa) biochemical recurrence (BCR) following prostatectomy (RP) is correlated with a higher risk of distant metastasis, local recurrence, and even PCa-specific deathGoal(s): To develop and validate a machine learning multi-modality model based on preoperative magnetic resonance imaging (MRI), surgical whole-slide images (WSIs) and clinical variables for predicting PCa BCR following RP. Approach: Radiomics signature and pathomics signature were constructed using preoperative MRI and surgical WSI, respectively. A multi-modality model was constructed by combining radiomics signature, pathomics signature and clinical factors. Results: The multi-modality model exhibited the best predictive efficacy, which is significantly higher than all single-modality models. Impact: Our research could provide an innovative and useful tool for facilitating precision decision-making and personalized treatment in PCa patients. Future studies could utilizing deep learning to analyses mpMRI and WSIs.