Deep Learning MRI-based Model for Prediction of Clinically Significant Prostate Cancer

Abstract

Prostate cancer is one of the leading causes of death for men globally, despite many men being diagnosed with indolent tumors that do not warrant treatment. Increasingly, magnetic resonance imaging (MRI) is being used as a risk assessment tool, before more invasive prostate biopsies are performed for patients at suspicion of prostate cancer. We hypothesize that we can train a deep learning model that combines multi-parametric MRI images with clinical factors to accurately predict patient risk of developing clinically significant prostate cancer. We train an image model and combined image and clinical factors model on a set of 9391 MRIs from the Massachusetts General Brigham (MGB) hospital system, which achieved an area under the receiver-operator curve (AUROC) of 0.80 and 0.84, respectively, for 1-year prediction of clinically significant prostate cancer, surpassing current human baselines and existing risk models’ performance.