Prostate cancer is the second leading cause of cancer death in American men, accounting for 26% of new cancer diagnoses and 9% of cancer deaths in men. Active surveillance, radical prostatectomy and radiotherapy are commonly used treatments for clinically localized prostate cancer. However, current risk stratification methods cannot be used effectively to avoid subjecting patients with clinically indolent cancers to unnecessary interventions, causing significant morbidity and cost. The primary components currently involved in screening are the digital rectal exam (DRE) and serum biomarkers, such as PSA, PCA3, PHI, and 4Kscore. Unfortunately, despite advances in these tests, overdiagnosis remains a major problem due to limited specificity. As a result, 90% of patients diagnosed with prostate cancer receive treatment, even though up to 60% of those patients could be candidates for active surveillance. Such treatment often results in long-term reductions in functional outcomes. The research objective of this R21 is to develop novel techniques using multiparametric magnetic resonance imaging (mp-MRI) and MRI-ultrasound (US) fusion guided biopsy data that provide discriminatory power in distinguishing indolent versus clinically significant prostatic adenocarcinoma based on non-invasive imaging. We propose to implement a multi-instance learning (MIL) based convolutional neural network (CNN) model for clinical prostate mp-MRI sequences to generate new quantitative imaging features representative of the underlying tissue. Our MIL-CNN model will accommodate ground truth labels from pathology whole mount specimens, as well as MRI-US fusion biopsy results. Hierarchical CNN features will be used to predict voxellevel cancer suspicion, thereby enabling a novel method for performing “imaging biopsy.” Finally, voxel-level suspicion maps will be aggregated into patient-level quantitative imaging biomarkers and combined with clinical data to create a multimodal nomogram for performing risk stratification.