Project Summary Prostate cancer is one of the most commonly occurring forms of cancer, accounting for 21% of all cancer in men. The Prostate Imaging Reporting and Data System (PI-RADS) aims to standardize reporting of prostate cancer using multi-parametric magnetic resonance imaging (mpMRI). However, the in-depth analysis, as demanded by PI-RADS, remains challenging due to the complexity and heterogeneity of the disease, and it is a clinically burdensome task subject to both significant intra- and inter-reader variability. Auxiliary tools based on machine learning methods such as deep learning can reduce diagnostic variability and increase workload efficiency by automatically performing tasks and presenting results to a radiologist for the purpose of decision support. In particular, automated identification and classification of lesion candidates using imaging data can be performed with respect to PI-RADS scoring. In Phase I of this project, we developed two automated methods to reduce the intra- and inter-observer variability while interpreting mpMRI images using the PI-RADS protocol: (i) a method to co-register mpMRI data, and (ii) a method to geometrically segment the prostate gland into the PI-RADS protocol sector map. The overarching goal of this Phase II project is to develop machine learning algorithms that incorporate both co-registered multi-modal imaging biomarkers and PI-RADS sector map information into an automated clinical diagnostic aid. The innovation in this project lies in the use of deep learning to automatically predict PI-RADS classification. This project is significant in that it has the potential to improve clinical efficiency and reduce diagnostic variation in prostate cancer diagnosis. In Aim 1 of this project, we will develop a deep learning approach to localize and classify lesions in mpMRI. In Aim 2, we will integrate this diagnostic tool into the ProFuseCAD system and perform rigorous multi-site validation to quantify PI-RADS classification performance. Both aims will utilize a database of over 1,000 existing mpMRI images from multiple clinical sites to develop and validate the algorithms. Ultimately, enhancements from this project will create a novel feature for Eigen's (the applicant company's) FDA 510(k)-cleared imaging product, ProFuseCAD, in order to improve the diagnosis and reporting of prostate cancer.