PROJECT SUMMARY African American (AA) men face the highest incidence and mortality rates from prostate cancer (PCa) in the United States. Prostate multi-parametric MRI (mpMRI) is a non-invasive imaging technique capable of sensitively detecting prostate tumors by integrating anatomical and functional information. The Prostate Imaging Reporting and Data System (PI-RADS) currently serves as the standardized scheme for interpreting mpMRI. However, the existing method for detecting cancerous lesions does not consider racially associated MRI characteristics, and there is a lack of studies exploring the impact of patients' race/ethnicity on PI-RADS-based interpretation. Our hypothesis is that racial disparities in PCa diagnosis can be reduced by incorporating racially-associated MRI measurements with appropriate error correction. We aim to test this hypothesis in a multi-institutional setting at the University of California, Los Angeles (UCLA) and the University of Alabama at Birmingham (UAB). Our interdisciplinary team plans to collect and link clinical, radiologic, and histopathologic information using patient- specific 3D-printed prostate molds, software registration, and expert annotation before and after radical prostatectomy. The resulting highly curated radiology-pathology dataset will be utilized to (1) characterize the mpMRI measurement associated with the tumor microenvironment in AA and CA groups, employing co-localized quantitative radiology-pathology analyses after error correction, (2) investigate whether racially-associated MRI- based tissue characterization enhances the detection of aggressive PCa, and (3) develop a race/ethnicity- specific deep learning model for improved detection of aggressive PCa. This supplementary funding will support a dedicated graduate student, enabling her to receive additional training in mpMRI and artificial intelligence for non-invasive imaging techniques that enhance the accuracy of risk stratification in PCa for distinct racial/ethnic populations. Her dissertation goal is to explore the underlying race/ethnicity-specific biological mechanisms of PCa and reduce prostate cancer health disparities between AA and CA men through artificial intelligence. To commence her research, she will actively participate in the acquisition and analysis of co-localized radiologic and histopathologic data with both retrospective and prospective cohorts (Aim 1). This involvement will equip her to become proficient in the latest imaging techniques and further investigate racially associated MRI-based tissue characterization (Aim 2). Her training will be augmented by attending national and international annual meetings and completing courses on Medical Imaging, MRI physics, and Artificial Intelligence, provided at UCLA. Collectively, these activities will broaden her training and position her to make fundamental discoveries about improved PCa detection in both AA and CA men, compared to conventional strategies, thereby reducing...