PROJECT SUMMARY African American (AA) men have the highest incidence and mortality rate from prostate cancer (PCa) in the United States. Prostate multi-parametric MRI (mpMRI) is a non-invasive imaging technique that can sensitively detect prostate tumors by integrating anatomical and functional information. The current standardized scheme for interpreting mpMRI is the Prostate Imaging Reporting and Data System (PI-RADS). However, detecting cancerous lesions currently does not account for racially associated MRI characteristics in PI-RADS. Our preliminary data showed a significant difference in detecting clinically significant PCa (csPCa) between AA and CA men using PI-RADS when the tumors are in the transition zone (67% vs. 80%, respectively, p=0.026). In addition, there was a distinctive difference in the PCa perfusion (that is, Ktrans) between AA and CA men, when measured by quantitative dynamic contrast-enhanced MRI (qDCE). When PI-RADS-based interpretation was combined with the Ktrans threshold value specified for AA men, the csPCa detection rate in the transition zone in AA men was improved to 76%, becoming not statistically different from that in CA men (p=0.180). We developed a point-of-care portable perfusion phantom named P4 to improve the reproducibility of qDCE measurement across different institutes. The P4-based error correction significantly reduced the variability in qDCE measurement across three MRI scanners in two institutes and improved the specificity of Ktrans for csPCa detection from 86% to 93%. We hypothesize that the racial disparity in PCa diagnosis can be reduced by using racially associated qDCE measurement after P4-based error correction. We propose 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 team will 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 highly curated radiology-pathology dataset will be used (1) to characterize the qDCE measurement associated with tumor microenvironment in AA and CA groups, using co-localized quantitative radiology-pathology analyses after P4-based error correction, (2) to investigate whether the racially associated MRI-based tissue characterization improves the detection of aggressive PCa, and (3) to develop the race/ethnicity-specific deep learning model for the improved detection of aggressive PCa. When the Aims are successfully accomplished, the improved detection of PCa in both AA and CA men is anticipated, compared to conventional strategies, reducing the racial disparity in detecting aggressive PCa.