MRI-Based Machine Learning Approach Versus Radiologist MRI Reading for the Detection of Prostate Cancer, The PRIMER Trial

This clinical trial studies how well a magnetic resonance imaging (MRI)-based machine learning approach (i.e., artificial intelligence \[AI\]) works as compared to radiologist MRI readings in detecting prostate cancer. One of the current methods used to help diagnose possible prostate cancer is performing a prostate MRI. An MRI uses a magnetic field to take pictures of the body. The MRI images are examined by a radiologist. If a suspicious area is seen in the MRI, the radiologist assigns it a PIRADS score. This stands for Prostate Imaging Reporting and Data System. The PIRADS score is used to report how likely it is that a suspicious area in the prostate is cancer. The AI system has been developed also to be able to analyze prostate MRI images and detect suspicious areas in the prostate that may be cancer. The AI system's ability to diagnose aggressive prostate cancer may be similar to detection performed by experienced radiologists using the standard PIRADS system of analyzing prostate MRI.

NCT Number	NCT07162194
Lead Sponsor	University of Southern California Collaborators: National Cancer Institute (NCI)
Conditions	Prostate Carcinoma
Enrollment	130 participants
Start Date	2025-09-19
Primary Completion	2027-10-15 (estimated)
Study Completion	2028-10-15 (estimated)
Updated on ClinicalTrials.gov	2026-06-15