PRIMER (Prostate MRI With Machine LEarning vs. Radiologist) A Novel MRI-Based Machine Learning Approach vs Radiologist MRI Reading for Targeted Prostate Biopsy: A Non-Inferiority, Within-Person Randomized Controlled Trial for Prostate Cancer Detection
University of Southern California
Summary
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.
Description
PRIMARY OBJECTIVE: I. To determine the non-inferiority of targeted biopsy according to Green Learning (GL) AI over Prostate Imaging Reporting \& Data System (PIRADS). SECONDARY OBJECTIVES: I. To determine the clinically significant prostate cancer (CSPCa) detection rate on Deep Learning (DL) AI-targeted biopsy. II. To determine the patient-level diagnostic performance of GL AI, Deep Learning (DL) AI and PIRADS for clinically significant prostate cancer (CSPCa) detection. III. To assess Targeted biopsy core characteristics. IV. To evaluate the predictors for patient-level CSPCa detection.…