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MRI-Based Machine Learning Approach Versus Radiologist MRI Reading for the Detection of Prostate Cancer, The PRIMER Trial
Sponsor: 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.
Official title: 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
Key Details
Gender
MALE
Age Range
20 Years - Any
Study Type
INTERVENTIONAL
Enrollment
130
Start Date
2025-09-19
Completion Date
2028-10-15
Last Updated
2026-01-12
Healthy Volunteers
No
Conditions
Interventions
Targeted Prostate Biopsy
Undergo targeted prostate biopsy
Prostate Imaging Reporting & Data System
PIRADS Assessment
Deep Learning Artificial Intelligence
Deep Learning (DL) AI predictions
Green Learning Artificial Intelligence
Green Learning (GL) AI predictions
Radical Prostatectomy
Undergo RP
Locations (1)
USC / Norris Comprehensive Cancer Center
Los Angeles, California, United States