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

Recruiting

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

Who can join

Ages 20 and older · Men

Full eligibility criteria
Inclusion Criteria:

* PROSTATE BIOPSY COHORT: Patients undergoing transperineal MRI/TRUS fusion prostate biopsy (PBx) as per standard of care
* PROSTATE BIOPSY COHORT: Patients who underwent or are undergoing 3T multiparametric MRI (T2W, diffusion weighted imaging \[DWI\], apparent diffusion coefficient \[ADC\], and dynamic contrast-enhanced \[DCE\]) within 365 days prior to biopsy
* PROSTATE BIOPSY COHORT: Patients who consented to the study
* RADICAL PROSTATECTOMY COHORT: Patients undergoing radical prostatectomy for primary treatment of prostate cancer as per standard of care
* RADICAL PROSTATECTOMY COHORT: Patients who underwent or are undergoing 3T multiparametric MRI (T2W, DWI, ADC, and DCE) within 365 days prior to radical prostatectomy
* RADICAL PROSTATECTOMY COHORT: Patients who consented to the study

Exclusion Criteria:

* PROSTATE BIOPSY COHORT: Patients with a history of prostate cancer
* PROSTATE BIOPSY COHORT: Patients with a history of surgical treatment on benign prostate hyperplasia
* PROSTATE BIOPSY COHORT: Patients undergoing saturation prostate biopsy
* PROSTATE BIOPSY COHORT: Patients under 20 years old
* PROSTATE BIOPSY COHORT: Patients with previous PBx history
* PROSTATE BIOPSY COHORT: MRI which was not interpreted by PIRADS
* PROSTATE BIOPSY COHORT: MRI with significant artifact
* RADICAL PROSTATECTOMY COHORT: Patients who are undergoing neo-adjuvant hormonal therapy in conjunction with radical prostatectomy
* RADICAL PROSTATECTOMY COHORT: Patients with a history of surgical treatment on benign prostate hyperplasia
* RADICAL PROSTATECTOMY COHORT: Patients under 20 years old
* RADICAL PROSTATECTOMY COHORT: Patients without pre-treatment MRI
* RADICAL PROSTATECTOMY COHORT: MRI which was not interpreted by PIRADS
* RADICAL PROSTATECTOMY COHORT: MRI with significant artifact
* RADICAL PROSTATECTOMY COHORT: Patients who are included in the Biopsy cohort

About the study

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.

What is being tested

Sponsor: University of Southern California · Participants: 130 · Started: Sep 19, 2025

Contact the study team

Official record on ClinicalTrials.gov — NCT07162194

Locations in the U.S.

CaliforniaUSC / Norris Comprehensive Cancer Center, Los Angeles

Conditions

From ClinicalTrials.gov, data retrieved Sep 30, 2026. Each study sets its own eligibility; the study team decides who can join.