RecruitingNot ApplicableNCT07162194

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

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


Sponsor

University of Southern California

Enrollment

130 participants

Start Date

Sep 19, 2025

Study Type

INTERVENTIONAL

Conditions

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.


Eligibility

Sex: MALEMin Age: 20 Years

Inclusion Criteria6

  • 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 90 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 180 days prior to radical prostatectomy
  • RADICAL PROSTATECTOMY COHORT: Patients who consented to the study

Exclusion Criteria14

  • 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

Interventions

PROCEDURETargeted Prostate Biopsy

Undergo targeted prostate biopsy

DIAGNOSTIC_TESTProstate Imaging Reporting & Data System

PIRADS Assessment

DIAGNOSTIC_TESTDeep Learning Artificial Intelligence

Deep Learning (DL) AI predictions

DIAGNOSTIC_TESTGreen Learning Artificial Intelligence

Green Learning (GL) AI predictions

PROCEDURERadical Prostatectomy

Undergo RP


Locations(1)

USC / Norris Comprehensive Cancer Center

Los Angeles, California, United States

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NCT07162194


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