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NCT Number: NCT07162194

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.

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Key information

Conditions

Prostate Carcinoma Genital Diseases Genital Diseases, Male Genital Neoplasms, Male Male Urogenital Diseases Neoplasms Neoplasms by Site Prostatic Diseases Prostatic Neoplasms Urogenital Diseases Urogenital Neoplasms

Age range

20 year and older

Sex eligibility

Male

Study type

Interventional

Phase

Not applicable

Primary location

USC / Norris Comprehensive Cancer Center

Los Angeles, California, 90033, United States

Location status: Recruiting

Location contact

Andre Luis Abreu

CONTACT

[email protected]

323-865-3000

Andre Luis Abreu

PRINCIPAL_INVESTIGATOR

About this study

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. V. To assess the spatial correlation of CSPCa distribution on radical prostatectomy (RP) specimens and region of interest (ROI) generated by GL AI and PIRADS.

OUTLINE: Patients undergoing prostate biopsy per standard of care (SOC) are assigned to Group 1. Patients who underwent a prostate biopsy followed by a radical prostatectomy within 6 months, as well as patients only undergoing a radical prostatectomy are assigned to Group 2.

GROUP 1: Patients are randomized to 1 of 6 arms.

ARM I: Patients undergo MRI/transrectal ultrasound (TRUS) followed by a targeted prostate biopsy using PIRADS on study. Patients then undergo a 2nd MRI/TRUS followed by a targeted prostate biopsy based on GL AI predictions. Patients then undergo a 3rd MRI/TRUS followed by a targeted biopsy based on DL AI predictions. Finally, patients undergo up to 12 additional prostate biopsies per SOC.

ARM II: Patients undergo MRI/TRUS followed by a targeted prostate biopsy using PIRADS. Patients then undergo a 2nd MRI/TRUS followed by a targeted prostate biopsy based on DL AI predictions. Patients then undergo a 3rd MRI/TRUS followed by a targeted biopsy based on GL AI predictions. Finally, patients undergo up to 12 additional prostate biopsies per SOC.

ARM III: Patients undergo MRI/TRUS followed by a targeted prostate biopsy using GL AI predictions. Patients then undergo a 2nd MRI/TRUS followed by a targeted prostate biopsy using PIRADS. Patients then undergo a 3rd MRI/TRUS followed by a targeted biopsy based on DL AI predictions. Finally, patients undergo up to 12 additional prostate biopsies per SOC.

ARM IV: Patients undergo MRI/TRUS followed by a targeted prostate biopsy using GL AI predictions. Patients then undergo a 2nd MRI/TRUS followed by a targeted prostate biopsy based on DL AI predictions. Patients then undergo a 3rd MRI/TRUS followed by a targeted biopsy using PIRADS. Finally, patients undergo up to 12 additional prostate biopsies per SOC.

ARM V: Patients undergo MRI/TRUS followed by a targeted prostate biopsy using DL AI predictions. Patients then undergo a 2nd MRI/TRUS followed by a targeted prostate biopsy based on GL AI predictions. Patients then undergo a 3rd MRI/TRUS followed by a targeted biopsy using PIRADS. Finally, patients undergo up to 12 additional prostate biopsies per SOC.

ARM VI: Patients undergo MRI/TRUS followed by a targeted prostate biopsy using DL AI predictions. Patients then undergo a 2nd MRI/TRUS followed by a targeted prostate biopsy using PIRADS. Patients then undergo a 3rd MRI/TRUS followed by a targeted biopsy based on GL AI predictions. Finally, patients undergo up to 12 additional prostate biopsies per SOC.

GROUP 2: Patients have their removed prostate evaluated using a special mold on study. Prostate tissue is mapped and compared with the prostate cancer prediction on MRI generated by radiologists and AI reports.

After completion of study intervention, patients are followed up at 10 days and at 3 months.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Targeted Prostate Biopsy

Procedure

Undergo targeted prostate biopsy

Other names: TB, Prostate Biopsy, PBx

Prostate Imaging Reporting & Data System

Diagnostic Test

PIRADS Assessment

Other names: PIRADS

Deep Learning Artificial Intelligence

Diagnostic Test

Deep Learning (DL) AI predictions

Other names: DL AI

Green Learning Artificial Intelligence

Diagnostic Test

Green Learning (GL) AI predictions

Other names: GL AI

radical prostatectomy

Procedure

Undergo RP

Other names: RP

Primary outcomes

  1. Clinically-significant prostate cancer (CSPCa) detection rate on Green Learning (GL) artificial intelligence (AI)-targeted and Prostate Imaging-Reporting and Data System (PIRADS)-targeted biopsies

    Time frame: Up to 3 months

    Detection rates of PIRADS and GL AI-targeted biopsy will be evaluated per index region of interest (ROI), respectively.

  2. CSPCa detection rate on GL AI-targeted and Deep Learning (DL) AI-targeted biopsies

    Time frame: Up to 3 months

    Detection rates of GL AI and DL AI targeted biopsy will be evaluated per index ROI, respectively.

Secondary outcomes

  1. Patient-level diagnostic performance of GL AI and PIRADS for CSPCa detection

    Time frame: Up to 3 months

    Sensitivity, specificity, accuracy, positive predictive value, and negative predictive value will be compared between PIRADS, GL AI, and DL AI predictions by McNemar test. The performance will be calculated according to the definitions below. Additionally, decision curve analysis will be conducted.

  2. Targeted biopsy core characteristics

    Time frame: Up to 3 months

    Will include prostate cancer subtypes, benign elements, lesion locations, cancer core length (mm), cancer core involvement (%), and Gleason Grade Group. Will be compared between PIRADS, GL AI, and DL AI by Chi-square test or Wilcoxon rank sum test.

  3. Predictors for patient-level CSPCa detection

    Time frame: Up to 3 months

    Independent predictors for patient-level CSPCa detection will be assessed by logistic regression. Predictors include age, race, ethnicity, prostate-specific antigen (PSA), PSA density (PSA/prostate volume measured on magnetic resonance imaging [MRI]), digital rectal examination (DRE) abnormality, PIRADS score, GL AI prediction score, and DL AI prediction score. Classifier will be created using the strong predictors for CSPCa, and its discriminant performance will be assessed by the receiver operating characteristic (ROC) analysis.

  4. Dice score and linear correlation coefficient of CSPCa distribution on radical prostatectomy (RP) specimens and ROI generated by GL AI, DL AI and PIRADS

    Time frame: Up to 3 months

    Distribution of CSPCa on the RP specimen will be annotated on the digitized slides and 3-dimensional reconstructed as ground truth. ROI segmentations according to PIRADS, GL AI prediction heatmap, and DL AI prediction heatmap also will be 3D reconstructed. The spatial concordance between GL-ROI, DL AI-ROI, PIRADS-ROI, and CSPCa distribution on RP specimen will be assessed. The accuracy of the spatial concordance and volume estimation will be analyzed by the Dice score and linear correlation analysis, respectively.

Study contacts

Contact information is provided by the study sponsor or research team.

Ileana Aldana

CONTACT

[email protected]

323-865-0702

Sponsors and collaborators

Lead sponsor

University of Southern California

Other

Collaborators

  • National Cancer Institute (NCI)

Registry information

Official study 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

Important dates

Study start
2025
Primary completion
2027
Study completion
2028
First posted
Sep 9, 2025
Registry last updated
Sep 1, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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