An interventional study of Targeted Prostate Biopsy and Prostate Imaging Reporting & Data System in Prostate Carcinoma, sponsored by University of Southern California. Recruiting at 1 site in United States. Open to male participants aged 20 Years and older. Per ClinicalTrials.gov, last updated 2026-09-01.
Sponsored by University of Southern California · Not applicable, Interventional, and Diagnostic
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.
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.
6,367 studies on the registry are indexed under Prostatic Neoplasms; 1,397 are open to participants now.
This study's planned enrollment of 130 is above the median of 58 across 4,821 interventional studies indexed under Prostatic Neoplasms.
Browse Prostatic Neoplasms studies →University of Southern California is the lead sponsor of 773 studies on the registry; 135 are open to participants now.
Of its 68 completed or terminated interventional studies of FDA-regulated products, 32 (47%) have results posted.
Counted across the registry records on this site, refreshed daily.
Exclusion Criteria:
Patients undergo MRI/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.
Procedure: Targeted Prostate Biopsy · Diagnostic Test: Prostate Imaging Reporting & Data System · Diagnostic Test: Deep Learning Artificial Intelligence · Diagnostic Test: Green Learning Artificial Intelligence · Procedure: Radical Prostatectomy
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. Patients undergo up to 12 additional prostate biopsies per SOC. Based on biopsy results, patients will either come off study or undergo radical prostatectomy without hormonal therapy within 180 days from baseline MRI.
Procedure: Targeted Prostate Biopsy · Diagnostic Test: Prostate Imaging Reporting & Data System · Diagnostic Test: Deep Learning Artificial Intelligence · Diagnostic Test: Green Learning Artificial Intelligence · Procedure: Radical Prostatectomy
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. Patients undergo up to 12 additional prostate biopsies per SOC. Based on biopsy results, patients will either come off study or undergo radical prostatectomy without hormonal therapy within 180 days from baseline MRI.
Procedure: Targeted Prostate Biopsy · Diagnostic Test: Prostate Imaging Reporting & Data System · Diagnostic Test: Deep Learning Artificial Intelligence · Diagnostic Test: Green Learning Artificial Intelligence · Procedure: Radical Prostatectomy
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. Patients may also undergo DRE on study.
Procedure: Targeted Prostate Biopsy · Diagnostic Test: Prostate Imaging Reporting & Data System · Diagnostic Test: Deep Learning Artificial Intelligence · Diagnostic Test: Green Learning Artificial Intelligence · Procedure: Radical Prostatectomy
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. Patients undergo up to 12 additional prostate biopsies per SOC. Based on biopsy results, patients will either come off study or undergo radical prostatectomy without hormonal therapy within 180 days from baseline MRI.
Procedure: Targeted Prostate Biopsy · Diagnostic Test: Prostate Imaging Reporting & Data System · Diagnostic Test: Deep Learning Artificial Intelligence · Diagnostic Test: Green Learning Artificial Intelligence · Procedure: Radical Prostatectomy
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. Patients undergo up to 12 additional prostate biopsies per SOC. Based on biopsy results, patients will either come off study or undergo radical prostatectomy without hormonal therapy within 180 days from baseline MRI.
Procedure: Targeted Prostate Biopsy · Diagnostic Test: Prostate Imaging Reporting & Data System · Diagnostic Test: Deep Learning Artificial Intelligence · Diagnostic Test: Green Learning Artificial Intelligence · Procedure: Radical Prostatectomy
Patients undergo MRI/TRUS then a radical prostatectomy (RP), which are performed per standard of care at our institution. PIRADS, GL AI, and DL AI will be used to interpret the MRI/TRUS results prior to RP.
Diagnostic Test: Prostate Imaging Reporting & Data System · Diagnostic Test: Deep Learning Artificial Intelligence · Diagnostic Test: Green Learning Artificial Intelligence · Procedure: Radical Prostatectomy
Undergo targeted prostate biopsy
Also known as: TB, Prostate Biopsy, PBx
PIRADS Assessment
Also known as: PIRADS
Deep Learning (DL) AI predictions
Also known as: DL AI
Green Learning (GL) AI predictions
Also known as: GL AI
Undergo RP
Also known as: RP
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
Detection rates of PIRADS and GL AI-targeted biopsy will be evaluated per index region of interest (ROI), respectively.
Time frame: Up to 3 months
CSPCa detection rate on GL AI-targeted and Deep Learning (DL) AI-targeted biopsies
Detection rates of GL AI and DL AI targeted biopsy will be evaluated per index ROI, respectively.
Time frame: Up to 3 months
Patient-level diagnostic performance of GL AI and PIRADS for CSPCa detection
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.
Time frame: Up to 3 months
Targeted biopsy core characteristics
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.
Time frame: Up to 3 months
Predictors for patient-level CSPCa detection
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.
Time frame: Up to 3 months
Dice score and linear correlation coefficient of CSPCa distribution on radical prostatectomy (RP) specimens and ROI generated by GL AI, DL AI and PIRADS
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.
Time frame: Up to 3 months
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