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.
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
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
Parallel
Targeted Prostate Biopsy
Procedure/SurgeryUndergo targeted prostate biopsy
Prostate Imaging Reporting & Data System
Diagnostic testPIRADS Assessment
Deep Learning Artificial Intelligence
Diagnostic testDeep Learning (DL) AI predictions
Green Learning Artificial Intelligence
Diagnostic testGreen Learning (GL) AI predictions
Radical Prostatectomy
Procedure/SurgeryUndergo RP
7
Treatment groupsSee each treatment group below.
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.
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.
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.
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.
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.
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.
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University of Southern California
Lead sponsor
National Cancer Institute (NCI)
Collaborator
