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Population-Specific Radiomics From Biparametric Magnetic Resonance Imaging Improves Prostate Cancer Risk Stratification in African American Men

Year: 2025

Type: article

Abstract: Purpose: To quantify population-specific differences in prostate cancer (PCa) presentation between African American (AA) and White (W) men on MRI using radiomics. Materials and Methods: We identified N = 149 men with PCa who underwent 3T MRI, a confirmatory biopsy and for whom self-reported race was available. Patient studies were partitioned into training (D Tr ) and hold-out test set (D Te ). Three hundred radiomic features quantifying textural patterns were extracted from radiologist delineated PCa regions of interest (ROI) on biparametric MRI. Features with significant differences ( P < .05) between clinically significant (csPCa) and insignificant (ciPCa) PCa were identified. Machine learning models were trained separately for AA and W men (C AA , C W ) on D Tr to distinguish csPCa and ciPCa. Validation on D Te was assessed for AUC and compared against a population agnostic model (C PA ) in combination with clinical parameters (age, PSA, Prostate Imaging Reporting and Diagnostic System and tumor volume). Results: Radiomic features from PCa ROIs on biparametric MRI associated with csPCa were observed to be different in AA compared with W men, especially in the peritumoral region. Population-specific radiomic models outperformed similarly trained C PA models (AUC = 0.84, 0.57 with C AA , C PA ; P < .05) in AA men on D Te . Similar findings were observed for W men (AUC = 0.71, 0.60 with C W , C PA ; P < .05). Integrating clinical and radiomics further improved the risk stratification for AA men (AUC = 0.90) and W men (AUC = 0.75). Conclusions: Accounting for population-specific differences in radiomics may enable improved PCa risk stratification at MRI among AA men compared with a population agnostic approach.

Source: JU Open Plus

DOI: 10.1097/ju9.0000000000000310