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

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.