ArticleBMC urology2025
A novel composite index of PSA and periprostatic adipose tissue quantification for enhancing high-grade prostate cancer prediction.
Article in BMC urology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- mpMRI-based clinic-radiomics-deep learning model integrating lesion and PPAT for predicting csPCa in PI-RADS category 3 lesions: a multicenter study.International urology and nephrology · 2026Article
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6 authors.
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Abstract
backgroundTo explore the efficacy of combining MRI-derived quantitative data on Periprostatic adipose tissue (PPAT) with clinical biomarkers, including prostate-specific antigen (PSA), to enhance the high-grade (PCa) screening.
methodsIn a retrospective analysis, we reviewed clinical and pathological records of patients who had undergone prostate MRI between January 2020 and January 2023. Two radiologists measured PPAT metrics - subcutaneous fat thickness (SFT), periprostatic fat thickness (PPFT), periprostatic fat area (PPFA), and periprostatic fat volume (PPFV) - on T1-weighted axial images. Ratios of PPFA to prostate area (PA) (PPFA/PA) and PPFV to prostate volume (PV) (PPFV/PV) were calculated, collinearity testing was performed, and differences between groups for PPAT metrics and PSA levels were analyzed. Selected variables underwent multivariate binary logistic regression to identify independent predictors of high-grade PCa. Model performance was assessed using ROC curves and AUC.
resultsThe study included 215 patients. Significant differences between high- and low-grade PCa groups were observed for PPFA, PPFA/PA, PSA, Prostate specific antigen density (PSAD) and the combined index PSA×PPFA/PA (P ≤ 0.001). Multivariate analysis identified PPFA/PA and PSA levels as independent predictors of high-grade PCa, with odds ratios (OR) of 1.011 (95% CI 1.002-1.021, P = 0.018) and 1.044 (95% CI 1.006-1.082, P = 0.022), respectively. The PSA, PSAD, PSA × PPFA/PA, and composite indicator models demonstrated strong predictive performance, with AUC values of 0.771, 0.796, 0.818, and 0.814, respectively. Among these, the PSA × PPFA/PA model showed superior performance, with an optimal cutoff value of 42.135.
conclusionsThe PSA×PPFA/PA index promises enhanced prediction of high-grade PCa, demonstrating that incorporating PPAT measurements alongside PSA improves screening efficacy and supports more informed clinical decision-making in the management of PCa.
trial registrationNot applicable.
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