一种用于增强高级别前列腺癌预测的前列腺特异性抗原（PSA）与前列腺周围脂肪组织定量的新型复合指标。

A novel composite index of PSA and periprostatic adipose tissue quantification for enhancing high-grade prostate cancer prediction.

作者信息

Xiong Jie, Liu Yunfan, Qiao Xiaofeng, Ai Guangyong, Ma Jiangqin, He Xiaojing

机构信息

Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, No.76 Linjiang Road,Yuzhong District, Chongqing, 400010, China.

出版信息

BMC Urol. 2025 Aug 11;25(1):199. doi: 10.1186/s12894-025-01884-7.

DOI:10.1186/s12894-025-01884-7

PMID:40790169

Abstract

BACKGROUND

To 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.

METHODS

In 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.

RESULTS

The 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.

CONCLUSIONS

The 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 REGISTRATION

Not applicable.

摘要

背景

探讨将磁共振成像（MRI）得出的前列腺周围脂肪组织（PPAT）定量数据与包括前列腺特异性抗原（PSA）在内的临床生物标志物相结合，以加强高级别前列腺癌（PCa）筛查的效果。

方法

在一项回顾性分析中，我们查阅了2020年1月至2023年1月期间接受前列腺MRI检查的患者的临床和病理记录。两名放射科医生在T1加权轴位图像上测量PPAT指标——皮下脂肪厚度（SFT）、前列腺周围脂肪厚度（PPFT）、前列腺周围脂肪面积（PPFA）和前列腺周围脂肪体积（PPFV）。计算PPFA与前列腺面积（PA）的比值（PPFA/PA）以及PPFV与前列腺体积（PV）的比值（PPFV/PV），进行共线性测试，并分析PPAT指标和PSA水平在各组之间的差异。对选定变量进行多变量二元逻辑回归，以确定高级别PCa的独立预测因素。使用ROC曲线和AUC评估模型性能。

结果

该研究纳入了215名患者。在PPFA、PPFA/PA、PSA、前列腺特异性抗原密度（PSAD）以及联合指标PSA×PPFA/PA方面，高级别和低级别PCa组之间存在显著差异（P≤0.001）。多变量分析确定PPFA/PA和PSA水平为高级别PCa的独立预测因素，其比值比（OR）分别为1.011（95%CI 1.002 - 1.021，P = 0.018）和1.044（95%CI 1.006 - 1.082，P = 0.022）。PSA、PSAD、PSA×PPFA/PA和复合指标模型显示出较强的预测性能，AUC值分别为0.771、0.796、0.818和0.814。其中，PSA×PPFA/PA模型表现更优，最佳临界值为42.135。