Yang Liqin, Jin Pengfei, Wang Ximing, Li Zhiping, Xu Huijing, Zhang Yongsheng, Cui Feng
Department of Radiology, Hangzhou Traditional Chinese Medicine Hospital Affiliated to Zhejiang Chinese Medical University, 453# Tiyuchang Road, Hangzhou, 310007, China.
Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, China.
Cancer Imaging. 2025 Jul 1;25(1):83. doi: 10.1186/s40644-025-00905-w.
To develop and validate a multimodal scoring system integrating clinical, radiological, and pathological variables to preoperatively predict extraprostatic extension (EPE) in prostate cancer (PCa).
This retrospective study included 667 PCa patients divided into a derivation cohort and two validation cohorts. Evaluated parameters comprised prostate-specific antigen density (PSAD), curvilinear contact length (CCL), lesion longest diameter (LD), National Cancer Institute EPE grade (NCI_EPE), International Society of Urological Pathology grade (ISUP), and other relevant variables. Independent predictors were identified through univariate and multivariate regression analysis to construct a logistic model. Coefficients from this model were then weighted to establish a scoring system. The predictive performance of the NCI_EPE, logistic model, and scoring system was systematically evaluated and compared. Finally, the scoring system was stratified into four distinct risk categories.
Multivariate analysis identified NCI_EPE, PSAD, CCL/LD, and ISUP as independent predictors of EPE. In the derivation and validation cohorts, the scoring system demonstrated robust predictive accuracy for EPE, with AUCs of 0.849, 0.830, and 0.847, respectively. These values outperformed the NCI_EPE (Derivation cohort: 0.849 vs. 0.750, P < 0.003, Validation cohort 1: 0.830 vs. 0.736, P = 0.138, Validation cohort 2: 0.837 vs. 0.715, P = 0.003) and were comparable to the logistic model (Derivation cohort: 0.849 vs. 0.860, P = 0.228, Validation cohort 1: 0.830 vs. 0.849, P = 0.711, Validation cohort 2: 0.837 vs. 0.843, P = 0.738). Decision curve analysis revealed higher net clinical benefit for both the scoring system and logistic model compared to the NCI_EPE. Risk stratification using the scoring system categorized patients into four tiers: low (0-3), intermediate-low (4-6), intermediate-high (7-9), and high risk (10-12) with corresponding mean EPE probabilities of 9.9%, 26.0%, 52.0%, and 85.0%. These probabilities closely aligned with observed pT3 incidences in the derivation and validation cohorts.
The scoring system provides enhanced predictive accuracy for EPE, preoperatively stratifying patients into distinct risk categories to facilitate personalized therapeutic strategies.
开发并验证一种整合临床、放射学和病理学变量的多模式评分系统,以术前预测前列腺癌(PCa)的前列腺外侵犯(EPE)情况。
这项回顾性研究纳入了667例PCa患者,分为一个推导队列和两个验证队列。评估参数包括前列腺特异性抗原密度(PSAD)、曲线接触长度(CCL)、病变最长直径(LD)、美国国立癌症研究所EPE分级(NCI_EPE)、国际泌尿病理学会分级(ISUP)以及其他相关变量。通过单因素和多因素回归分析确定独立预测因素,构建逻辑模型。然后对该模型的系数进行加权,以建立评分系统。系统地评估并比较NCI_EPE、逻辑模型和评分系统的预测性能。最后,将评分系统分为四个不同的风险类别。
多因素分析确定NCI_EPE、PSAD、CCL/LD和ISUP为EPE的独立预测因素。在推导队列和验证队列中,评分系统对EPE显示出强大的预测准确性,其曲线下面积(AUC)分别为0.849、0.830和0.847。这些值优于NCI_EPE(推导队列:0.849对0.750,P < 0.003;验证队列1:0.830对0.736,P = 0.138;验证队列2:0.837对0.715,P = 0.003),并且与逻辑模型相当(推导队列:0.849对0.860,P = 0.228;验证队列1:0.830对0.849,P = 0.711;验证队列2:0.837对0.843,P = 0.738)。决策曲线分析显示,与NCI_EPE相比,评分系统和逻辑模型均具有更高的净临床获益。使用评分系统进行风险分层将患者分为四个层级:低风险(0 - 3分)、中低风险(4 - 6分)、中高风险(7 - 9分)和高风险(10 - 12分),相应的平均EPE概率分别为9.9%、26.0%、52.0%和85.0%。这些概率与推导队列和验证队列中观察到的pT3发生率密切一致。
该评分系统为EPE提供了更高的预测准确性,术前将患者分层为不同的风险类别,以促进个性化治疗策略的制定。