PI-RADSv2.1联合PSAMR在tPSA<20ng/mL的前列腺癌诊断中的价值

Value of PI-RADS v2.1 combined with PSAMR in the diagnosis of prostate cancer with tPSA<20 ng/mL

目的:探索前列腺特异性抗原质量比(prostatespecific antigen mass ratio,PSAMR)在总前列腺特异性抗原(total prostate-specific antigen,tPSA)<20ng/mL的前列腺癌(prostate cancer,PCa)诊断中的价值。并结合2.1版前列腺影像报告与数据系统(prostate imagingr eporting and data system,PI-RADSv2.1)评分进一步建立预测PCa风险的预测模型。方法:回顾性分析2021年7月—2023年3月期间皖南医学院弋矶山医院收治的382例行MRITRUS融合靶向活检的可疑PCa患者的临床资料。入组患者的tPSA均在4~20ng/mL之间,且穿刺前均行多参数磁共振(multi-parameter magnetic resonance imaging,mpMRI)检查,并按照PI-RADSv2.1进行评分。其中2021年7月—2022年9月的患者为开发队列,2022年10月—2023年3月的患者为验证队列。分别比较2队列PCa组和非PCa组间年龄、tPSA、游离/总前列腺特异性抗原(f/tPSA)、前列腺体积(prostate volume,PV)、PI-RADSv2.1评分、PSAMR的差异。采用单因素和多因素logistic回归分析探究PSAMR及其他指标与PCa发生的关系,其中多因素分析采用向后逐步回归法。根据多因素分析结果建立预测模型,分别在2队列及整体患者中以受试者工作特征(receiver operating characteristic,ROC)曲线、校准曲线及决策曲线分析(decision curve analysis,DCA)评估模型预测性能。结果:本研究共纳入342例患者,其中病理结果显示为前列腺腺癌的患者93例,非PCa的患者249例。开发队列患者共210例,PCa患者58例,非PCa患者152例。验证队列患者132例,PCa患者35例,非PCa患者97例。基于开发队列单因素和多因素分析显示,PSAMR(OR=5.467,P=0.020)是PCa发生的危险因素。纳入构建预测模型的因素包括年龄、前列腺体积、PI-RADSv2.1评分和PSAMR。预测模型在开发队列、验证队列和总体患者的ROC曲线下面积分别为0.921、0.934和0.924,诊断效能良好。校准曲线显示预测准确性良好。DCA曲线显示该预测模型具有临床应用价值。结论:在tPSA 4~20ng/mL时,PSAMR是PCa的危险因素。纳入PI-RADSv2.1评分和PSAMR构建的预测模型可为前列腺穿刺活检决策提供参考。

Objective:To explore the value of prostate specific antigen mass ratio(PSAMR)in the diagnosis of prostate cancer(PCa)with total prostate-specific antigen(tPSA)<20 ng/mL.The prediction model of PCa risk was further established.Methods:The clinical data of 382 patients with suspected PCa who underwent MRITRUS fusion targeted biopsy admitted to Yijishan Hospital of Wannan Medical College from July 2021 to March 2023 were retrospectively analyzed.The tPSA of the enrolled patients was between 4 and 20 ng/mL.Multi-parameter magnetic resonance imaging(mpMRI)was performed before puncture,and the score was performed according to prostate imaging reporting and data system v2.1(PI-RADS v2.1).Among these,patients from July 2021 to September 2022 were the development cohort and patients from October 2022 to March 2023 were the validation cohort.Age,tPSA,f/tPSA,prostate volume(PV),and PI-RADS v2.1 were compared between the two cohorts of PCa and non-PCa groups,respectively.Univariate and multivariate logistic regression analysis were used to explore the relationship between PSAMR,other indicators and prostate cancer development,using stepwise backward regression.Based on the results of multivariate analysis,the prediction performance was evaluated by the receiver operating characteristic(ROC)curve,calibration curve,and decision curve analysis(DCA).Results:A total of 342 patients were included in this study,including 93 patients with pathological findings of prostate adenocarcinoma and 249 patients with non-PCa.There were 210 patients in the development cohort,58 with PCa and 152 without PCa.The validation cohort included 132 patients,35 with PCa and 97 without PCa.Based on the univariate and multivariate analysis of the development cohort,PSAMR(OR=5.467,P=0.020)was a risk factor for the development of PCa.Factors included in constructing the prediction models included age,PV,PI-RADS v2.1 score,and PSAMR.The predictive model had the area under the ROC curve for the development cohort,validation cohort and overall patients 0.921,0.934,and 0.924,respectively,with good diagnostic efficacy.The calibration curve showed a good predictive accuracy.DCA curve showed that the prediction model had clinical application value.Conclusion:PSAMR is a risk factor for PCa at tPSA 4 to 20 ng/mL.The prediction model built with PSAMR and PI-RADS v2.1 can provide reference for prostate biopsy decision-making.