PI-RADS v2.1联合PSA密度对有临床意义前列腺癌的预测价值  

Predictive value of Prostate Imaging Reporting and Data System v2.1 combined with prostate specific antigen density for clinically significant prostate cancer

作　　者：丁志广 钟淑媛 魏晓婷 胡根文 邹锦森 成志强 徐坚民 DING Zhi-guang;ZHONG Shu-yuan;WEI Xiao-ting;HU Gen-wen;ZOU Jin-sen;CHENG Zhi-qiang;XU Jian-min(Department of Radiology,The Second Clinical Medical College of Jinan University,Shenzhen People’s Hospital,Guangdong 518020,China)

机构地区：[1]暨南大学第二临床医学院/深圳市人民医院放射科,广东深圳518020 [2]暨南大学第二临床医学院/深圳市人民医院病理科,广东深圳518020 [3]中山大学附属第八医院放射科,广东深圳518033

出　　处：《影像诊断与介入放射学》2020年第6期426-432,共7页Diagnostic Imaging & Interventional Radiology

基　　金：通信作者:徐坚民,Email:[e-mail]。

摘　　要：目的探讨PI-RADS v2.1联合临床指标对有临床意义前列腺癌(CsPCa)穿刺结果的预测价值。方法回顾性收集2018年1月~2019年6月于我院行MRI检查并经前列腺穿刺活检的243例患者。分析MRI资料并进行前列腺影像报告与数据系统(PI-RADS v2.1)评分,收集患者临床指标,包括年龄、前列腺特异性抗原(PSA)、游离PSA/总PSA(fPSA/tPSA)、前列腺体积(PV)及前列腺特异性抗原密度(PSAD),并进行单因素分析及多因素分析。多因素Logistic回归分析确定CsPCa独立危险因子并建立回归预测模型,比较预测模型与各独立危险因子的曲线下面积(AUC),评估其诊断效能。校准曲线和决策曲线分别用于评估预测模型校准度及其净效益。并对独立危险因子进行等级划分和组合。结果入组患者中CsPCa 57例,无临床意义前列腺癌和前列腺增生共186例。多因素Logistic回归分析显示PI-RADS v2.1评分和PSAD是CsPCa独立危险因子。联合上述独立危险因子建立CsPCa预测模型,其AUC值为0.959。校准曲线显示模型有良好的校准度,决策曲线显示当风险阈值大于等于0.05时可获得净收益。将PI-RADS v2.1评分和PSAD值进行组合,当PI-RADS v2.1评分小于等于2且PSAD小于等于0.27 ng/ml2或PI-RADS v2.1评分等于3且PSAD小于0.17 ng/ml2时,CsPCa阳性率为0%。结论PI-RADS v2.1评分与PSAD联合预测模型对CsPCa穿刺结果的预测具有重要价值,将二者等级划分和组合有助于穿刺前对CsPCa的风险评估和决策。Objective To explore the predictive value of Prostate Imaging Reporting and Data System(PI-RADS)version 2.1 combined with clinical indicators for the puncture results of clinically significant prostate cancer(CSPCa).Methods MRI of 243 patients who underwent prostate biopsy from January 2018 to June 2019 was retrospectively analyzed using PI-RADS v2.1 scoring.Univariate and multivariate analysis was performed on patient’s age,prostate specific antigen(PSA),free PSA/total PSA(fPSA/tPSA),prostate volume(PV)and prostate-specific antigen density(PSAD).The independent risk factors of CSPCa were obtained by multivariate logistic regression analysis and the regression prediction model was established.The area under receiver operating characteristic curve(AUC)of the combined model was compared with each independent risk factor to evaluate its diagnostic efficiency.The calibration curve and the decision curve were used to evaluate the calibration degree of prediction model and its net benefit.The independent risk factors were classified and combined.Results Of 243 patients,there were 57 CSPCa and 186 clinically insignificant prostate cancer and prostatic hyperplasia.The multivariate Logistic regression analysis showed that PI-RADS v2.1 score and PSAD were independent risk factors of CSPCa.The CSPCa prediction model with AUC of 0.959 was established combined with the independent risk factors.The model showed good calibration and the decision curve analyses showed that the net benefit could be obtained at the risk threshold≥0.05.The positive rate of CSPCa was 0%at PI-RADS v2.1 score≤2 and PSAD≤0.27 ng/ml2,or at PI-RADS v2.1 score=3 and PSAD<0.17 ng/ml2.Conclusion The combined prediction model of PI-RADS v2.1 score and PSAD is valuable for predicting CSPCa biopsy results and may aid risk assessment and decision-making before biopsy.

关 键 词：前列腺癌 磁共振成像 穿刺活检 前列腺特异性抗原 

分 类 号：R737.25[医药卫生—肿瘤]