基于双参数磁共振成像PI-RADS v2的列线图模型对前列腺临床显著癌的诊断价值分析  

A nomogram based on biparametric prostate magnetic resonance imaging PI-RADS v2 to predict clinically significant prostate cancer

作　　者：王钧 王志兵 陆应军[2] WANG Jun;WANG Zhibing;LU Yingjun(Department of Radiology,Huai’an hospital,Huai’an 223200,Jiangsu Province,China;Department of Radiology,The People’s Hospital of Funing Country,Yancheng 224400,Jiangsu Province,China)

机构地区：[1]淮安市淮安医院影像科,江苏淮安223200 [2]阜宁县人民医院影像科,江苏盐城224400

出　　处：《肿瘤影像学》2020年第6期579-587,共9页Oncoradiology

摘　　要：目的:探讨基于双参数磁共振成像(biparametric magnetic resonance imaging,bp-MRI)第二版前列腺影像报告和数据系统(Prostate Imaging Reporting and Data System version 2,PI-RADS v2)联合临床指标建立的列线图模型对前列腺临床显著癌(clinically significant prostate cancer,cs-PCa)风险的预测能力。方法:回顾并分析2015年1月—2019年12月经穿刺标本病理学检查证实的251例前列腺bp-MRI检查的患者临床及影像学资料,包括PI-RADS v2评分、年龄、总前列腺特异性抗原(total prostate-specific antigen,t-PSA)、游离PSA(free PSA,f-PSA)、游离PSA比值(percent free PSA,f/t-PSA)、前列腺体积(prostate volume,PV)和PSA密度(PSA density,PSAD)。多因素logistic回归分析确定诊断cs-PCa的独立预测指标,建立列线图模型。应用受试者工作特征(receiver operating characteristic,ROC)曲线比较预测模型和各独立预测指标对cs-PCa的诊断效能。结果:PI-RADS v2评分、年龄和PV是诊断cs-PCa的独立预测指标(P<0.05)。构建的列线图预测模型有良好的预测准确度(C-index值为0.920)。预测模型诊断cs-PCa曲线下面积(area under curve,AUC)为0.932,显著高于PI-RADS v2评分(0.864,P<0.001)、PV(0.754,P<0.001)和年龄(0.676,P<0.001)。预测模型诊断cs-PCa的灵敏度和特异度分别为90.3%、85.2%,均高于PI-RADS v2评分(85.5%,76.2%)、PV(71.0%,69.3%)和年龄(85.5%,41.3%)。结论:基于bp-MRI PI-RADS v2评分、PV和年龄建立的列线图模型有较高的预测价值,能提高对cs-PCa的诊断能力,具有良好的临床实用价值。Objective:To evaluate the predictive value of a nomogram constructed by Prostate Imaging Reporting and Data System version 2(PI-RADS v2)based on biparametric magnetic resonance imaging(bp-MRI)combined with clinical indicators for the diagnosis of clinically significant prostate cancer(cs-PCa).Methods:Clinical and imaging data of 251 patients who underwent prostate bp-MRI and confirmed pathologically by transrectal ultrasound-guided prostate biopsy from Jan.2015 to Dec.2019 were retrospectively analyzed,including PI-RADS v2 score,total prostate-specific antigen(t-PSA),free PSA(f-PSA),percent free PSA(f/t-PSA),prostate volume(PV),and PSA density(PSAD).Multivariate logistic regression analysis was performed to determine independent predictors for the diagnosis of cs-PCa,thereby establishing a nomogram predictive model and internally validated its predictive accuracy and consistency.The receiver operating characteristic(ROC)curve was used to compare diagnostic performance in the predictive model and these independent predictors for cs-PCa.Results:PI-RADS v2 score based on bp-MRI,age and PV were independent predictors of cs-PCa(P<0.05).The predictive nomogram based on these independent predictors was developed and proven to have a satisfactory prediction accuracy with C-index of 0.920 for the internal validation.The area under curve(AUC)in the predictive model was 0.932,significantly greater than those in PI-RADS v2 score(0.864,P<0.001),PV(0.754,P<0.001)and age(0.676,P<0.001).In addition,diagnostic sensitivity and specificity for the predictive model for cs-PCa were 90.3%and 85.2%,which higher than those for PI-RADS v2 score(85.5%,76.2%),PV(71.0%,69.3%)and age(85.5%,41.3%).Conclusion:The predictive nomogram established by PI-RADS v2 score based on bp-MRI,PV and age shows a satisfactory predictive value for cs-PCa,which can improve the diagnostic performance and has a preferable clinical practical value.

关 键 词：前列腺癌 列线图 前列腺影像报告和数据系统 

分 类 号：R737.1[医药卫生—肿瘤] R445.2[医药卫生—临床医学]