双参数MRI联合临床相关指标对前列腺特异性抗原灰区前列腺癌的诊断价值  

Diagnostic value of biparametric MRI combined with realated clinical factors for prostate cancer in prostate specific antigen gray area

作　　者：檀双秀 杨硕 张跃跃[1] 魏超刚 王焰峰 潘鹏 赵文露[1] 沈钧康[1] TAN Shuangxiu;YANG Shuo;ZHANG Yue yue;WEI Chaogang;WANG Yanfeng;PAN Peng;ZHAO Wenlu;SHEN Junkang(Department of Imaging,the Second Affiliated Hospital of Soochow University,Suzhou,Jiangsu Province 215004,China)

机构地区：[1]苏州大学附属第二医院影像科,江苏苏州215004

出　　处：《实用放射学杂志》2021年第11期1847-1851,共5页Journal of Practical Radiology

基　　金：国家自然基金青年项目(81801754);苏州市科技发展计划(SS2019012)。

摘　　要：目的探讨基于2.1版前列腺影像报告与数据系统(PI-RADS v2.1)的双参数磁共振成像(bp-MRI)联合临床相关指标对前列腺特异性抗原(PSA)灰区中临床显著性前列腺癌(csPCa)的诊断价值。方法回顾性分析211例PSA灰区(4~10 ng/mL)患者的临床、影像及病理资料,其中csPCa组35例,非csPCa组176例。根据PI-RADS v2.1评分标准对前列腺主病灶进行bp-MRI评分。对年龄、总前列腺特异性抗原(tPSA)、游离前列腺特异性抗原(fPSA)、游离与总前列腺特异性抗原比值(f/tPSA)、前列腺体积(PV)、前列腺特异性抗原密度(PSAD)及bp-MRI评分进行单因素和多因素分析,确定csPCa独立预测因子,并建立联合预测模型。运用受试者工作特征(ROC)曲线评估各独立预测因子及联合预测模型对csPCa的诊断效能,并通过Z检验对曲线下面积(AUC)进行两两比较。结果f/tPSA、PV、PSAD及bp-MRI评分在csPCa组和非csPCa组间存在统计学差异(均P<0.05)。PV和bp-MRI评分为csPCa的独立预测因子(OR=0.974,P=0.024;OR=4.206;P<0.001)。PV、bp-MRI评分和二者联合预测模型诊断csPCa的AUC值分别为0.684、0.856、0.878,且PV和bp-MRI评分间,PV和联合预测模型间,bp-MRI评分和联合预测模型间AUC值差异均有统计学意义(Z=3.416,P=0.001;Z=4.562,P<0.001;Z=2.059,P=0.040)。结论Bp-MRI有助于检出PSA灰区csPCa,与PV联合应用后,可进一步提高对csPCa的检出效能,减少患者不必要的穿刺。Objective To evaluate the value of diagnosis for clinically significant prostate cancer(csPCa)in prostate specific antigen(PSA)gray area by combining biparametric magnetic resonance imaging(bp-MRI)with related clinical factors on the basis of the prostate imaging reporting and data system version 2.1(PI-RADS v2.1).Methods The clinical,imaging and pathological data of 211 patients with PSA levels of 4-10 ng/mL were analyzed retrospectively.There were 35 cases in the csPCa group and 176 cases in the non-csPCa group.According to P-I RADS v2.1 protocol,the primary lesions of prostate were scored by bp-MRI.Univariate and multivariate Logistic regression analysis were performed for the influencing factors[age,total prostate specific antigen(tPSA),free prostate specific antigen(fPSA),free to total prostate specific antigen ratio(f/tPSA),prostate volume(PV),prostate specific antigen density(PSAD)and bp-MRI scoring]to determine independent predictors of csPCa,and then a hybrid prediction model was proposed.The receiver operating characteristic(ROC)curve was used to evaluate the diagnostic efficacy of the independent predictors and the hybrid prediction model for csPCa,and the area under the curve(AUC)was compared in pairs by Z test.Results There were statistical differences in f/tPSA,PV,PSAD,and bp-MRI scoring between the csPCa and non-csPCa groups(P<0.05).The PV and bp-MRI scoring were independent predictors for csPCa(OR=0.974,P=0.024;OR=4.206;P<0.001).The AUC of PV,bp-MRI scoring and the hybrid prediction model were 0.684,0.856 and 0.878,and there were statistically significant differences between each other(Z=3.416,P=0.001;Z=4.562,P<0.001;Z=2.059,P=0.040).Conclusion Bp-MRI can help to detect csPCa in PSA gray area,and the combination of it with PV can further to improve diagnostic efficacy for csPCa which may reduce unnecessary biopsy.

关 键 词：前列腺癌 双参数磁共振成像 前列腺影像报告与数据系统 前列腺体积 

分 类 号：R737.25[医药卫生—肿瘤] R445.2[医药卫生—临床医学]