摘要目的 通过构建模型来辅助临床决策从而能够预测第2版前列腺影像报告和数据系统(PI-RADs v2)3分患者罹患临床有意义前列腺癌(CSPCa)的可能性,从而避免不必要的穿刺.方法 回顾性分析2012年1月-2018年7月首都医科大学附属北京友谊医院收治的临床怀疑前列腺癌并行初次前列腺穿刺的218例PI-RADs v2 3分患者的病例资料,平均年龄70.7岁,年龄范围为63 ～ 77岁.其中良性疾病患者137例,临床无意义前列腺癌(CIPCa)患者30例,CSPCa患者51例.构建基于相关临床指标的CSPCa预测模型.计量资料以中位数(四分位间距)[M(P25,P75)]表示,组间比较采用秩和检验;计数资料组间比较采用x2检验.单因素及多因素logistic回归分析用以确定阳性结果的预测因素.以受试者工作特征曲线下面积(AUC)来评估预测模型的诊断性能,校准曲线用于评估模型的高估或低估程度,决策曲线用于确定模型的应用所产生的临床净获益.结果 前列腺癌的检出率为37.2％(81/218),CSPCa的检出率为23.4％ (51/218).CSPCa患者的中位前列腺特异性抗原为12.1 ng/ml,高于CIPCa (9.5 ng/ml)及良性疾病(10.5 ng/ml)患者;CSPCa患者的中位前列腺体积为41.2 ml,低于CIPCa(45.8 ml)及良性疾病(57.3 ml)患者;CSPCa患者的中位前列腺特异性抗原密度(PSAD)为0.28 ng/ml2,高于CIPCa(0.20 ng/ml2)及良性疾病(0.15 mg/ml2)患者.基于年龄、PSAD、病灶位点以及ADC值构建的模型,其AUC大于任一单一因素.校准曲线显示,当阈值大于35％时,模型可能会高估罹患前列腺癌的风险,而当阈值大于60％时,对于CSPCa的预测风险更接近实际风险.决策曲线显示,此模型应用于临床决策会使得临床获益.结论 基于年龄、PSAD、病灶位点及ADC值等指标构建的模型对于PI-RADs v2 3分患者罹患前列腺癌和CSPCa均有着较高的预测价值.此模型可用来提高此类患者CSPCa的检出,且能够避免不必要的穿刺.

abstractsObjective Assist in clinical decision making by building models to predict the probability of clinically significant prostate cancer (CSPCa) with prostate imaging reporting and data system version 2 (PI-RADs v2) 3and avoid unnecessary biopsy.Methods It's a retrospective study which maintained database of 218 consecutive men who received prostate biopsy and with PI-RADs v2 category 3 in Capital Medical University,Beijing Friendship Hospital between January 2012 to July 2018,the average age was 70.7 years,and the age range was 63-77 years.Among them,137 patients with benign diseases,30 patients with clinically insignificant prostate cancer (CIPCa),and 51 patients with CSPCa.Models were established based on clinical variables.The measurement data were expressed as the median (interquartile range) [M(P25,P75)],and the rank sum test was used for comparison between groups;the Chi-square test was used for comparison between the count data groups.The decision curve was used to determine the clinical net benefit unilateral factors generated by the application of the model,univariate and multivariate logistic regression analysis to determine the predictors of positive outcomes.The diagnostic performance of the predictive model was evaluated by the area under the curve (AUC) of receiver operating curve,which was used to assess the overestimation or underestimation of the model,and the decision curve was used to determine the clinical net gain from the application of the model.Results Detection of prostate caner (PCa) and CSPCa in the PI-RADs v2 cohort were 37.2％ (81/218) and 23.4％ (51/218).The median prostate specific antigen of CSPCa patients was 12.1 ng/ml,which was higher than CIPCa (9.5 ng/ml) and benign (10.5 ng/ml) patients.The median prostate volume of CSPCa patients was 41.2 ml,lower than CIPCa (45.8 ml) and benign (57.3 ml) patients.The median prostate special antigen density (PSAD) was 0.28 ng/ml2,higher than CIPCa (0.20 ng/ml2) and benign (0.15 ng/ml2) patients.The predictive power of the developed model,based on age,PSAD,lesion region and ADC value,showed a higher AUC than that of parameters alone.Internally validated calibration curves showed that the nomogram might overestimate the risk of PCa when the threshold was above 35％.As for CSPCa,the predicted risk was closer to actual probability wben the threshold was above 60％.Decision curves showed that a better net benefit was met when the model was used to guide clinical practice.Conclusions The models based on age,PSAD,lesion region and ADC value showed internally validated high predictive value for both PCa and CSPCa.It could be used to improve the detection rate of CSPCa and avoid unnecessary biopsy.