摘要目的 结合第2版前列腺影像报告和数据系统(PI-RADS v2)评分系统,分析影响前列腺再次穿刺活检结果的临床病理因素,建立预测有临床意义前列腺癌的列线图模型.方法 回顾性分析2001年1月至2017年4月(北京第一大学第一医院收治前列腺再次穿刺活检病例资料共231例,根据PI-RADS v2评分系统对前列腺可疑病灶进行评分,对临床病理及影像学资料进行多因素Logistic回归分析,建立列线图预测模型.受试者工作特征(ROC)曲线、校准图形及决策曲线分析模型的诊断价值.结果 在231例前列腺再次穿刺活检患者中,有临床意义肿瘤的检出率为25.5％(59/231).多因素Logistic回归分析显示患者年龄前列腺特异抗原(PSA)、前列腺体积、直肠指检及PI-RADS v2评分是有临床意义肿瘤的独立预测因素.ROC曲线分析列线图模型在诊断有临床意义肿瘤的曲线下面积(AUC)最大为0.927(P＜0.001).校准图形显示模型在预测有临床意义肿瘤风险上表现出较好的预测符合度.决策曲线分析显示结合PI-RADS v2后列线图的临床应用价值更高.结论 结合PI-RADS v2评分系统建立前列线图模型的预测效能明显提高,可以提高前列腺再次再次穿刺时有临床意义肿瘤风险的检出,减少不必要的穿刺及无临床意义前列腺癌的检出.

abstractsObjective To develop a nomogram based on prostate imaging reporting and data system version 2 (PI-RADS v2) to predict clinically significant prostate cancer in patients with a prior negative prostate biopsy.Methods The clinical and pathological data of 231 patients who underwent repeat prostate biopsy and multiparametric MRI (mpMRI) were reviewed.Based on PI-RADS v2,the mpMRI results were assigned as PI-RADS grade from 0 to 2.A Logistic regression nomogram for predicting the probabilities of clinically significant prostate cancer were constructed.The performances of the nomogram were assessed using area under the receiver operating characteristic (ROC) curve,calibrations and decision curve analysis.Results Of the total 231 repeat prostate biopsy patients,clinically significant prostate cancer was detected in 59 cases(25.5％).In multivariate Logistic regression analysis,age,prostate specific antigen (PSA),prostate volume (PV),digital rectal examination (DRE) and mpMRI results were significant independent predictors of the diagnosis of clinically significant prostate cancer (P ＜ 0.05).The nomogram with super predictive accuracy were constructed (AUC =0.927,P ＜ 0.001),and exhibited excellent calibration.Decision curve analysis also demonstrated a high net benefit across a wide range of threshold probabilities.Conclusions PI-RADS v2 combined with age,PSA,PV and DRE can predict the probability of clinically significant prostate cancer in patients with negative initial biopsies.The nomogram generated may help the decision-making process in patients with prior benign histology before the performance of repeat biopsy.