Huang C, Song G, Wang H, Ji G J, Chen Y K, He Q, Zhou L Q
Department of Urology, Peking University First Hospital, Institute of Urology, Peking University, National Urological Cancer Center, Beijing 100034, China.
Zhonghua Yi Xue Za Zhi. 2018 Aug 28;98(32):2559-2563. doi: 10.3760/cma.j.issn.0376-2491.2018.32.005.
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. 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. 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 (<0.05). The nomogram with super predictive accuracy were constructed (AUC=0.927, <0.001), and exhibited excellent calibration. Decision curve analysis also demonstrated a high net benefit across a wide range of threshold probabilities . 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.
基于前列腺影像报告和数据系统第2版(PI-RADS v2)开发一种列线图,以预测既往前列腺活检阴性患者的临床显著性前列腺癌。回顾了231例行重复前列腺活检和多参数磁共振成像(mpMRI)患者的临床和病理数据。根据PI-RADS v2,将mpMRI结果指定为PI-RADS 0至2级。构建了用于预测临床显著性前列腺癌概率的逻辑回归列线图。使用受试者操作特征(ROC)曲线下面积、校准和决策曲线分析评估列线图的性能。在231例重复前列腺活检患者中,59例(25.5%)检测到临床显著性前列腺癌。在多变量逻辑回归分析中,年龄、前列腺特异性抗原(PSA)、前列腺体积(PV)、直肠指检(DRE)和mpMRI结果是临床显著性前列腺癌诊断的显著独立预测因素(<0.05)。构建了具有超高预测准确性的列线图(AUC=0.927,<0.001),并表现出良好的校准。决策曲线分析也显示在广泛的阈值概率范围内具有较高的净效益。PI-RADS v2结合年龄、PSA、PV和DRE可以预测初始活检阴性患者临床显著性前列腺癌的概率。生成的列线图可能有助于在重复活检前对既往组织学为良性的患者进行决策。