Objective: To develop a predictive nomogram based on multi-parametric magnetic resonance imaging (mpMRI) information to identify men more likely to have a cancer diagnosed on repeat prostate biopsy. Methods: The clinical data of 237 patients who received repeat prostate biopsy after initial negative biopsy from Department of Urology of Peking University First Hospital between January 2001 and August 2016 was reviewed. Patient age, body mass index (BMI), serum total prostate-specific antigen (PSA), percent free PSA (f/t), prostate volume (PV), PSA density (PSAD), PSA velocity (PSAV), digital rectal examination (DRE), transrectal ultrasound (TRUS)and mpMRI results were included in the univariate and multivariate analysis. A nomogram was developed using selected variables and the area under the receiver operating characteristic (ROC) curve was calculated as a measure of discrimination. Results: A total of 76 patients (32.07%) had prostate cancer (PCa) detected on repeat biopsy. Based on univariate and multivariate logistic regression analysis, the patient age, PSA, PV, DRE and mpMRI results were independent predictors for the diagnosis of PCa on repeat biopsy. The current nomogram performed well (AUC=0.910) and showed excellent calibration. Conclusions: Multi-parametric magnetic resonance imaging combined with age, PSA, PV and DRE can predict the probability of PCa in patients with initial negative biopsy. The nomogram might help in decision-making for men with prior benign histology before the performance of repeat biopsy.
目的： 探讨初次前列腺穿刺活检阴性再次穿刺结果的预测因素，建立基于多参数磁共振成像(mpMRI)预测前列腺重复穿刺活检结果的列线图模型。 方法： 选择2001年1月至2016年6月，在北京大学泌尿外科研究所，国家泌尿男生殖系研究中心初次穿刺活检诊断为前列腺良性病变的237例患者行重复穿刺，记录患者的年龄、体质指数(BMI)、血清总前列腺特异抗原(PSA)、游离PSA/总PSA(f/t)、前列腺体积(PV)、PSA密度(PSAD)、PSA速率(PSAV)、直肠指检(DRE)、经直肠超声(TRUS)和mpMRI结果。单因素和多因素Logistic回归分析筛选变量，并在此基础上构建预测前列腺重复穿刺活检结果的列线图模型，再通过内部验证该模型的预测效能。 结果： 在237例前列腺重复穿刺活检的患者中，前列腺癌(PCa)的检出率为32.07%(76/237)。患者的年龄、PSA、PV、DRE和mpMRI结果是重复穿刺活检阳性的独立预测因素。内部验证该模型的AUC值为0.910,同时有较好的校准度。 结论： 我们建立了一个基于mpMRI结果，用于预测前列腺重复穿刺活检结果的列线图模型，经内部验证显示有较好的预测效能。.
Zhonghua yi xue za zhi. 2018 Jan 09 [Epub]
C Huang, G J Ji, G Song, H Wang, Y K Chen, L Q Zhou
Department of Urology, Peking University First Hospital, Institute of Urology, Peking University, National Urological Cancer Center, Beijing 100034, China.