Developing a nomogram based on multiparametric magnetic resonance imaging for forecasting high-grade prostate cancer to reduce unnecessary biopsies within the prostate-specific antigen gray zone

BMC Medical Imaging

Research Article

Jun Li¹  Susant Kumar Das²  Xiang-ke Niu³  Chao-bing Yang³  Yan Xiong³  Tao Peng³ 

[1] Department of General Surgery, Affiliated Hospital of Chengdu University, No. 82 2nd North Section of Second Ring Road, 610081, Chengdu, Sichuan, China;Department of Intervention Radiology, Tenth People’s Hospital of Tongji University, 200072, Shanghai, China;Department of Radiology, Affiliated Hospital of Chengdu University, 610081, Chengdu, China;

关键词: Prostate cancer;    Prostate-specific antigen;    Magnetic Resonance Imaging;    PI-RADS;    Nomogram;   

DOI  :  10.1186/s12880-017-0184-x

 received in 2016-12-05, accepted in 2017-01-26,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundSince 1980s the application of Prostate specific antigen (PSA) brought the revolution in prostate cancer diagnosis. However, it is important to underline that PSA is not the ideal screening tool due to its low specificity, which leads to the possible biopsy for the patient without High-grade prostate cancer (HGPCa). Therefore, the aim of this study was to establish a predictive nomogram for HGPCa in patients with PSA 4–10 ng/ml based on Prostate Imaging Reporting and Data System version 2 (PI-RADS v2), MRI-based prostate volume (PV), MRI-based PV-adjusted Prostate Specific Antigen Density (adjusted-PSAD) and other traditional classical parameters.MethodsBetween January 2014 and September 2015, Of 151 men who were eligible for analysis were formed the training cohort. A prediction model for HGPCa was built by using backward logistic regression and was presented on a nomogram. The prediction model was evaluated by a validation cohort between October 2015 and October 2016 (n = 74). The relationship between the nomogram-based risk-score as well as other parameters with Gleason score (GS) was evaluated. All patients underwent 12-core systematic biopsy and at least one core targeted biopsy with transrectal ultrasonographic guidance.ResultsThe multivariate analysis revealed that patient age, PI-RADS v2 score and adjusted-PSAD were independent predictors for HGPCa. Logistic regression (LR) model had a larger AUC as compared with other parameters alone. The most discriminative cutoff value for LR model was 0.36, the sensitivity, specificity, positive predictive value and negative predictive value were 87.3, 78.4, 76.3, and 90.4%, respectively and the diagnostic performance measures retained similar values in the validation cohort (AUC 0.82 [95% CI, 0.76–0.89]). For all patients with HGPCa (n = 50), adjusted-PSAD and nomogram-based risk-score were positively correlated with the GS of HGPCa in PSA gray zone (r = 0.455, P = 0.002 and r = 0.509, P = 0.001, respectively).ConclusionThe nomogram based on multiparametric magnetic resonance imaging (mp-MRI) for forecasting HGPCa is effective, which could reduce unnecessary prostate biopsies in patients with PSA 4–10 ng/ml and nomogram-based risk-score could provide a more robust parameter of assessing the aggressiveness of HGPCa in PSA gray zone.

【 授权许可】

CC BY   
© The Author(s). 2017