Developing a new PI-RADS v2-based nomogram for forecasting high-grade prostate cancer

作者：Niu, X. -k.; He, W. -f.; Zhang, Y.; Das, S. K.^*; Li, J.; Xiong, Y.; Wang, Y. -h.

来源：Clinical Radiology, 2017, 72(6): 458-464.

DOI：10.1016/j.crad.2016.12.005

• IN-BORE BIOPSY
• SCORING SYSTEM
• MULTIPARAMETRIC MRI
• PREVENTION TRIAL
• VERSION 2
• ANTIGEN
• VALIDATION
• PREDICTION
• DIAGNOSIS
• ACCURACY

AIM: To establish a predictive nomogram for high-grade prostate cancer (HGPCa) in biopsynaive patients based on the Prostate ImagingeReporting and Data System version 2 (PI-RADS v2), magnetic resonance imaging (MRI)-based prostate volume (PV), MRI-based PV-adjusted prostate-specific antigen density (PSAD), and other classical parameters. @@@ MATERIAL AND METHODS: Between August 2014 and August 2015, 158 men who were eligible for analysis were included as the training cohort. A prediction model for HGPCa was built using backward logistic regression and was presented on a nomogram. The prediction model was evaluated by a validation cohort between September 2015 and March 2016 (n = 89). Histology of all lesions was obtained with MRI-directed transrectal ultrasound (TRUS)-guided targeted and sectoral biopsy. @@@ RESULTS: The multivariate analysis revealed that patient age, PI-RADS v2 score, and adjusted PSAD were independent predictors for HGPCa. The most discriminative cut-off value for the logistic regression model was 0.33; the sensitivity, specificity, positive predictive value, and negative predictive value were 83.3%, 87.4%, 88.4%, and 81.2%, respectively. The diagnostic performance measures retained similar values in the validation cohort (AUC = 0.83). @@@ CONCLUSION: The nomogram for forecasting HGPCa is effective and potentially reducing harm from unnecessary prostate biopsy and over-diagnosis.

• 出版日期2017-6
• 单位同济大学; 成都大学; 川北医学院