title: Developing a new PI-RADS v2-based nomogram for forecasting high-grade prostate cancer.
abstract: OBJECTIVE
  To establish a predictive nomogram for high-grade prostate cancer (HGPCa) in biopsy-naive patients based on the Prostate Imaging-Reporting 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.
  
  
  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).
  
  
  CONCLUSIONS
  The nomogram for forecasting HGPCa is effective and potentially reducing harm from unnecessary prostate biopsy and over-diagnosis.
authors.affiliation: Department of Radiology, Affiliated Hospital of Chengdu University, Chengdu, 610081, China.
authors.forename: X-K
authors.initials: XK
authors.lastname: Niu
authors.affiliation: Department of Cardiology, Affiliated Hospital of North Sichuan Medical College, Sichuan 637000, China.
authors.forename: W-F
authors.initials: WF
authors.lastname: He
authors.affiliation: Department of Radiology, Deyang City People's Hospital, 618000, China.
authors.forename: Y
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Department of Interventional Radiology, Tenth People's Hospital of Tongji University, Shanghai 200072, China. Electronic address: niu051228@163.com.
authors.forename: S K
authors.initials: SK
authors.lastname: Das
authors.affiliation: Department of General Surgery, Affiliated Hospital of Chengdu University, Chengdu 610081, China.
authors.forename: J
authors.initials: J
authors.lastname: Li
authors.affiliation: Department of Radiology, Affiliated Hospital of Chengdu University, Chengdu, 610081, China.
authors.forename: Y
authors.initials: Y
authors.lastname: Xiong
authors.affiliation: Department of Urology, Affiliated Hospital of Chengdu University, Chengdu, 610081, China.
authors.forename: Y-H
authors.initials: YH
authors.lastname: Wang
mesh_terms: D000368:Aged; D001706:Biopsy; D006801:Humans; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D060787:Neoplasm Grading; D049451:Nomograms; D011237:Predictive Value of Tests; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology*; D012189:Retrospective Studies; D012307:Risk Factors