title: Prospective multi-institutional study evaluating the performance of prostate cancer risk calculators.
abstract: OBJECTIVE
  Prostate cancer risk calculators incorporate many factors to evaluate an individual's risk for prostate cancer. We validated two common North American-based, prostate cancer risk calculators.
  
  
  METHODS
  We conducted a prospective, multi-institutional study of 2,130 patients who underwent a prostate biopsy for prostate cancer detection from five centers. We evaluated the performance of the Sunnybrook nomogram-based prostate cancer risk calculator (SRC) and the Prostate Cancer Prevention Trial (PCPT) -based risk calculator (PRC) to predict the presence of any cancer and high-grade cancer. We examined discrimination, calibration, and decision curve analysis techniques to evaluate the prediction models.
  
  
  RESULTS
  Of the 2,130 patients, 867 men (40.7%) were found to have cancer, and 1,263 (59.3%) did not have cancer. Of the patients with cancer, 403 (46.5%) had a Gleason score of 7 or more. The area under the [concentration-time] curve (AUC) for the SRC was 0.67 (95% CI, 0.65 to 0.69); the AUC for the PRC was 0.61 (95% CI, 0.59 to 0.64). The AUC was higher for predicting aggressive disease from the SRC (0.72; 95% CI, 0.70 to 0.75) compared with that from the PRC (0.67; 95% CI, 0.64 to 0.70). Decision curve analyses showed that the SRC performed better than the PRC for risk thresholds of more than 30% for any cancer and more than 15% for aggressive cancer.
  
  
  CONCLUSIONS
  The SRC performed better than the PRC, but neither one added clinical benefit for risk thresholds of less than 30%. Further research is needed to improve the AUCs of the risk calculators, particularly for higher-grade cancer.
authors.affiliation: Sunnybrook Health Sciences Centre, 2075 Bayview Ave, Room MG-406, Toronto, Ontario, Canada. robert.nam@utoronto.ca
authors.forename: Robert K
authors.initials: RK
authors.lastname: Nam
authors.forename: Michael W
authors.initials: MW
authors.lastname: Kattan
authors.forename: Joseph L
authors.initials: JL
authors.lastname: Chin
authors.forename: John
authors.initials: J
authors.lastname: Trachtenberg
authors.forename: Rajiv
authors.initials: R
authors.lastname: Singal
authors.forename: Ricardo
authors.initials: R
authors.lastname: Rendon
authors.forename: Laurence H
authors.initials: LH
authors.lastname: Klotz
authors.forename: Linda
authors.initials: L
authors.lastname: Sugar
authors.forename: Christopher
authors.initials: C
authors.lastname: Sherman
authors.forename: Jonathan
authors.initials: J
authors.lastname: Izawa
authors.forename: David
authors.initials: D
authors.lastname: Bell
authors.forename: Aleksandra
authors.initials: A
authors.lastname: Stanimirovic
authors.forename: Vasundara
authors.initials: V
authors.lastname: Venkateswaran
authors.forename: Eleftherios P
authors.initials: EP
authors.lastname: Diamandis
authors.forename: Changhong
authors.initials: C
authors.lastname: Yu
authors.forename: D Andrew
authors.initials: DA
authors.lastname: Loblaw
authors.forename: Steven A
authors.initials: SA
authors.lastname: Narod
mesh_terms: D000368:Aged; D019540:Area Under Curve; D014408:Biomarkers, Tumor / Q000097:blood*; D001706:Biopsy; D003661:Decision Support Techniques; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D049451:Nomograms; D011237:Predictive Value of Tests; D011446:Prospective Studies; D017430:Prostate-Specific Antigen / Q000097:blood*; D011471:Prostatic Neoplasms / Q000097:blood / Q000175:diagnosis* / Q000517:prevention & control*; D012372:ROC Curve; D018570:Risk Assessment; D012307:Risk Factors; D012680:Sensitivity and Specificity
chemical_list: D014408:Biomarkers, Tumor; D017430:Prostate-Specific Antigen