title: A nomogram for prediction of prostate cancer on multi-core biopsy using age, serum prostate-specific antigen, prostate volume and digital rectal examination in Singapore.
abstract: OBJECTIVE
  To develop and internally validate two nomograms for predicting the probability of overall and clinically-significant prostate cancer on initial biopsy in a Singaporean population.
  
  
  METHODS
  Data were collected from men undergoing initial prostate biopsy at a single center. The indications for biopsy were serum prostate-specific antigen (PSA) ≥4.0 ng/mL or suspicious digital rectal examination (DRE) findings. Men with PSA >30 ng/mL were excluded. Age, PSA, prostate volume (PV) and DRE were predictors included in our logistic regression model and used to construct two nomograms for overall prostate cancer and clinically-significant (Gleason sum ≥7) cancer detection. Predictive accuracies of our nomograms were assessed using area under curve (AUC) of their receiver-operator characteristic curves. Internal validation was performed using the bootstrap method. Our nomograms were compared to a model based on PSA alone using AUC and decision curve analysis (DCA).
  
  
  RESULTS
  Out of 672 men analyzed, our positive biopsy rate was 26.2% (n = 176), of which 63.6% (n = 112) had clinically significant disease. Age, PSA, PV and DRE status were all independent risk factors for both overall prostate cancer detection as well as clinically-significant cancer detection (all P < 0.05). Our nomogram outperformed serum PSA for both overall and clinically-significant cancer detection (0.736 vs 0.642, P < 0.001 and 0.793 vs 0.696, P < 0.001, respectively). Using DCA, our nomograms had superior net benefit and net reduction in biopsy rate compared to PSA alone.
  
  
  CONCLUSIONS
  Our nomograms have been shown to be superior to PSA alone, on both AUC and DCA. However, it warrants external validation.
authors.affiliation: Department of Urology, Tan Tock Seng Hospital, Singapore.
authors.forename: Alvin
authors.initials: A
authors.lastname: Lee
authors.affiliation: Department of Urology, Tan Tock Seng Hospital, Singapore.
authors.forename: Joel
authors.initials: J
authors.lastname: Lim
authors.affiliation: Clinical Research and Innovation Office, Tan Tock Seng Hospital, Singapore.
authors.forename: Xiao
authors.initials: X
authors.lastname: Gao
authors.affiliation: Clinical Research and Innovation Office, Tan Tock Seng Hospital, Singapore.
authors.forename: Lizhen
authors.initials: L
authors.lastname: Liu
authors.affiliation: Department of Urology, Tan Tock Seng Hospital, Singapore.
authors.forename: Sing Joo
authors.initials: SJ
authors.lastname: Chia
mesh_terms: D000367:Age Factors; D000368:Aged; D019540:Area Under Curve; D001706:Biopsy / Q000379:methods / Q000706:statistics & numerical data; D051517:Digital Rectal Examination / Q000379:methods / Q000706:statistics & numerical data; D006801:Humans; D007610:Kallikreins / Q000097:blood*; D008297:Male; D008875:Middle Aged; D049451:Nomograms; D011237:Predictive Value of Tests; D017430:Prostate-Specific Antigen / Q000097:blood*; D011471:Prostatic Neoplasms / Q000097:blood / Q000175:diagnosis* / Q000453:epidemiology / Q000473:pathology; D015203:Reproducibility of Results; D012846:Singapore / Q000453:epidemiology
chemical_list: C549870:KLK3 protein, human; D007610:Kallikreins; D017430:Prostate-Specific Antigen
keywords: decision curve analysis; nomogram; prostate cancer