title: Initial biopsy outcome prediction--head-to-head comparison of a logistic regression-based nomogram versus artificial neural network.
abstract: OBJECTIVE
  Nomograms and artificial neural networks (ANNs) represent alternative methodologic approaches to predict the probability of prostate cancer on initial biopsy. We hypothesized that, in a head-to-head comparison, one of the approaches might demonstrate better accuracy and performance characteristics than the other.
  
  
  METHODS
  A previously published nomogram, which relies on age, digital rectal examination, serum prostate-specific antigen (PSA), and percent-free PSA, and an ANN, which relies on the same predictors plus prostate volume, were applied to a cohort of 3980 men, who were subjected to multicore systematic prostate biopsy. The accuracy and the performance characteristics were compared between these two approaches.
  
  
  RESULTS
  The accuracy of the nomogram was 71% versus 67% for the ANN (p=0.0001). Graphical exploration of the performance characteristics demonstrated virtually perfect predictions for the nomogram. Conversely, the ANN underestimated the observed rate of prostate cancer.
  
  
  CONCLUSIONS
  A 4% increase in predictive accuracy implies that the use of the nomogram instead of the ANN will result in 40 additional patients who will be correctly classified between benign and cancer.
authors.affiliation: Cancer Prognostics and Health Outcomes Unit, University of Montreal, Montreal, Quebec, Canada.
authors.forename: Felix K-H
authors.initials: FK
authors.lastname: Chun
authors.forename: Markus
authors.initials: M
authors.lastname: Graefen
authors.forename: Alberto
authors.initials: A
authors.lastname: Briganti
authors.forename: Andrea
authors.initials: A
authors.lastname: Gallina
authors.forename: Julia
authors.initials: J
authors.lastname: Hopp
authors.forename: Michael W
authors.initials: MW
authors.lastname: Kattan
authors.forename: Hartwig
authors.initials: H
authors.lastname: Huland
authors.forename: Pierre I
authors.initials: PI
authors.lastname: Karakiewicz
mesh_terms: D000328:Adult; D000368:Aged; D001707:Biopsy, Needle; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D016571:Neural Networks, Computer; D049451:Nomograms; D011237:Predictive Value of Tests; D011467:Prostate / Q000473:pathology*; D011471:Prostatic Neoplasms / Q000175:diagnosis*; D012680:Sensitivity and Specificity