title: Predicting the outcome of prostate biopsy in screen-positive men by a multilayer perceptron network.
abstract: OBJECTIVE
  To assess whether an artificial neural network (multilayer perceptron, MLP) and logistic regression (LR) could eliminate more false-positive prostate-specific antigen (PSA) results than the proportion of free PSA in a prostate cancer screening.
  
  
  METHODS
  MLP and LR models were constructed on the basis of data on total PSA, the proportion of free PSA, digital rectal examination (DRE), and prostate volume from 656 consecutive men (aged 55 to 67 years) with total serum PSA concentrations of 4 to 10 ng/mL in the randomized population-based prostate cancer screening study in Finland. The MLP and LR models were validated using the "leave-one-out" method.
  
  
  RESULTS
  Of the 656 men, 23% had prostate cancer and 77% had either normal prostatic histology or a benign disease. At a 95% sensitivity level, 19% of the false-positive PSA results could be eliminated by using the proportion of free PSA versus 24% with the LR model and 33% with the MLP model (P < 0.001). At 80% to 99% sensitivity levels, the accuracy of the MLP and LR models was significantly higher than that of the proportion of free PSA. At 89% to 99% sensitivities, the accuracy of the MLP was higher than that of LR (P </= 0.001).
  
  
  CONCLUSIONS
  At clinically relevant sensitivity levels, the MLP and LR models based on total PSA, the proportion of free PSA, DRE, and prostate volume could reduce the number of unnecessary biopsies significantly better than the proportion of free PSA alone in men with total PSA levels in the range 4 to 10 ng/mL.
authors.affiliation: Department of Clinical Chemistry, Helsinki University Central Hospital, Helsinki, Finland.
authors.forename: P
authors.initials: P
authors.lastname: Finne
authors.forename: R
authors.initials: R
authors.lastname: Finne
authors.forename: A
authors.initials: A
authors.lastname: Auvinen
authors.forename: H
authors.initials: H
authors.lastname: Juusela
authors.forename: J
authors.initials: J
authors.lastname: Aro
authors.forename: L
authors.initials: L
authors.lastname: Määttänen
authors.forename: M
authors.initials: M
authors.lastname: Hakama
authors.forename: S
authors.initials: S
authors.lastname: Rannikko
authors.forename: T L
authors.initials: TL
authors.lastname: Tammela
authors.forename: U
authors.initials: U
authors.lastname: Stenman
mesh_terms: D000368:Aged; D001706:Biopsy; D003937:Diagnosis, Differential; D005189:False Positive Reactions; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D016017:Odds Ratio; D010173:Palpation; D011336:Probability; D011467:Prostate / Q000473:pathology; D017430:Prostate-Specific Antigen / Q000097:blood*; D011469:Prostatic Diseases / Q000097:blood / Q000175:diagnosis; D011471:Prostatic Neoplasms / Q000097:blood / Q000175:diagnosis* / Q000473:pathology; D012007:Rectum; D012680:Sensitivity and Specificity
chemical_list: D017430:Prostate-Specific Antigen