title: Novel artificial neural network for early detection of prostate cancer.
abstract: OBJECTIVE
  Two artificial neural networks (ANN) for the early detection of prostate cancer in men with total prostate-specific antigen (PSA) levels from 2.5 to 4 ng/mL and from 4 to 10 ng/mL were prospectively developed. The predictive accuracy of the ANN was compared with that obtained by use of conventional statistical analysis of standard PSA parameters.
  
  
  METHODS
  Consecutive men with a serum total PSA level between 4 and 10 ng/mL (n = 974) and between 2.5 and 4 ng/mL (n = 272) were analyzed. A separate ANN model was developed for each group of patients. Analyses were performed to determine the presence of prostate cancer.
  
  
  RESULTS
  The area under the receiver operator characteristic (ROC) curve (AUC) was 87.6% and 91.3% for the 2.5 to 4 ng/mL and 4 to 10 ng/mL ANN models, respectively. For the latter model, the AUC generated by the ANN was significantly higher than that produced by the single variables of total PSA, percentage of free PSA, PSA density of the transition zone (TZ), and TZ volume (P <.01), but not significantly higher compared with multivariate analysis. For the 2.5 to 4 ng/mL model, the AUC of the ANN ROC curve was significantly higher than the AUCs for percentage of free PSA (P =.0239), PSA-TZ (P =.0204), and PSA density and total prostate volume (P <.01 for both).
  
  
  CONCLUSIONS
  The predictive accuracy of the ANN was superior to that of conventional PSA parameters. ANN models might change the way patients referred for early prostate cancer detection are counseled regarding the need for prostate biopsy.
authors.affiliation: Department of Urology, University of Vienna, Austria. bdjavan@hotmail.com
authors.forename: Bob
authors.initials: B
authors.lastname: Djavan
authors.forename: Mesut
authors.initials: M
authors.lastname: Remzi
authors.forename: Alexandre
authors.initials: A
authors.lastname: Zlotta
authors.forename: Christian
authors.initials: C
authors.lastname: Seitz
authors.forename: Peter
authors.initials: P
authors.lastname: Snow
authors.forename: Michael
authors.initials: M
authors.lastname: Marberger
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D001706:Biopsy; D003657:Decision Making; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D011446:Prospective Studies; D017430:Prostate-Specific Antigen / Q000032:analysis*; D011471:Prostatic Neoplasms / Q000175:diagnosis* / Q000473:pathology; D012680:Sensitivity and Specificity
chemical_list: D017430:Prostate-Specific Antigen