title: An artificial neural network to predict the outcome of repeat prostate biopsies.
abstract: OBJECTIVE
  To develop an advanced artificial neural network (ANN) to predict the presence of prostate cancer (PCa) and to predict the outcome of repeat prostate biopsies. The predictive accuracy was compared with the accuracy obtained using standard cutoffs for the free/total (f/t) prostate-specific antigen (PSA) ratio, PSA density (PSAD), PSA density of the transition zone (PSA-TZ), and the total and transition zone volumes. Clinical and biochemical diagnostic tests have been shown to improve PCa detection. When these tests are combined using an ANN, significant increases in specificity at high sensitivity are observed.
  
  
  METHODS
  The Vienna-based multicenter European referral database for early PCa detection of 820 men with a PSA level between 4 and 10 ng/mL was used. The presence of PCa was determined using transrectal ultrasound-guided octant needle repeat biopsy. Variables in the database consisted of age, PSA, f/t PSA ratio, digital rectal examination findings, PSA velocity, and the transrectal ultrasound-guided variables of prostate volume, transition zone volume, PSAD, and PSA-TZ. The ANN used in the analysis was an advanced multilayer perceptron selected for accuracy by a genetic algorithm.
  
  
  RESULTS
  The repeat biopsy PCa detection rate was 10% (n = 83). At 95% sensitivity, the specificity for ANN was 68% compared with 54%, 33.5%, 21.4%, 14.7%, and 8.3% for multivariate logistic regression analysis, f/t PSA ratio, PSA-TZ, PSAD, and total PSA, respectively. The ANN reduced unnecessary repeat biopsies by 68% in this study. The area under the curve was 83% for the ANN versus 79%, 74.5%, 69.1%, 61.8%, and 60.5% for multivariate analysis, f/t PSA ratio, PSA-TZ, PSAD, and total PSA, respectively.
  
  
  CONCLUSIONS
  The current ANN found a strong pattern predictive of PCa in patients with a negative initial biopsy. By combining the individual clinical and biochemical markers into the ANN, 68% specificity at 95% sensitivity was achieved. The ANN allows more accurate and individual counseling of patients with a negative initial biopsy.
authors.affiliation: Department of Urology, University of Vienna, Vienna, Austria.
authors.forename: Mesut
authors.initials: M
authors.lastname: Remzi
authors.forename: Theodore
authors.initials: T
authors.lastname: Anagnostou
authors.forename: Vincent
authors.initials: V
authors.lastname: Ravery
authors.forename: Alexandre
authors.initials: A
authors.lastname: Zlotta
authors.forename: Carsten
authors.initials: C
authors.lastname: Stephan
authors.forename: Michael
authors.initials: M
authors.lastname: Marberger
authors.forename: Bob
authors.initials: B
authors.lastname: Djavan
mesh_terms: D000368:Aged; D001706:Biopsy; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D011467:Prostate / Q000000981:diagnostic imaging / Q000473:pathology*; D017430:Prostate-Specific Antigen / Q000032:analysis; D011471:Prostatic Neoplasms / Q000175:diagnosis*; D012372:ROC Curve; D012680:Sensitivity and Specificity; D014463:Ultrasonography
chemical_list: D017430:Prostate-Specific Antigen