title: Outcome prediction for prostate cancer detection rate with artificial neural network (ANN) in daily routine.
abstract: BACKGROUND
  We evaluated the use of the artificial neural network (ANN) program "ProstataClass" of the Department of Urology and the Institute of Medical Informatics at the Charité-Universitätsmedizin Berlin in daily routine to increase prostate cancer (CaP) detection rate and to reduce unnecessary biopsies.
  
  
  METHODS
  From May 2005 to April 2007, a total of 204 patients were included in the study. The Beckman Access PSA assay was used, and pretreatment prostate specific antigen (PSA) was measured prior to digital rectal examination (DRE) and 12 core systematic transrectal ultrasound (TRUS) guided biopsies. The individual ANN predictions were generated with the use of the ANN application for the Beckman Access PSA and free PSA assays, which relies on age, PSA, percent free prostate specific antigen (%fPSA), prostate volume, and DRE. Diagnostic validity of total prostate specific antigen (tPSA), %fPSA, and the ANN was evaluated by ROC curve analysis.
  
  
  RESULTS
  PSA and %fPSA ranged from 4.01 to 9.91 ng/ml (median: 6.65) and 5% to 48% (median: 15%), respectively. Of all men, 46 (22.5%) demonstrated suspicious DRE findings. Total prostate volume ranged from 7.1 to 119.2 cc (median: 35). Overall, 71 (34.8%) CaP were detected. Of men with suspicious DRE, 28 (60.9%) had CaP on initial biopsy. The ANN was 78% accurate in the original report. The AUC of ROC curve analysis was 0.51 for PSA, 0.66 for %PSA, and 0.72 for the ANN-Output, respectively.
  
  
  CONCLUSIONS
  Our results in this independent cohort show that ANN is a very helpful parameter in daily routine to increase the CaP detection rate and reduce unnecessary biopsies.
authors.affiliation: Department of Urology, HELIOS Hospital, Bad Saarow, Germany. tho_ecke@hotmail.com
authors.forename: Thorsten H
authors.initials: TH
authors.lastname: Ecke
authors.forename: Peter
authors.initials: P
authors.lastname: Bartel
authors.forename: Steffen
authors.initials: S
authors.lastname: Hallmann
authors.forename: Stefan
authors.initials: S
authors.lastname: Koch
authors.forename: Jürgen
authors.initials: J
authors.lastname: Ruttloff
authors.forename: Henning
authors.initials: H
authors.lastname: Cammann
authors.forename: Michael
authors.initials: M
authors.lastname: Lein
authors.forename: Mark
authors.initials: M
authors.lastname: Schrader
authors.forename: Kurt
authors.initials: K
authors.lastname: Miller
authors.forename: Carsten
authors.initials: C
authors.lastname: Stephan
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D001604:Berlin; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D011379:Prognosis; D017430:Prostate-Specific Antigen / Q000097:blood*; D011471:Prostatic Neoplasms / Q000097:blood* / Q000175:diagnosis*; D012372:ROC Curve
chemical_list: D017430:Prostate-Specific Antigen