title: Multicenter evaluation of an artificial neural network to increase the prostate cancer detection rate and reduce unnecessary biopsies.
abstract: BACKGROUND
  The percentage of free prostate-specific antigen (%fPSA) has been shown to improve specificity for the diagnosis of prostate cancer (PCa) over total PSA (tPSA). A multicenter study was performed to evaluate the diagnostic value of a %fPSA-based artificial neural network (ANN) in men with tPSA concentrations between 2 and 20 microg/L for detecting patients with increased risk of a positive prostate biopsy for cancer.
  
  
  METHODS
  We enrolled 1188 men from six different hospitals with PCa or benign prostates between 1996 and 2001. We used a newly developed ANN with input data of tPSA, %fPSA, patient age, prostate volume, and digital rectal examination (DRE) status to calculate the risk for the presence of PCa within different tPSA ranges (2-4, 4.1-10, 2-10, 10.1-20, and 2-20 microg/L) at the 90% and 95% specificity or sensitivity cutoffs, depending on the tPSA concentration. ROC analysis and cutoff calculations were used to estimate the diagnostic improvement of the ANN compared with %fPSA alone.
  
  
  RESULTS
  In the low tPSA range (2-4 microg/L), the ANN detected 72% and 65% of cancers at specificities of 90% or 95%, respectively. At 4-10 microg/L tPSA, the ANN detected 90% and 95% of cancers with specificities of 62% and 41%, respectively. Use of the ANN with 2-10 microg/L tPSA enhanced the specificity of %fPSA by 20-22%, thus reducing the number of unnecessary biopsies.
  
  
  CONCLUSIONS
  Enhanced accuracy of PCa detection over that obtained using %fPSA alone can be achieved with a %fPSA-based ANN that also includes clinical information from DRE and prostate volume measurements.
authors.affiliation: Department of Urology, University Hospital Charité, Humboldt University, D-10098 Berlin, Germany. carsten.stephan@charite.de
authors.forename: Carsten
authors.initials: C
authors.lastname: Stephan
authors.forename: Henning
authors.initials: H
authors.lastname: Cammann
authors.forename: Axel
authors.initials: A
authors.lastname: Semjonow
authors.forename: Eleftherios P
authors.initials: EP
authors.lastname: Diamandis
authors.forename: Leon F A
authors.initials: LF
authors.lastname: Wymenga
authors.forename: Michael
authors.initials: M
authors.lastname: Lein
authors.forename: Pranav
authors.initials: P
authors.lastname: Sinha
authors.forename: Stefan A
authors.initials: SA
authors.lastname: Loening
authors.forename: Klaus
authors.initials: K
authors.lastname: Jung
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001706:Biopsy / Q000592:standards; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D017430:Prostate-Specific Antigen / Q000097:blood* / Q000378:metabolism; D011471:Prostatic Neoplasms / Q000175:diagnosis*; D012372:ROC Curve; D012680:Sensitivity and Specificity
chemical_list: D017430:Prostate-Specific Antigen