title: Clinical utility of human glandular kallikrein 2 within a neural network for prostate cancer detection.
abstract: OBJECTIVE
  To assess, using artificial neural networks (ANNs), human glandular kallikrein 2 (hK2), prostate-specific antigen (PSA), and percentage free/total PSA (f/tPSA), for discriminating between prostate cancer and benign prostatic hyperplasia (BPH).
  
  
  METHODS
  Serum samples from 475 patients with prostate cancer (n = 347) or BPH (n = 128) within the PSA range of 1-20 ng/mL were analysed for tPSA, fPSA and hK2 (research assay, Toronto, Canada). Data were analysed in the ranges of 1-4, 2-4, 4-10, and 2-20 ng/mL tPSA. Back-propagation ANN models with the variables PSA, f/tPSA, and hK2, hK2/fPSA and hK2/(f/tPSA) were constructed. The diagnostic validity was evaluated by receiver-operating characteristic (ROC) curve analysis.
  
  
  RESULTS
  Whereas the median concentration of hK2 was not significantly different between patients with BPH or prostate cancer in any of the tPSA ranges, the f/tPSA, hK2/fPSA and hK2/(f/tPSA), and the hK2-based ANN outputs were always significantly different between patients with prostate cancer or BPH. Using ROC curve comparison, all variables were significantly better than hK2 in all ranges. The hK2-based ANN performed better than f/tPSA except in the 4-10 ng/mL tPSA range. At 90% and 95% sensitivity, the hK2-based ANN was also significantly better than f/tPSA in the 1-4 ng/mL tPSA range. hK2/(f/tPSA) achieved equal results to the hK2-based ANN except in the range 2-20 ng/mL tPSA.
  
  
  CONCLUSIONS
  The hK2-based ANN improves the outcome of f/tPSA but not hK2/(f/tPSA) in almost all analysed subgroups. When comparing the results at 90% and 95% sensitivity the hK2-based ANN only performed significantly better than f/tPSA in the lowest tPSA range. Only in lower tPSA ranges do hK2-based ANNs show an advantage for further improving prostate cancer detection.
authors.affiliation: Department of Urology, Univeristy Hospital Charité, Berlin, Germany. carsten.stephan@charite.de
authors.forename: Carsten
authors.initials: C
authors.lastname: Stephan
authors.forename: Klaus
authors.initials: K
authors.lastname: Jung
authors.forename: Antoninus
authors.initials: A
authors.lastname: Soosaipillai
authors.forename: George M
authors.initials: GM
authors.lastname: Yousef
authors.forename: Henning
authors.initials: H
authors.lastname: Cammann
authors.forename: Hellmuth
authors.initials: H
authors.lastname: Meyer
authors.forename: Chuanliang
authors.initials: C
authors.lastname: Xu
authors.forename: Eleftherios P
authors.initials: EP
authors.lastname: Diamandis
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D003936:Diagnosis, Computer-Assisted; D003937:Diagnosis, Differential; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D017430:Prostate-Specific Antigen / Q000097:blood; D011470:Prostatic Hyperplasia / Q000097:blood; D011471:Prostatic Neoplasms / Q000097:blood*; D012372:ROC Curve; D012680:Sensitivity and Specificity; D018709:Statistics, Nonparametric; D020840:Tissue Kallikreins / Q000097:blood*
chemical_list: D020840:Tissue Kallikreins; D017430:Prostate-Specific Antigen