title: Three new serum markers for prostate cancer detection within a percent free PSA-based artificial neural network.
abstract: BACKGROUND
  We aimed to evaluate the value of macrophage inhibitory cytokine 1 (MIC-1), human kallikrein 11 (hK11) migration inhibitor factor (MIF) in comparison to prostate-specific antigen (PSA) and % fPSA and also to develop a % fPSA-based ANN with the new input factors to determine whether these additional markers can further eliminate unnecessary prostate biopsies.
  
  
  METHODS
  Serum samples from 371 patients with prostate cancer (PCa, n=135) or benign prostate hyperplasia (BPH, n=236) within the PSA range 0.5-20 microg/L were analyzed for total PSA, free PSA, MIC-1, hK11, and MIF. 'Leave one out' ANN models with these variables and prostate volume were constructed and compared to logistic regression (LR) and all single parameters.
  
  
  RESULTS
  The discriminatory power of MIC-1, hK11, and MIF was less than that for PSA despite significant differences in BPH compared to PCa patients. At 90% and 95% sensitivity, the artificial neural networks (ANNs) were only significantly better than % fPSA if prostate volume was included.
  
  
  CONCLUSIONS
  ANNs with the novel input factors of MIC-1, MIF, and/or hK11 and additional use of prostate volume demonstrated significant advantage compared with % fPSA and tPSA and may lead to a reduction in unnecessary prostate biopsies.
authors.affiliation: Department of Urology, University Hospital Charité Berlin, Berlin, Germany. carsten.stephan@charite.de
authors.forename: Carsten
authors.initials: C
authors.lastname: Stephan
authors.forename: Chuanliang
authors.initials: C
authors.lastname: Xu
authors.forename: David A
authors.initials: DA
authors.lastname: Brown
authors.forename: Samuel N
authors.initials: SN
authors.lastname: Breit
authors.forename: Anja
authors.initials: A
authors.lastname: Michael
authors.forename: Terukazu
authors.initials: T
authors.lastname: Nakamura
authors.forename: Eleftherios P
authors.initials: EP
authors.lastname: Diamandis
authors.forename: Hellmuth
authors.initials: H
authors.lastname: Meyer
authors.forename: Henning
authors.initials: H
authors.lastname: Cammann
authors.forename: Klaus
authors.initials: K
authors.lastname: Jung
mesh_terms: D014408:Biomarkers, Tumor / Q000097:blood*; D019485:Bone Morphogenetic Proteins / Q000097:blood*; D003937:Diagnosis, Differential; D055436:Growth Differentiation Factor 15; D006801:Humans; D008263:Macrophage Migration-Inhibitory Factors / Q000097:blood*; D008297:Male; D016571:Neural Networks, Computer; D017430:Prostate-Specific Antigen / Q000097:blood*; D011470:Prostatic Hyperplasia / Q000097:blood* / Q000175:diagnosis; D011471:Prostatic Neoplasms / Q000097:blood / Q000175:diagnosis*; D012044:Regression Analysis; D012697:Serine Endopeptidases / Q000097:blood*
chemical_list: D014408:Biomarkers, Tumor; D019485:Bone Morphogenetic Proteins; C526140:GDF15 protein, human; D055436:Growth Differentiation Factor 15; D008263:Macrophage Migration-Inhibitory Factors; C114880:trypsin-like serine protease; D012697:Serine Endopeptidases; D017430:Prostate-Specific Antigen