title: Estimation of prostate cancer probability by logistic regression: free and total prostate-specific antigen, digital rectal examination, and heredity are significant variables.
abstract: BACKGROUND
  Despite low specificity, serum prostate-specific antigen (PSA) is widely used in screening for prostate cancer. Specificity can be improved by measuring free and total PSA and by combining these results with clinical findings. Methods such as neural networks and logistic regression are alternatives to multistep algorithms for clinical use of the combined findings.
  
  
  METHODS
  We compared multilayer perceptron (MLP) and logistic regression (LR) analysis for predicting prostate cancer in a screening population of 974 men, ages 55-66 years. The study sample comprised men with PSA values >3 microg/L. Explanatory variables considered were age, free and total PSA and their ratio, digital rectal examination (DRE), transrectal ultrasonography, and a family history of prostate cancer.
  
  
  RESULTS
  When at least 90% sensitivity in the training sets was required, the mean sensitivity and specificity obtained were 87% and 41% with LR and 85% and 26% with MLP, respectively. The cancer specificity of an LR model comprising the proportion of free to total PSA, DRE, and heredity as explanatory variables was significantly better than that of total PSA and the proportion of free to total PSA (P <0.01, McNemar test). The proportion of free to total PSA, DRE, and heredity were used to prepare cancer probability curves.
  
  
  CONCLUSIONS
  The probability calculated by logistic regression provides better diagnostic accuracy for prostate cancer than the presently used multistep algorithms for estimation of the need to perform biopsy.
authors.affiliation: Central Laboratory, University Central Hospital of Turku, Medical Informatics Research Centre in Turku (MIRCIT), FIN-20521 Turku, Finland. arjen.virtanen@utu.fi
authors.forename: A
authors.initials: A
authors.lastname: Virtanen
authors.forename: M
authors.initials: M
authors.lastname: Gomari
authors.forename: R
authors.initials: R
authors.lastname: Kranse
authors.forename: U H
authors.initials: UH
authors.lastname: Stenman
mesh_terms: D001798:Blood Proteins / Q000378:metabolism; D015200:Fluoroimmunoassay; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D010173:Palpation; D011336:Probability; D017430:Prostate-Specific Antigen / Q000097:blood* / Q000378:metabolism; D011471:Prostatic Neoplasms / Q000097:blood* / Q000175:diagnosis / Q000235:genetics / Q000473:pathology; D011485:Protein Binding; D012007:Rectum; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D001798:Blood Proteins; D017430:Prostate-Specific Antigen