title: [Study on interpretation of quantitative results of prostate-specific antigen using information theory].
abstract: BACKGROUND
  The prostate-specific antigen (PSA) is considered the most useful among tumor markers currently used. However, its quantitative results are interpreted only qualitatively for the diagnosis of prostate cancer. The recently introduced information theory enables the information of the quantitative results transformed into Shannon's entropy (S) that represents uncertainties and then "1-S" representing diagnostic certainty.
  
  
  METHODS
  The 882 urological patients enrolled were categorized into 2 groups: a patient group comprising 233 patients with prostate cancer and a disease control group comprising 649 patients with benign prostate disease. The level of PSA in all the patients was tested and was found to be >or=2 ng/mL. The variables like PSA level and age were modeled on logistic regression analysis to predict the probability of prostate cancer and the diagnostic certainty.
  
  
  RESULTS
  The mean (SD) of PSA levels in the patient group and the disease control group were 44.5 ng/mL (37.62 ng/mL) and 5.7 ng/mL (3.70 ng/mL), respectively. The logistic regression model fitted well when the age variable was dichotomized at the age of 55 yr. The diagnostic certainty was lowest at a PSA level of 18.90 ng/mL in the <55-yr age group, and 15.45 ng/mL in the >55-yr age group.
  
  
  CONCLUSIONS
  The diagnostic certainty (1-S) of whether to diagnose prostate cancer or not at a certain PSA level could be obtained using the information theory. The methodology used in this study may help interpret the results of other quantitative tests.
authors.affiliation: Department of Laboratory Medicine, School of Medicine, Pusan National University Hospital, Busan, Korea.
authors.forename: Sang Hyun
authors.initials: SH
authors.lastname: Hwang
authors.forename: Heung Bum
authors.initials: HB
authors.lastname: Oh
authors.forename: Tina
authors.initials: T
authors.lastname: Pyo
authors.forename: Hyun Jin
authors.initials: HJ
authors.lastname: Lee
authors.forename: Kwan Jeh
authors.initials: KJ
authors.lastname: Lee
mesh_terms: D000367:Age Factors; D000368:Aged; D019277:Entropy; D006801:Humans; D007257:Information Theory; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D017430:Prostate-Specific Antigen / Q000097:blood*; D011469:Prostatic Diseases / Q000175:diagnosis; D011471:Prostatic Neoplasms / Q000175:diagnosis*
chemical_list: D017430:Prostate-Specific Antigen