title: Case diagnosis as positive identification in prostatic neoplasia.
abstract: OBJECTIVE
  To apply a distance measure and Bayesian belief network-based methodology to the positive identification of case diagnosis in prostatic neoplasia.
  
  
  METHODS
  Eight morphologic and cellular features were analyzed in 20 cases of normal prostate, 20 of low grade prostatic intraepithelial neoplasia (PIN), 20 of high grade PIN, 20 of prostatic adenocarcinoma with a cribriform pattern and 20 of prostatic adenocarcinoma with an acinar pattern. The diagnostic distance was evaluated to measure the "extent" to which the feature outcomes of the individual cases differed from the expected profile of outcomes in typical cases of normal prostate, low and high grade PIN, and cribriform and large acinar adenocarcinoma. Belief values were evaluated with a Bayesian belief network (BBN).
  
  
  RESULTS
  A bivariate representation of the cumulative absolute diagnostic distances of all the cases from the prototypes of normal prostate and cribriform adenocarcinoma was made. Three separate groups of cases were observed, corresponding to normal prostate, low grade PIN and cribriform adenocarcinoma. An additional group was formed by the cases of high grade PIN and acinar adenocarcinoma--i.e., there was complete overlap between the diagnostic distance values of cases belonging to these two categories. However, these cases showed differences in clue outcomes. To explore the contribution of such observations to case identification, a bivariate representation of the diagnostic distances from high grade PIN and acinar adenocarcinoma was made. The cases then formed five separate groups corresponding to the five diagnostic categories. When the individual cases were considered, their shortest distance was from the prototype of the category into which they were originally diagnosed. The BBN gave these diagnostic categories the highest belief values.
  
  
  CONCLUSIONS
  The combined evaluation of diagnostic distance and belief represents an identification procedure. The numeric value of certainty characterizes individual cases according to the level of progression from PIN toward cancer.
authors.affiliation: Institute of Pathological Anatomy and Histopathology, University of Ancona, Italy. r.montironi@popcsi.unian.it
authors.forename: R
authors.initials: R
authors.lastname: Montironi
authors.forename: R
authors.initials: R
authors.lastname: Mazzucchelli
authors.forename: A
authors.initials: A
authors.lastname: Santinelli
authors.forename: P W
authors.initials: PW
authors.lastname: Hamilton
authors.forename: D
authors.initials: D
authors.lastname: Thompson
authors.forename: P H
authors.initials: PH
authors.lastname: Bartels
mesh_terms: D001499:Bayes Theorem; D002450:Cell Communication; D002467:Cell Nucleus / Q000473:pathology; D003661:Decision Support Techniques; D003937:Diagnosis, Differential; D006801:Humans; D008297:Male; D019048:Prostatic Intraepithelial Neoplasia / Q000145:classification / Q000473:pathology*; D011471:Prostatic Neoplasms / Q000145:classification / Q000175:diagnosis* / Q000473:pathology*