title: An algorithm for predicting nonorgan confined prostate cancer using the results obtained from sextant core biopsies with prostate specific antigen level.
abstract: OBJECTIVE
  We determined the enhanced ability to predict nonorgan confined prostate cancer using several histopathological and quantitative nuclear imaging parameters combined with serum prostate specific antigen (PSA).
  
  
  METHODS
  Several independent pathological and quantitative image analysis variables obtained from sextant biopsy specimens, as well as preoperative PSA were used. The study population included 210 patients with pathologically staged disease (192 with PSA). All variables were examined by univariate and multivariate logistic regression analyses to assess ability to predict disease organ confinement status.
  
  
  RESULTS
  Univariate logistic regression analysis demonstrated that, in decreasing order, quantitative nuclear grade, preoperative PSA, total percent tumor involvement, number of positive sextant cores, preoperative Gleason score and involvement of more than 5% of a base and/or apex biopsy were significant (p < or = 0.006) for prediction of disease organ confinement status. Backward stepwise logistic regression was applied to these univariately significant variables, including deoxyribonucleic acid ploidy, to calculate a multivariate model for prediction of disease organ confinement status. This algorithm had a sensitivity of 85.7%, specificity 71.3%, positive predictive value 72.9%, negative predictive value 84.7% and area under the receiver operating characteristic curve 85.9%.
  
  
  CONCLUSIONS
  Information from pathological study of sextant prostate biopsies, preoperative PSA blood test and a new image analysis variable termed quantitative nuclear grade can be combined to create a multivariate algorithm that can predict more accurately nonorgan confined prostate cancer compared to previously reported methods.
authors.affiliation: Crittenton Hospital, Rochester Hills, Michigan, USA.
authors.forename: R A
authors.initials: RA
authors.lastname: Badalament
authors.forename: M C
authors.initials: MC
authors.lastname: Miller
authors.forename: P A
authors.initials: PA
authors.lastname: Peller
authors.forename: D C
authors.initials: DC
authors.lastname: Young
authors.forename: D K
authors.initials: DK
authors.lastname: Bahn
authors.forename: P
authors.initials: P
authors.lastname: Kochie
authors.forename: G J
authors.initials: GJ
authors.lastname: O'Dowd
authors.forename: R W
authors.initials: RW
authors.lastname: Veltri
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001706:Biopsy / Q000379:methods; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D011184:Postoperative Period; D011237:Predictive Value of Tests; D011300:Preoperative Care; D017430:Prostate-Specific Antigen / Q000097:blood*; D011471:Prostatic Neoplasms / Q000097:blood* / Q000473:pathology*; D012680:Sensitivity and Specificity
chemical_list: D017430:Prostate-Specific Antigen