title: Use of the Prostate Health Index for the Detection of Aggressive Prostate Cancer at Radical Prostatectomy.
abstract: BACKGROUND
  In current study, we compared the accuracy of the PSA isoform p2PSA and its derivatives, the percentage of p2PSA to free PSA (%p2PSA) and the Prostate Health Index (PHI) in the detection of prostate cancer (PC) characteristics at the xFB01;nal pathology with respect to reference standards.
  
  
  METHODS
  This was an observational prospective study evaluating 43 consecutive PC patients treated with laparoscopic/robotic radical prostatectomy (RP). Logistic regression models were fitted to test the predictors of pT3 stage, pathologic Gleason score ≥ 8 or Gleason score upgrading, margin status, lymph node invasion, and the presence of high-risk disease (pT3 disease and/or Gleason score ≥ 8 and/or positive lymph node). The comparative base model included tPSA, clinical stage, biopsy Gleason score, and percentage of positive core.
  
  
  RESULTS
  Seventeen patients (39.5%) were affected by pT3 disease or had a pathologic Gleason score ≥ 8; positive margins were detected in 12 patients (27.9%), lymph node invasion was found in 2 patients (4.7%), and 15 patients (34.8%) harbored high-risk disease. In the univariate analysis, p2PSA, %p2PSA, and PHI were significant predictors of pT3 disease, pathologic Gleason score, and the presence of high-risk disease (all p < 0.05), whereas only PHI was an independent predictor of pT3 disease, margin status, and presence of high-risk disease, increasing the accuracy of a base multivariable model by 6.3% (p < 0.05) and 4.2% (p < 0.05) for the prediction of pT3 and high-risk disease, respectively.
  
  
  CONCLUSIONS
  p2PSA and its derivatives, primarily PHI, were significant predictors of unfavorable PC characteristics as detected at the xFB01;nal pathology, thus improving the clinical performance of standard prognostic factors for aggressive disease.
authors: Department of Urology, University of Perugia, Perugia, Italy.
authors: Luigi
authors: L
authors: Mearini
authors: Elisabetta
authors: E
authors: Nunzi
authors: Carla
authors: C
authors: Ferri
authors: Guido
authors: G
authors: Bellezza
authors: Carolina
authors: C
authors: Lolli
authors: Carlo
authors: C
authors: Porrozzi
authors: Massimo
authors: M
authors: Porena
mesh_terms: D000368:Aged; D001706:Biopsy; D005500:Follow-Up Studies; D006304:Health Status; D006801:Humans; D008297:Male; D008875:Middle Aged; D060787:Neoplasm Grading; D011237:Predictive Value of Tests; D011379:Prognosis; D011446:Prospective Studies; D017430:Prostate-Specific Antigen / Q000378:metabolism*; D011468:Prostatectomy / Q000379:methods*; D011471:Prostatic Neoplasms / Q000378:metabolism / Q000473:pathology* / Q000601:surgery; D012372:ROC Curve; D012720:Severity of Illness Index; D047368:Tumor Burden
chemical_list: D017430:Prostate-Specific Antigen