title: Independent diagnostic and post-treatment prognostic models for prostate cancer demonstrate significant correlation with disease progression end points.
abstract: OBJECTIVE
  A major advance in the standard practice of tissue-based pathology is the new discipline of systems pathology (SP) that uses computational modeling to combine clinical, pathologic, and molecular measurements to predict biologic activity. Recently, a SP-based prostate cancer (PCa) predictive model for both preoperative (Px+) and postoperative (Px) prostatectomy has been developed. The purpose of this study is to calculate the percent agreement and the concordance between the Px+ and Px end points.
  
  
  METHODS
  Fifty-three patients underwent robot-assisted prostatectomy for PCa, and had Px+ and Px testing performed. Data were collected on Px+ end points and Px end points along with pathologic specimen results. The percent agreement and the degree of correlation between the Px+ and Px end points were then calculated.
  
  
  RESULTS
  The percent agreement (PA) between Px+ end points and Px end points ranged from 77% to 87%. The PA between a high Px+ favorable pathology (FP) classification and dominant Gleason score ≤ 3 and Gleason sum ≤ 6 was 71.7% and 37.4%, respectively. On univariate analysis, Px+ disease progression (DP) score significantly correlated with Px prostate-specific antigen recurrence (PSAR) score (P<0.001), while Px+ DP probability significantly correlated with PxPSAR probability (P<0.001). Px+ FP probability significantly correlated with postprostatectomy dominant Gleason grade ≤ 3 (P<0.001) and Gleason sum (P<0.001).
  
  
  CONCLUSIONS
  The PA between Px+ and Px testing end points for radical prostatectomy patients was very good. Furthermore, there was a direct correlation between most Px+ and Px end points. While the Px+FP classification and Gleason sum demonstrated a poor PA, Px+FP score still maintained a direct correlation to prostatectomy Gleason sum.
authors.affiliation: Department of Urology, University of California, Irvine, Irvine, California, USA. joseph.graversen@gmail.com
authors.forename: Joseph A
authors.initials: JA
authors.lastname: Graversen
authors.forename: Lara K
authors.initials: LK
authors.lastname: Suh
authors.forename: Adam C
authors.initials: AC
authors.lastname: Mues
authors.forename: Ruslan
authors.initials: R
authors.lastname: Korets
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Donovan
authors.forename: Faisal M
authors.initials: FM
authors.lastname: Khan
authors.forename: Qiuhua
authors.initials: Q
authors.lastname: Liu
authors.forename: Jaime
authors.initials: J
authors.lastname: Landman
authors.forename: Mantu
authors.initials: M
authors.lastname: Gupta
authors.forename: James M
authors.initials: JM
authors.lastname: McKiernan
authors.forename: Ketan K
authors.initials: KK
authors.lastname: Badani
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001706:Biopsy; D015331:Cohort Studies; D018450:Disease Progression; D023381:Endpoint Determination; D006801:Humans; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D011182:Postoperative Care; D011300:Preoperative Care; D011379:Prognosis; D011467:Prostate / Q000473:pathology; D011468:Prostatectomy; D011471:Prostatic Neoplasms / Q000175:diagnosis* / Q000473:pathology / Q000601:surgery / Q000628:therapy*