Independent diagnostic and post-treatment prognostic models for prostate cancer demonstrate significant correlation with disease progression end points - Abstract

Department of Urology, University of California , Irvine, Irvine, California.


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.

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.

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).

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.

Written by:
Graversen JA, Suh LK, Mues AC, Korets R, Donovan MJ, Khan FM, Liu Q, Landman J, Gupta M, McKiernan JM, Badani KK.   Are you the author?

Reference: J Endourol. 2011 Sep 23. Epub ahead of print.
doi: 10.1089/end.2011.0192

PubMed Abstract
PMID: 21942796 Prostate Cancer Section