A comprehensive and novel predictive modeling technique using detailed pathology factors in men with localized prostate carcinoma.

Abstract

The purpose of the current study was to evaluate modeling strategies using sextant core prostate biopsy specimen data that would best predict biochemical control in patients with localized prostate carcinoma treated with permanent prostate brachytherapy (PPB). One thousand four hundred seventy-seven patients underwent PPB between 1992 and 2000. The authors restricted analysis to those patients who had sextant biopsies (n = 1073). A central pathology review was undertaken on all specimens. Treatment consisted of PPB with either I-125 or Pd-103 prescribed to 144 Gy or 140 Gy, respectively. Two hundred twenty-eight patients (21%) received PPB in combination with external radiotherapy and 333 patients (31%) received neoadjuvant hormones. In addition to clinical stage, biopsy Gleason sum, and pretreatment prostate specific antigen (pretx-PSA), the following detailed biopsy variables were considered: mean percentage of cancer in an involved core; maximum percentage of cancer; mean primary and secondary Gleason grades; maximum Gleason grade (primary or secondary); percentage of cancer in the apex, mid, and base; percent of cores positive; maximum primary and secondary Gleason grades in apex, mid, and base; maximum percent cancer in apex, mid, and base; maximum Gleason grade in apex, mid, and base; maximum primary Gleason grade; and maximum secondary Gleason grade. In all, 23 biopsy variables were considered. Four modeling strategies were compared. As a base model, the authors considered the pretx-PSA, clinical stage, and biopsy Gleason sum as predictors. For the second model, the authors added percent of cores positive. The third modeling strategy was to use stepwise variable selection to select only those variables (from the total pool of 26) that were statistically significant. The fourth strategy was to apply principal components analysis, which has theoretical advantages over the other strategies. Principal components analysis creates component scores that account for maximum variance in the predictors. The median followup of the study cohort was 36 months (range, 6-92), and the Kattan modification of the American Society for Therapeutic Radiology and Oncology (ASTRO) definition was used to define PSA freedom from recurrence (PSA-FFR). The four models were compared in their ability to predict PSA-FFR as measured by the Somers D rank correlation coefficient. The Somers D rank correlation coefficients were then corrected for optimism with use of bootstrapping. The results for the four models were 0.32, 0.34, 0.37, and 0.39, respectively. The current study shows that the use of principal components analysis with additional pathology data is a more discriminating model in predicting outcome in prostate carcinoma than other conventional methods and can also be used to model outcome predictions for patients treated with radical prostatectomy and external beam.