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Int J Clin Exp Pathol 2012;5(6):496-502
Predictors of Gleason score (GS) upgrading on subsequent prostatectomy: a single
institution study in a cohort of patients with GS 6
Vikas Mehta, Kevin Rycyna, Bart M M Baesens, Güliz A Barkan, Gladell P Paner*, Robert C Flanigan, Eva M Wojcik, Girish Venkataraman
Departments of Pathology, Stritch School of Medicine and Urology, Loyola University Medical Center, Maywood, IL; Department of Decision
Sciences and Information Management, Catholic University Naamsestraat 69 3000 Leuven, Belgium. *Current affiliation: Department of
Pathology, University of Chicago Hospital, Hyde Park, IL
Received May 13, 2012; Accepted June 3, 2012; Epub July 29, 2012; Published August 15, 2012
Abstract: Background: Biopsy Gleason score (bGS) remains an important prognostic indicator for adverse outcomes in Prostate Cancer
(PCA). In the light of recent studies purporting difference in prognostic outcomes for the subgroups of GS7 group (primary Gleason pattern 4 vs.
3), upgrading of a bGS of 6 to a GS≥7 has serious implications. We sought to identify pre-operative factors associated with upgrading in a
cohort of GS6 patients who underwent prostatectomy. Design: We identified 281 cases of GS6 PCA on biopsy with subsequent
prostatectomies. Using data on pre-operative variables (age, PSA, biopsy pathology parameters), logistic regression models (LRM) were
developed to identify factors that could be used to predict upgrading to GS≥7 on subsequent prostatectomy. A decision tree (DT) was
constructed. Results: 92 of 281 cases (32.7%) were upgraded on subsequent prostatectomy. LRM identified a model with two variables with
statistically significant ability to predict upgrading, including pre-biopsy PSA (Odds Ratio 8.66; 2.03-37.49, 95% CI) and highest percentage of
cancer at any single biopsy site (Odds Ratio 1.03, 1.01-1.05, 95% CI). This two-parameter model yielded an area under curve of 0.67. The
decision tree was constructed using only 3 leave nodes; with a test set classification accuracy of 70%. Conclusions: A simplistic model using
clinical and biopsy data is able to predict the likelihood of upgrading of GS with an acceptable level of certainty. External validation of these
findings along with development of a nomogram will aid in better stratifying the cohort of low risk patients as based on the GS. (IJCEP1205005).
Keywords: Carcinoma, Prostate/ pathology/predictive modeling, Statistical techniques/logistic regression, binary recursive partitioning
Address all correspondence to:
Dr. Girish Venkataraman
Department of Pathology
Loyola University Medical Center
Building 110, Room 2222
2160 South First Avenue
Maywood, IL 60153, USA.
Tel: (708) 216-2053; Fax: (708) 327-2620