Validation of World Health Organization/International Society of Urologic Pathology 2004 classification schema for bladder urothelial carcinomas using quantitative nuclear morphometry: identification of predictive features using bootstrap method.
OBJECTIVE
Despite the introduction of the new World Health Organization 2004 grading classification, the grading of urothelial carcinoma remains difficult and subjective. The aim of this study was to evaluate the role of computer-assisted image morphometric analysis as a tool to improve the objectivity of histologic grading of urothelial carcinoma.


METHODS
A total of 75 urinary bladder biopsies from a cohort of patients with a first-time diagnosis of urothelial carcinoma representing low-grade (n = 19) and high-grade (n = 56) urothelial carcinoma were evaluated. Quantitative nuclear morphometry was performed on these biopsies using approximately 80 to 100 cells per case. A total of 17 nuclear morphometry features were extracted, and a bootstrap-based predictor selection using stepwise logistic regression analysis was performed. Subsequently, a validation was performed using the five top features from the logistic regression analyses by implementing a nonparametric discriminant analysis to identify the most discriminative features that predicted for high-grade cases.


RESULTS
The bootstrap technique included nuclear pleomorphism as the most frequently selected predictor of high-grade urothelial carcinoma (in 213 of 500 replicates). Validation using the top five features in the logistic regression analysis method (pleomorphism, configuration run length, DNA mass, feret-Y, and age) using discriminant analysis gave a resubstitution error of 4%, indicating the usefulness of the selected predictors.


CONCLUSIONS
The present study is the first to provide a morphometric validation of the World Health Organization 2004 system for pathologic grading of bladder cancer. Furthermore, quantitative nuclear morphometry could aid in the objective grading of urinary bladder biopsies. This information might aid the treating physicians in better risk stratification of patients with urothelial carcinoma.
Kapur
Umesh
U
Department of Pathology, Loyola University Medical Center, Maywood, Illinois 60153, USA.
Antic
Tatjana
T
Venkataraman
Girish
G
Durazo-Arvizu
Ramon
R
Quek
Marcus M
MM
Flanigan
Robert C
RC
Wojcik
Eva M
EM
D000368:Aged; D002295:Carcinoma, Transitional Cell / Q000145:classification* / Q000473:pathology*; D002467:Cell Nucleus / Q000473:pathology*; D005260:Female; D006801:Humans; D008297:Male; D010336:Pathology; D011237:Predictive Value of Tests; D012955:Societies, Medical; D001749:Urinary Bladder Neoplasms / Q000145:classification* / Q000473:pathology*; D014572:Urology; D014944:World Health Organization