Discrimination between dysplastic and malignant epithelium of the ampulla of vater based on quantitative image cytometric data

Citation
Ab. Hittelet et al., Discrimination between dysplastic and malignant epithelium of the ampulla of vater based on quantitative image cytometric data, ANAL QUAN C, 22(2), 2000, pp. 98-106
Citations number
23
Categorie Soggetti
Medical Research Diagnosis & Treatment
Journal title
ANALYTICAL AND QUANTITATIVE CYTOLOGY AND HISTOLOGY
ISSN journal
08846812 → ACNP
Volume
22
Issue
2
Year of publication
2000
Pages
98 - 106
Database
ISI
SICI code
0884-6812(200004)22:2<98:DBDAME>2.0.ZU;2-0
Abstract
OBJECTIVE: To assess the ability to associate histopathologic grading with objective criteria obtained by nuclear image cytometry in epithelium of the ampulla of Vater. STUDY DESIGN: Forty-one resected ampullary specimens were studied, includin g 8 dysplastic ampullomas together with 22 well-differentiated and 11 poorl y differentiated ampullary adenocarcinomas. The nuclei were Feulgen stained and analyzed using a computer-assisted microscope, which generated 38 quan titative variables describing chromatin texture and nuclear DNA content (DN A ploidy level). These variables were explored by discriminant analysis to determine the most stable and informative variables. Univariate analysis wa s performed on the four most informative ones. The whole set of variables w as also subjected to principal component analysis in order to characterize intragroup and intergroup heterogeneity. RESULTS: The univariate analysis defined two morphonuclear variables (relat ed to nuclear chromatin distribution) discriminating between dysplasia and well-differentiated cancers. Aneuploidy occurrence was associated with disc rimination between well-differentiated and poorly differentiated cancers. CONCLUSION: While alterations in chromatin distribution may be an early eve nt in the malignant degeneration of this epithelium, alterations in nuclear DNA content should correspond to a later phenomenon. Quantification of the se features can be exploited to assist in diagnosis.