Nuclear morphology in preneoplastic lesions of rat liver

The nuclear morphology of preneoplastic and unaltered hepatocytes in diethylnitrosamine-treated rats was investigated. Two-micrometer-thick sections of methacrylate-embedded liver were scanned with a TV camera and evaluated in a computer using multivariate analysis methods. The preneoplastic cell po...

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Bibliographic Details
Published inAnalytical and quantitative cytology Vol. 5; no. 4; p. 275
Main Authors Abmayr, W, Deml, E, Oesterle, D, Gössner, W
Format Journal Article
LanguageEnglish
Published United States 01.12.1983
Subjects
Animals
Cell Nucleus - pathology
Cytological Techniques
Diethylnitrosamine - pharmacology
Female
Histocytochemistry
Liver Glycogen - analysis
Liver Neoplasms - chemically induced
Liver Neoplasms - pathology
Minicomputers
Precancerous Conditions - chemically induced
Precancerous Conditions - pathology
Rats
Rats, Inbred Strains
Staining and Labeling - methods
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Summary:The nuclear morphology of preneoplastic and unaltered hepatocytes in diethylnitrosamine-treated rats was investigated. Two-micrometer-thick sections of methacrylate-embedded liver were scanned with a TV camera and evaluated in a computer using multivariate analysis methods. The preneoplastic cell populations (islands) were distinguished from unaltered hepatocytes by histochemical demonstration of glycogen storage in specimens from starved animals. After the hemalaun-stained liver sections were scanned randomly, the sections were stained for glycogen, and the previously registered cells were identified visually using a scanning stage for relocation. This objective identification of unaltered and preneoplastic hepatocytes formed the basis for the selection of a training set for feature evaluation and supervised classification. Image analysis for quantitative nuclear morphology was applied to the hemalaun-stained cells. The results showed that condensed chromatin was reduced and nuclear area was increased in the nuclei of glycogen-storing cells. Differences in the nuclear structure were also found. A multivariate analysis including seven features gave a correct classification result of about 82%.
ISSN:0190-0471