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The aim of the present study was to investigate whether biological features determined through image cytometry are able to characterize clinical subpopulations of lipomas. Forty lipomas excised from 36 patients were studied. On the one hand, the tumors were clinically characterized by means of patient-related and pre- and post-operative features. On the other, the tumors were analyzed by means of the computer-assisted microscopy analysis of Feulgen-stained nuclei. This analysis generated 3 groups of biological quantitative variables describing morphonuclear aspects (i.e. the chromatin pattern of the cell nuclei), the nuclear DNA content (DNA ploidy level), and architectural features (such as the cell density and the topographical cell nuclei organization). Possible relations between the clinical and the biological features of the lipomas were investigated by means of univariate and multivariate statistical analyses. The results show the existence of such relations, in particular between the morphonuclear and architectural features of lipomas, on the one hand, and their consistency, volume and weight, on the other. Furthermore, multivariate analysis made it possible to distinguish two subpopulations of lipomas exhibiting different biological characteristics in terms of morphonuclear patterns.

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, including 8 dysplastic ampullomas together with 22 well-differentiated and 11 poorly differentiated ampullary adenocarcinomas. The nuclei were Feulgen stained and analyzed using a computer-assisted microscope, which generated 38 quantitative variables describing chromatin texture and nuclear DNA content (DNA ploidy level). These variables were explored by discriminant analysis to determine the most stable and informative variables. Univariate analysis was performed on the four most informative ones. The whole set of variables was also subjected to principal component analysis in order to characterize intragroup and intergroup heterogeneity., Results: The univariate analysis defined two morphonuclear variables (related to nuclear chromatin distribution) discriminating between dysplasia and well-differentiated cancers. Aneuploidy occurrence was associated with discrimination between well-differentiated and poorly differentiated cancers., Conclusion: While alterations in chromatin distribution may be an early event in the malignant degeneration of this epithelium, alterations in nuclear DNA content should correspond to a later phenomenon. Quantification of these features can be exploited to assist in diagnosis.

Well-differentiated lipomatous tumors constitute a histopathologic category whose nomenclature has been controversial, particularly with respect to the distinction between atypical lipomas of the extremities and well-differentiated liposarcomas of the retroperitoneum. To determine whether there were differences in image analytic parameters between these neoplasms, 72 lesions including 21 typical lipomas, 7 atypical lipomas, 16 retroperitoneal and 5 nonretroperitoneal well-differentiated, 9 dedifferentiated, and 14 pleomorphic liposarcomas were submitted to the computer-assisted microscopic analysis of Feulgen-stained nuclei. This methodology enabled four groups of variables to be calculated. These included: (1) quantitative chromatin pattern description (14 variables); (2) the measurement of proliferative activity (1 variable); (3) nuclear DNA content (DNA ploidy level, 5 variables); and (4) the measurement of cell density and topographical cell nuclei organization (2 variables). The results strongly suggest that atypical lipomas, whether superficial or deep, and well-differentiated liposarcomas, whether retroperitoneal or not, belong to the same category in terms of the variables analyzed.

Thirty-five lipomatous tumors were quantitatively described using 47 variables generated by means of computer-assisted microscope analysis. Of these 47 quantitative variables, 27 were computed on Feulgen-stained specimens (25 on cytologic and 2 on histologic samples) and, of the remaining 20, 8 related to vimentin and S-100 protein immunostaining patterns and the other 12 to the glycohistochemical staining patterns of peanut agglutinin, succinylated wheat germ agglutinin, and concavalin A agglutinin. The 35 lipomatous tumors included 6 atypical lipomas and 8 well differentiated, 5 dedifferentiated, 6 myxoid, and 10 pleomorphic liposarcomas. The actual diagnostic value contributed by each of the 47 variables with respect to the 5 lipomatous tumor groups was determined by means of the decision tree technique, an artificial intelligence-related algorithm that forms part of the supervised learning algorithms. Of the 47 quantitative variables, the decision tree technique retained 8: i.e., 2 tissue architecture-, 2 DNA ploidy level-, 2 morphonuclear-, 1 lectin histochemical-, and 1 vimentin immunostain-related variables. The decision tree technique made use of these 8 variables to set up logical rules that make it possible to identify atypical lipomas from well differentiated liposarcomas, on the one hand, and dedifferentiated liposarcomas from those that are well differentiated and pleomorphic, on the other. Thus, the combination of an artificial intelligence algorithm analyzing quantitative variables generated by means of the computer-assisted microscope analysis of cytologic and histologic samples from lipomatous tumors can be considered an expert system contributing significant diagnostic information to conventional diagnosis.