Affiliations: Department of Clinical and Experimental Pathomorphology, Collegium Medicum, Jagiellonian University, Kraków, Poland
Note: [] Corresponding author: Krzysztof Okoń, ul. Grzegórzecka 16, Kraków 31-531, Poland. Tel.: +48 12 6199612; Fax: +48 12 4119725; E-mail: [email protected]
Abstract: Background: Marginal zone lymphomas are indolent B-cell lymphomas associated with autoimmunity and chronic inflammation. The two most frequent variants are mucosa associated lymphoid tissues marginal zone lymphomas and splenic marginal zone lymphomas. The aim of the study was to determine if it is possible to classify splenic and gastric lymphomas according to karyometric features. Methods: The material consisted of 16 splenic and 14 gastric lymphomas. The measurements were done with the AnalySIS image analysis system. In each case at least 100 nuclei were selected, and 19 different geometric parameters were measured. Results: On statistical analysis, the nuclei of splenic and gastric lymphomas showed differences in most parameters, but significant overlap of the values was present. Neural networks were trained and used for classification of the data. By this method, the nuclei were properly classified with a sensitivity of 0.75 and specificity of 0.71. In addition, in all the cases the majority of the nuclei were properly classified, thus allowing correct classification of all the cases into “splenic” or “gastric”. Conclusion: These results support the view that mucosa-associated lymphoid tissue lymphomas and splenic marginal-zone lymphomas are separate entities.
Keywords: Marginal zone lymphoma, image analysis, karyometry, neural network, classification
