Several studies have described malignancy-associated changes (MACs) of chromatin arrangement in the nuclei of apparently normal cells adjacent to and distant from an invasive cancer area. MAC assessment is a hard task, since it requires a deep knowledge of morphologic features of chromatin arrangement. The aim of this work is to verify the reproducibility of the subjective evaluation of the expert on the basis of a decision support system (DSS) that automatically and objectively reproduces MAC diagnosis. A set of 61 patients with suspected clinical diagnosis for lung cancer has been taken into account. The scientist who first described MAC defined each patient as MAC positive or negative on the basis of the MAC diagnosis performed on all cells of the related cytologic sample. A DSS based on an artificial neural network has been set up to learn the relation between 14 morphometric and texture parameters, computed on each nucleus by image processing techniques, with the MAC diagnosis of the expert on each cell. The results show that an objective automatic assessment on MAC by the DSS can effectively support the MAC diagnosis. The method adopted in this approach may be also appropriate for other problems, where an automatic classification of visually inspected patterns of biological micro- and submicrostructure is needed.

A decision support system to detect morphologic changes of chromatin arrangement in normal-appearing cells.

SACILE, ROBERTO;RUGGIERO, CARMELINA;
2003

Abstract

Several studies have described malignancy-associated changes (MACs) of chromatin arrangement in the nuclei of apparently normal cells adjacent to and distant from an invasive cancer area. MAC assessment is a hard task, since it requires a deep knowledge of morphologic features of chromatin arrangement. The aim of this work is to verify the reproducibility of the subjective evaluation of the expert on the basis of a decision support system (DSS) that automatically and objectively reproduces MAC diagnosis. A set of 61 patients with suspected clinical diagnosis for lung cancer has been taken into account. The scientist who first described MAC defined each patient as MAC positive or negative on the basis of the MAC diagnosis performed on all cells of the related cytologic sample. A DSS based on an artificial neural network has been set up to learn the relation between 14 morphometric and texture parameters, computed on each nucleus by image processing techniques, with the MAC diagnosis of the expert on each cell. The results show that an objective automatic assessment on MAC by the DSS can effectively support the MAC diagnosis. The method adopted in this approach may be also appropriate for other problems, where an automatic classification of visually inspected patterns of biological micro- and submicrostructure is needed.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/376847
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