title: Classification of lung carcinoma by means of digital nuclear image analysis.
abstract: An investigation was performed of the maximum discriminating efficiency for each subgroup of digital nuclear image features and of the overall classification of nuclei from three types of human lung carcinomas in histologic sections: adenocarcinoma, small-cell carcinoma and squamous-cell carcinoma. The results indicate that, for each subgroup of features, the nuclei of the small-cell carcinomas are generally "correctly" classified in a higher percentage (80% to 100%) than are the nuclei of the adenocarcinomas (46% to 74%) and squamous-cell carcinomas (29% to 68%). The discriminant analysis for the overall classification selected features from most of the subgroups, suggesting that it is useful to perform nuclear image analysis with many subgroups having different properties. The overall classifications for the nuclei of the adenocarcinomas, small-cell carcinomas and squamous-cell carcinomas were, respectively, 81.4%, 93.2% and 74.7%. Before this technique can be applied to histopathologic diagnosis, a larger number of unselected lung carcinomas must be evaluated.
mesh_terms: D000230:Adenocarcinoma / Q000145:classification / Q000648:ultrastructure; D002277:Carcinoma / Q000145:classification* / Q000648:ultrastructure; D018288:Carcinoma, Small Cell / Q000145:classification / Q000648:ultrastructure; D002294:Carcinoma, Squamous Cell / Q000145:classification / Q000648:ultrastructure; D002467:Cell Nucleus / Q000648:ultrastructure; D006801:Humans; D008175:Lung Neoplasms / Q000145:classification* / Q000648:ultrastructure