title: Discriminating benign from malignant thyroid lesions using artificial intelligence and statistical selection of morphometric features.
abstract: The objective of this study was to perform a comparative investigation of the capability of various classifiers in discriminating benign from malignant thyroid lesions. Using May Grunvald-Giemsa-stained smears taken by fine needle aspiration (FNA) and a custom image analysis system, 25 nuclear features describing the size, shape and texture of the nuclei were measured in each case. A statistical pre-processing of features revealed that only 4 of the 25 features are important when discriminating benign from malignant thyroid lesions, which were transformed and fed to four classifiers for subsequent analysis. The cases were divided into one set used for the training of classifiers, a second set used as the test set, and the remaining cases with no clear classification formed an ambiguous test set. Classification was performed at the nuclear and patient level. The technique described in this study produced encouraging results and promises to be a helpful tool in the daily cytological laboratory routine.
authors.affiliation: Service Central d'Anatomie et de Cytologie Pathologiques, Hospital Lariboisiere, Paris, France.
authors.forename: Beatrix
authors.initials: B
authors.lastname: Cochand-Priollet
authors.forename: Konstantinos
authors.initials: K
authors.lastname: Koutroumbas
authors.forename: Tatiana Mona
authors.initials: TM
authors.lastname: Megalopoulou
authors.forename: Abraham
authors.initials: A
authors.lastname: Pouliakis
authors.forename: Gregory
authors.initials: G
authors.lastname: Sivolapenko
authors.forename: Petros
authors.initials: P
authors.lastname: Karakitsos
mesh_terms: D000886:Anthropometry; D001185:Artificial Intelligence; D044963:Biopsy, Fine-Needle; D003627:Data Interpretation, Statistical; D006801:Humans; D013959:Thyroid Diseases / Q000175:diagnosis* / Q000473:pathology; D013964:Thyroid Neoplasms / Q000175:diagnosis* / Q000473:pathology