A decision support system for assisting fine needle aspiration diagnosis of thyroid malignancy

dc.contributor.author	Ζούλιας, Εμμανουήλ Α.	el
dc.contributor.author	Ασβεστάς, Παντελής Α.	el
dc.contributor.author	Ματσόπουλος, Γεώργιος Κ.	el
dc.contributor.author	Τσελένη-Μπαλαφούτα, Σοφία	el
dc.date.accessioned	2015-02-09T11:15:38Z
dc.date.issued	2015-02-09
dc.identifier.uri	http://hdl.handle.net/11400/5918
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://www.ncbi.nlm.nih.gov/	en
dc.subject	Neural networks (Computer science)
dc.subject	Classification--Archives
dc.subject	Τεχνητά νευρωνικά δίκτυα
dc.subject	Ταξινόμηση
dc.title	A decision support system for assisting fine needle aspiration diagnosis of thyroid malignancy	en
heal.type	journalArticle
heal.classification	Medicine
heal.classification	Biomedical engineering
heal.classification	Ιατρική
heal.classification	Βιοϊατρική τεχνολογία
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh00006614
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85014237
heal.classificationURI	N/A-Ιατρική
heal.classificationURI	N/A-Βιοϊατρική τεχνολογία
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh90001937
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh85026720
heal.dateAvailable	10000-01-01
heal.language	en
heal.access	forever
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2011
heal.bibliographicCitation	Zoulias, E., Asvestas, P., Matsopoulos, G. and Tseleni-Balafouta, S. (August 2011). A decision support system for assisting fine needle aspiration diagnosis of thyroid malignancy. Analytical and Quantitative Cytology and Histology. 33(4). pp. 215-222. Science Printers and Publishers Inc: 2011.	en
heal.abstract	OBJECTIVE: To assist diagnosis of thyroid malignancy, implementing a decision support system (DSS) using fine needle aspiration biopsy (FNAB) data. STUDY DESIGN: The set of 2,035 thyroid smears contained 1,886 smears of nonmalignancy (class 1) and 150 smears of malignancy (class 2) verified histologically. For each smear, 67 medical features were considered by the expert, forming 2,036 feature vectors, which were fed into a DSS for discriminating between malignant and nonmalignant smears. The DSS comprised a feature selection and classification module using a combination of three classifiers, the artificial neural network, the support vector machines, and the k-nearest neighbor, under the majority vote procedure. RESULTS: The overall classification accuracy of the DSS was 98.6%, marginally better than the FNAB (97.3%). The DSS had lower sensitivity (89.1%) and better specificity (99.4%) compared to the FNAB. Regarding the smears characterized as "suspicious" by FNAB, a significant improvement of overall accuracy was obtained by the proposed DSS system (84.6%) compared to the FNAB (50.0%). CONCLUSION: The proposed DSS provides significant improvement compared to FNAB regarding discrimination of smears characterized by an expert as "suspicious," reducing the number of patients undergoing surgical procedures.	en
heal.publisher	Science Printers and Publishers Inc	en
heal.journalName	Analytical and Quantitative Cytology and Histology	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true