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

Zoulias, EA; Asvestas, PA; Matsopoulos, GK; Tseleni-Balafouta, S

dc.contributor.author	Zoulias, EA	en
dc.contributor.author	Asvestas, PA	en
dc.contributor.author	Matsopoulos, GK	en
dc.contributor.author	Tseleni-Balafouta, S	en
dc.date.accessioned	2014-03-01T02:01:39Z
dc.date.available	2014-03-01T02:01:39Z
dc.date.issued	2011	en
dc.identifier.issn	08846812	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/29219
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-80955140473&partnerID=40&md5=7c86a1631fb99378b586a275ba51d332	en
dc.subject	Artificial neural networks	en
dc.subject	Aspiration	en
dc.subject	Classification	en
dc.subject	Decision support system	en
dc.subject	Fine needle	en
dc.subject	Thyroid malignancy	en
dc.subject.other	article	en
dc.subject.other	artificial neural network	en
dc.subject.other	aspiration biopsy	en
dc.subject.other	classification	en
dc.subject.other	decision support system	en
dc.subject.other	diagnostic accuracy	en
dc.subject.other	diagnostic test accuracy study	en
dc.subject.other	human	en
dc.subject.other	human tissue	en
dc.subject.other	k nearest neighbor	en
dc.subject.other	major clinical study	en
dc.subject.other	priority journal	en
dc.subject.other	sensitivity and specificity	en
dc.subject.other	support vector machine	en
dc.subject.other	thyroid cancer	en
dc.subject.other	Biopsy, Fine-Needle	en
dc.subject.other	Decision Support Techniques	en
dc.subject.other	Humans	en
dc.subject.other	Neural Networks (Computer)	en
dc.subject.other	Thyroid Gland	en
dc.subject.other	Thyroid Neoplasms	en
dc.title	A decision support system for assisting fine needle aspiration diagnosis of thyroid malignancy	en
heal.type	journalArticle	en
heal.publicationDate	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. © Science Printers and Publishers, Inc.	en
heal.journalName	Analytical and Quantitative Cytology and Histology	en
dc.identifier.volume	33	en
dc.identifier.issue	4	en
dc.identifier.spage	215	en
dc.identifier.epage	222	en