Please use this identifier to cite or link to this item: https://olympias.lib.uoi.gr/jspui/handle/123456789/11389
Full metadata record
DC FieldValueLanguage
dc.contributor.authorExarchos, K.P.,en
dc.contributor.authorGoletsis, Y.,en
dc.contributor.authorFotiadis, D.I.en
dc.date.accessioned2015-11-24T17:05:50Z-
dc.date.available2015-11-24T17:05:50Z-
dc.identifier.urihttps://olympias.lib.uoi.gr/jspui/handle/123456789/11389-
dc.rightsDefault Licence-
dc.subjectOral cancer, classification, decision support system, gene expression, reoccurrence predictionen
dc.titleMultiparametric decision support system for the prediction of oral cancer reoccurenceen
heal.typejournalArticle-
heal.type.enJournal articleen
heal.type.elΆρθρο Περιοδικούel
heal.languageen-
heal.accesscampus-
heal.recordProviderΠανεπιστήμιο Ιωαννίνων. Σχολή Οικονομικών και Κοινωνικών Επιστημών. Τμήμα Οικονομικών Επιστημώνel
heal.publicationDate2011-
heal.abstractOral squamous cell carcinoma (OSCC) constitutes the predominant neoplasm of the head and neck region, featuring particularly aggressive nature, associated with quite unfavorable prognosis. In this work formulate a Decision Support System (DSS) which integrates a multitude of heterogeneous data (clinical, imaging and genomic), thus, framing all manifestations of the disease. Our primary aim is to identify the factors that dictate OSCC progression and subsequently predict potential relapses (local or metastatic) of the disease. The discrimination potential of each source of data is initially explored separately, and afterwards the individual predictions are combined to yield a consensus decision achieving complete discrimination between patients with and without a disease relapse.en
heal.journalTypepeer reviewed-
heal.fullTextAvailabilityTRUE-
Appears in Collections:Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά) - ΟΕ

Files in This Item:
File Description SizeFormat 
goletsis-2011-multiparametric decision support.pdf244.91 kBAdobe PDFView/Open    Request a copy


This item is licensed under a Creative Commons License Creative Commons