Computer-based malignancy grading of astrocytomas employing a support vector machine classifier, the WHO grading system and the regular hematoxylin-eosin diagnostic staining procedure

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dc.contributor.authorGlotsos, D.en
dc.contributor.authorSpyridonos, P.en
dc.contributor.authorPetalas, P.en
dc.contributor.authorCavouras, D.en
dc.contributor.authorRavazoula, P.en
dc.contributor.authorDadioti, P. A.en
dc.contributor.authorLekka, I.en
dc.contributor.authorNikiforidis, G.en
dc.date.accessioned2015-11-24T19:28:01Z
dc.date.available2015-11-24T19:28:01Z
dc.identifier.issn0884-6812-
dc.identifier.urihttps://olympias.lib.uoi.gr/jspui/handle/123456789/22861
dc.rightsDefault Licence-
dc.subject*Artificial Intelligenceen
dc.subjectAstrocytoma/diagnosis/*pathologyen
dc.subjectBiopsyen
dc.subjectBrain Neoplasms/diagnosis/*pathologyen
dc.subjectEosine Yellowish-(YS)/chemistryen
dc.subjectHematoxylin/chemistryen
dc.subjectHumansen
dc.subject*Image Processing, Computer-Assisteden
dc.subject*Softwareen
dc.subjectWorld Health Organizationen
dc.titleComputer-based malignancy grading of astrocytomas employing a support vector machine classifier, the WHO grading system and the regular hematoxylin-eosin diagnostic staining procedureen
heal.abstractOBJECTIVE: To investigate and develop an automated technique for astrocytoma malignancy grading compatible with the clinical routine. STUDY DESIGN: One hundred forty biopsies of astrocytomas were collected from 2 hospitals. The degree of tumor malignancy was defined as low or high according to the World Health Organization grading system. From each biopsy, images were digitized and segmented to isolate nuclei from background tissue. Morphologic and textural nuclear features were quantified to encode tumor malignancy. Each case was represented by a 40-dimensional feature vector. An exhaustive search procedure in feature space was utilized to determine the best feature combination that resulted in the smallest classification error. Low and high grade tumors were discriminated using support vector machines (SVMs). To evaluate the system performance, all available data were split randomly into training and test sets. RESULTS: The best vector combination consisted of 3 textural and 2 morphologic features. Low and high grade cases were discriminated with an accuracy of 90.7% and 88.9%, respectively, using an SVM classifier with polynomial kernel of degree 2. CONCLUSION: The proposed methodology was based on standards that are common in daily clinical practice and might be used in parallel with conventional grading as a second-opinion tool to reduce subjectivity in the classification of astrocytomas.en
heal.accesscampus-
heal.fullTextAvailabilityTRUE-
heal.identifier.secondaryhttp://www.ncbi.nlm.nih.gov/pubmed/15131894-
heal.journalNameAnal Quant Cytol Histolen
heal.journalTypepeer-reviewed-
heal.languageen-
heal.publicationDate2004-
heal.recordProviderΠανεπιστήμιο Ιωαννίνων. Σχολή Επιστημών Υγείας. Τμήμα Ιατρικήςel
heal.typejournalArticle-
heal.type.elΆρθρο Περιοδικούel
heal.type.enJournal articleen

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