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Identification of significant metabolic markers from MRSI data for brain cancer classification

Zervakis Michalis, Michalis E. Blazadonakis, Geert J. Postma, Arend Heerschap, Michail G. Kounelakis

Απλή Εγγραφή


URIhttp://purl.tuc.gr/dl/dias/45FA353A-A535-4181-A124-B84604612246-
Αναγνωριστικόhttps://doi.org/10.1109/BIBE.2008.4696668-
Γλώσσαen-
Μέγεθος7 pagesen
ΤίτλοςIdentification of significant metabolic markers from MRSI data for brain cancer classificationen
ΔημιουργόςZervakis Michalisen
ΔημιουργόςΖερβακης Μιχαληςel
ΔημιουργόςMichalis E. Blazadonakisen
Δημιουργός Geert J. Postmaen
ΔημιουργόςArend Heerschapen
ΔημιουργόςMichail G. Kounelakisen
ΕκδότηςInstitute of Electrical and Electronics Engineersen
ΠερίληψηInvestigation of the significance of metabolites peak area ratios derived from brain magnetic resonance spectroscopic imaging (MRSI) spectra, in brain tumors classification, has been applied. Results have shown that in most binary classifications using SVM and LSSVM classifiers, the accuracy achieved was greater than 0.90 AUC except the case of Gliomas grade 2 vs Gliomas grade 3 where 0.84 AUC was recorded due to the great heterogeneity of these two types of tumor. The minimum but also biologically significant set of features (markers), where maximum AUCs recorded, was derived. Ratios of N-acetyl-aspartate, choline, creatine and lipids metabolites found to play the most crucial role in brain tumors discrimination. The biological importance of these markers was also verified by literature. Finally the influence of four magnetic resonance image (MRI) intensities on the classification process was also measured. It was found that MRI data do not improve significantly the classification accuracies.en
ΤύποςΠλήρης Δημοσίευση σε Συνέδριοel
ΤύποςConference Full Paperen
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2015-10-24-
Ημερομηνία Δημοσίευσης2008-
Θεματική ΚατηγορίαClinical laboratory techniciansen
Θεματική ΚατηγορίαMedical laboratory techniciansen
Θεματική Κατηγορίαmedical technologistsen
Θεματική Κατηγορίαclinical laboratory techniciansen
Θεματική Κατηγορίαmedical laboratory techniciansen
Θεματική ΚατηγορίαSystems, Neuroadaptive (Bioengineering)en
Θεματική Κατηγορίαneuroadaptive systems bioengineeringen
Θεματική Κατηγορίαsystems neuroadaptive bioengineeringen
Βιβλιογραφική ΑναφοράM. G. Kounelakis, M. E. Zervakis, M. E. Blazadonakis, G. J. Postma, L.M. Buydens, A. Heerschap, X. Kotsiakis ,"Identification of significant metabolic markers from MRSI data for brain cancer classification ,"in 2008 8th IEEE Intern.Conf. on BioInf.s and BioEngineering ,pp.1-6.doi:10.1109/BIBE.2008.4696668en

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