![Institutional Repository [SANDBOX]](images/dias.logo.png "Institutional Repository [SANDBOX]"){kind=logo}
Institutional Repository [SANDBOX]
Technical University of Crete
Technical University of Crete
EN
|
EL
| URI: | http://purl.tuc.gr/dl/dias/45FA353A-A535-4181-A124-B84604612246 | ||
| Year | 2008 | ||
| Type of Item | Conference Full Paper | ||
| License |
|
||
| Bibliographic Citation | M. 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.2008.4696668 https://doi.org/10.1109/BIBE.2008.4696668 | ||
| Appears in Collections |
Investigation 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.
Copyright © DIAS 2013
