García-Gómez, JM and Luts, J and Julià-Sapé, M and Krooshof, P and Tortajada, S and Robledo, JV and Melssen, W and Fuster-García, E and Olier, I and Postma, G and Monleón, D and Moreno-Torres, À and Pujol, J and Candiota, A-P and Martínez-Bisbal, MC and Suykens, J and Buydens, L and Celda, B and Van Huffel, S and Arús, C and Robles, M (2008) Multiproject–multicenter evaluation of automatic brain tumor classification by magnetic resonance spectroscopy. Magnetic Resonance Materials in Physics, Biology and Medicine, 22. 5 - 18. ISSN 0968-5243

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Abstract

JUSTIFICATION: Automatic brain tumor classification by MRS has been under development for more than a decade. Nonetheless, to our knowledge, there are no published evaluations of predictive models with unseen cases that are subsequently acquired in different centers. The multicenter eTUMOUR project (2004-2009), which builds upon previous expertise from the INTERPRET project (2000-2002) has allowed such an evaluation to take place.MATERIALS AND METHODS: A total of 253 pairwise classifiers for glioblastoma, meningioma, metastasis, and low-grade glial diagnosis were inferred based on 211 SV short TE INTERPRET MR spectra obtained at 1.5 T (PRESS or STEAM, 20-32 ms) and automatically pre-processed. Afterwards, the classifiers were tested with 97 spectra, which were subsequently compiled during eTUMOUR.RESULTS: In our results based on subsequently acquired spectra, accuracies of around 90% were achieved for most of the pairwise discrimination problems. The exception was for the glioblastoma versus metastasis discrimination, which was below 78%. A more clear definition of metastases may be obtained by other approaches, such as MRSI + MRI.$$n$$nCONCLUSIONS: The prediction of the tumor type of in-vivo MRS is possible using classifiers developed from previously acquired data, in different hospitals with different instrumentation under the same acquisition protocols. This methodology may find application for assisting in the diagnosis of new brain tumor cases and for the quality control of multicenter MRS databases.

Item Type: Article
Additional Information: Open Access paper, © 2008 The Authors, Springer Verlag
Uncontrolled Keywords: Magnetic resonance spectroscopy, Pattern classification, Brain tumors, Decision support systems, Multicenter evaluation study
Subjects: R Medicine > R Medicine (General)
Divisions: Faculty of Medicine and Health Sciences > Institute for Science and Technology in Medicine
Depositing User: Symplectic
Date Deposited: 10 Nov 2015 16:31
Last Modified: 27 May 2016 15:58
URI: http://eprints.keele.ac.uk/id/eprint/1125

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