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The effect of combining two echo times in automatic brain tumor classification by MRS

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The effect of combining two echo times in automatic brain tumor classification by MRS

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dc.contributor.author García-Gómez, Juan M es_ES
dc.contributor.author Tortajada, Salvador es_ES
dc.contributor.author Vidal, César es_ES
dc.contributor.author Julià -Sapé, Margalida es_ES
dc.contributor.author Luts, Jan es_ES
dc.contributor.author Moreno-Torres, Àngel es_ES
dc.contributor.author Van Huffel, Sabine es_ES
dc.contributor.author Arús, Carles es_ES
dc.contributor.author Robles Viejo, Montserrat es_ES
dc.date.accessioned 2020-10-05T07:00:03Z
dc.date.available 2020-10-05T07:00:03Z
dc.date.issued 2008-12 es_ES
dc.identifier.issn 0952-3480 es_ES
dc.identifier.uri http://hdl.handle.net/10251/151098
dc.description.abstract [EN] H-1 MRS is becoming an accurate, non-invasive technique for initial examination of brain masses. We investigated if the combination of single-voxel H-1 MRS at 1.5 T at two different (TEs), short TE (PRESS or STEAM, 20-32 ms) and long TE (PRESS, 135-136 ms), improves the classification of brain tumors over using only one echo TE. A clinically validated dataset of 50 low-grade meningiomas, 105 aggressive tumors (glioblastoma and metastasis). and 30 low-grade glial tumors (astrocytomas grade II, oligodendrogliomas anti oligoastrocytomas) was used to fit predictive models based on the combination of features from short-TEs and long-TE spectra. A new approach that combines the two consecutively was used to produce a single data vector from which relevant features of the two TE spectra could be extracted by means of three algorithms: stepwise, reliefF, and principal components analysis. Least Squares support vector machines and linear discriminant analysis were applied to fit the pairwise and multiclass classifiers, respectively. Significant differences in performance were found when short-TE, long-TE or both spectra combined were used as input. In our dataset, to discriminate meningiomas, the combination of the two TE acquisitions produced optimal performance. To discriminate aggressive tumors from low-grade filial tumours, the use of short-TE acquisition alone was preferable. The classifier development strategy used here lends itself to automated learning and test performance processes, which may, be of use for future web-based multicentric classifier development studies. Copyright (c) 2008 John Wiley & Sons, Ltd. es_ES
dc.description.sponsorship This work was partially funded by the European Commission: eTUMOUR (contract no. FP6-2002-LIFESCI-HEALTH 503094). HealthAgents (contract no. FP6-2005-IST 027213), BIOPATTERN (contract no. FP6-2002-IST 508803); Programa de Apoyo a la Investigacion y Desarrollo, PAID-00-06 UPV; Research Council KUL: GOA-AMBioRICS, Centers-of-excellence optimisation; Belgian Federal Government: DWTC, IUAPV P6/04 (DYSCO 2007-2011). Jan Luts is a PhD student supported by an IWT grant. We thank INTERPRET partners for providing data, in particular, C. Majos (IDI-Bellvitge), John Griffiths (SGUL), Arend Heerschap (RU), Witold Gajewicz (MUL), and Jorge Calvar (FLENI). We also thank Guillem Mercadal for his feedback about 1H MRS data processing. es_ES
dc.language Inglés es_ES
dc.publisher John Wiley & Sons es_ES
dc.relation.ispartof NMR in Biomedicine es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject 1H MRS es_ES
dc.subject Short TE es_ES
dc.subject Long TE es_ES
dc.subject Pattern recognition es_ES
dc.subject Brain es_ES
dc.subject Cancer es_ES
dc.subject Decision support systems es_ES
dc.subject.classification CIENCIAS DE LA COMPUTACION E INTELIGENCIA ARTIFICIAL es_ES
dc.subject.classification FISICA APLICADA es_ES
dc.title The effect of combining two echo times in automatic brain tumor classification by MRS es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1002/nbm.1288 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP6/508803/EU/Computational intelligence for Bio-pattern analysis in support of eHealthcare/BIOPATTERN/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP6/027214/EU/Agent-based Distributed Decision Support System for brain tumour diagnosis and prognosis/HEALTHAGENTS/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP6/503094/EU/WEB ACCESSIBLE MR DECISION SUPPORT SYSTEM FOR BRAIN TUMOUR DIAGNOSIS AND PROGNOSIS, INCORPORATING IN VIVO AND EX VIVO GENOMIC AND METABOLIMIC DATA/ETUMOUR/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/UPV//PAID-00-06/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/BELSPO//P6%2F04/ es_ES
dc.rights.accessRights Cerrado es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Física Aplicada - Departament de Física Aplicada es_ES
dc.description.bibliographicCitation García-Gómez, JM.; Tortajada, S.; Vidal, C.; Julià -Sapé, M.; Luts, J.; Moreno-Torres, À.; Van Huffel, S.... (2008). The effect of combining two echo times in automatic brain tumor classification by MRS. NMR in Biomedicine. 21(10):1112-1125. https://doi.org/10.1002/nbm.1288 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1002/nbm.1288 es_ES
dc.description.upvformatpinicio 1112 es_ES
dc.description.upvformatpfin 1125 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 21 es_ES
dc.description.issue 10 es_ES
dc.identifier.pmid 18759382 es_ES
dc.relation.pasarela S\36120 es_ES
dc.contributor.funder KU Leuven es_ES
dc.contributor.funder European Commission es_ES
dc.contributor.funder Belgian Federal Science Policy Office es_ES
dc.contributor.funder Universitat Politècnica de València es_ES
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