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Effect of feature extraction for brain tumor classification based on short echo time 1H MR spectra

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Effect of feature extraction for brain tumor classification based on short echo time 1H MR spectra

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dc.contributor.author Luts, Jan es_ES
dc.contributor.author Poullet, Jean-Baptiste es_ES
dc.contributor.author Garcia-Gomez, Juan M es_ES
dc.contributor.author Heerschap, Arend es_ES
dc.contributor.author Robles, Montserrat es_ES
dc.contributor.author Suykens, Johan A.K. es_ES
dc.contributor.author Van Huffel, Sabine es_ES
dc.date.accessioned 2020-10-07T03:35:28Z
dc.date.available 2020-10-07T03:35:28Z
dc.date.issued 2008-08 es_ES
dc.identifier.issn 0740-3194 es_ES
dc.identifier.uri http://hdl.handle.net/10251/151315
dc.description.abstract [EN] This study examines the effect of feature extraction methods prior to automated pattern recognition based on magnetic resonance spectroscopy (MRS) for brain tumor diagnosis. Since individual inspection of spectra is time-consuming and requires specific spectroscopic expertise, the introduction of clinical decision support systems.(DSSs) is expected to strongly promote the clinical use of MRS. This study focuses on the feature extraction step in the preprocessing protocol of MRS when using a DSS. On two independent data sets, encompassing single-voxel and multi-voxel data, it is observed that the use of the full spectra together with a kernel-based technique, handling high dimensional data, or using an automated pattern recognition method based on independent component analysis or Relief-F achieves accurate performances. In addition, these approaches have low cost and are easy to automate. When sophisticated quantification methods are used in a DSS, user interaction should be minimized. The computationally intensive quantification techniques do not tend to increase the performance in these circumstances. The results suggest to simplify the feature reduction step in the preprocessing protocol when using a DSS purely for classification purposes. This can greatly speed up the execution of classifiers and DSSs and may accelerate their introduction into clinical practice. es_ES
dc.description.sponsorship The authors thank the Institute for Molecules and Materials, Analytical Chemistry, Chemometrics Research Department of the Radboud University Nijmegen for preprocessing the MRSI data. Margarida Julia-Sape is gratefully acknowledged for all data management tasks; the clinical partners represented by Dr. Witold Gajewicz (MUL, Lodzi, Poland) and Jorge Andres Calvar (FLENI, Ciudad de Buenos Aires, Argentina) are gratefully acknowledged for providing data. Grant sponsors: Institute for the Promotion of Innovation through Science and Technology in Flanders (IWT-Vlaanderen), Research Council KUL: GOA-AMBioRICS, Centers-of-Excellence in Optimisation; Grant sponsor: IDO; Grant number: 05/010 EEG-fMRI; Grant sponsor: Flemish Government: FWO; Grant numbers: G.0360.05, G.0519.06, G.0341.07, G.0321.06, G.0302.07; Grant sponsors: ICCoS, ANMMM, IWT; Grant sponsor: Belgian Federal Government: DWTC; Grant numbers: IUAP IV-02 (1996 2001), IUAP V-22 (2002 2006);Grant sponsor: Belgian Federal Science Policy Office;Grant number: IUAP P6/04; Grant sponsor: EU; Grant number: IST-1999-10310; Grant sponsor: BIOPATTERN; Grant number: FP6-2002-IST 508803; Grant sponsor: eTUMOUR; Grant number: FP6-2002-LIFESCIHEALTH 503094; Grant sponsor: HealthAgents; Grant number: FP6-2005-IST 027213; Grant sponsor: FAST; Grant number: FP6-019279-2; Grant sponsor: ESA: Cardiovascular Control; Grant number: Prodex-8 C90242. es_ES
dc.language Inglés es_ES
dc.publisher John Wiley & Sons es_ES
dc.relation EC/FP6-2002-LIFESCIHEALTH 503094 es_ES
dc.relation EC/FP6-2005-IST 027213 es_ES
dc.relation EC/FP6-2002-IST 508803 es_ES
dc.relation BELSPO/IUAP P6/04 es_ES
dc.relation FWO/G.0360.05 es_ES
dc.relation FWO/G.0519.06 es_ES
dc.relation FWO/G.0341.07 es_ES
dc.relation FWO/G.0321.06 es_ES
dc.relation FWO/G.0302.07 es_ES
dc.relation EC/IST-1999-10310 es_ES
dc.relation EC/FP6-019279-2 es_ES
dc.relation ESA/Prodex-8 C90242 es_ES
dc.relation Belgian Federal Government/IUAP IV-02 es_ES
dc.relation Belgian Federal Government/IUAP V-22 es_ES
dc.relation.ispartof Magnetic Resonance in Medicine es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Brain tumors es_ES
dc.subject Feature extraction es_ES
dc.subject Classification es_ES
dc.subject Decision support system (DSS) es_ES
dc.subject Magnetic resonance spectroscopy (MRS) es_ES
dc.subject Magnetic resonance spectroscopic imaging (MRSI) es_ES
dc.subject.classification FISICA APLICADA es_ES
dc.subject.classification CIENCIAS DE LA COMPUTACION E INTELIGENCIA ARTIFICIAL es_ES
dc.title Effect of feature extraction for brain tumor classification based on short echo time 1H MR spectra es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1002/mrm.21626 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 Luts, J.; Poullet, J.; Garcia-Gomez, JM.; Heerschap, A.; Robles, M.; Suykens, JA.; Van Huffel, S. (2008). Effect of feature extraction for brain tumor classification based on short echo time 1H MR spectra. Magnetic Resonance in Medicine. 60(2):288-298. https://doi.org/10.1002/mrm.21626 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1002/mrm.21626 es_ES
dc.description.upvformatpinicio 288 es_ES
dc.description.upvformatpfin 298 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 60 es_ES
dc.description.issue 2 es_ES
dc.identifier.pmid 18666120 es_ES
dc.relation.pasarela S\36121 es_ES
dc.contributor.funder KU Leuven es_ES
dc.contributor.funder European Commission es_ES
dc.contributor.funder European Space Agency es_ES
dc.contributor.funder Belgian Federal Government es_ES
dc.contributor.funder Belgian Federal Science Policy Office es_ES
dc.contributor.funder Research Foundation Flanders es_ES
dc.contributor.funder Agency for Innovation by Science and Technology, Flanders es_ES
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