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dc.contributor.author | Fuster García, Elíes | es_ES |
dc.contributor.author | Navarro., Clara | es_ES |
dc.contributor.author | Vicente Robledo, Javier | es_ES |
dc.contributor.author | Tortajada Velert, Salvador | es_ES |
dc.contributor.author | García Gómez, Juan Miguel | es_ES |
dc.contributor.author | Sáez Silvestre, Carlos | es_ES |
dc.contributor.author | Calvar ., Jorge | es_ES |
dc.contributor.author | Griffiths ., John | es_ES |
dc.contributor.author | Julia Sape, Margarita | es_ES |
dc.contributor.author | Howe ., Franklyn A | es_ES |
dc.contributor.author | Pujol ., Jesús | es_ES |
dc.contributor.author | Peet ., Andrew C | es_ES |
dc.contributor.author | Heerschap ., Arend | es_ES |
dc.contributor.author | Moreno Torres, Àngel | es_ES |
dc.contributor.author | Martínez-Bisbal, M.Carmen | es_ES |
dc.contributor.author | Martinez Granados, Beatriz | es_ES |
dc.contributor.author | Wesseling ., Pieter | es_ES |
dc.contributor.author | Semmler ., Wolfhard | es_ES |
dc.contributor.author | Capellades ., Jaume | es_ES |
dc.contributor.author | Majós ., Carles | es_ES |
dc.contributor.author | Alberich Bayarri, Ángel | es_ES |
dc.contributor.author | Capdevila ., Antoni | es_ES |
dc.contributor.author | Monleón ., Daniel | es_ES |
dc.contributor.author | Marti Bonmati, Luis | es_ES |
dc.contributor.author | Arús ., Carles | es_ES |
dc.contributor.author | Celda ., Bernardo | es_ES |
dc.contributor.author | Robles Viejo, Montserrat | es_ES |
dc.date.accessioned | 2014-09-11T16:04:51Z | |
dc.date.issued | 2011-02 | |
dc.identifier.issn | 0968-5243 | |
dc.identifier.uri | http://hdl.handle.net/10251/39576 | |
dc.description | The final publication is available at Springer via http://dx.doi.org/10.1007/s10334-010-0241-8 | es_ES |
dc.description.abstract | Object: This study demonstrates that 3T SV-MRS data can be used with the currently available automatic brain tumour diagnostic classifiers which were trained on databases of 1.5T spectra. This will allow the existing large databases of 1.5T MRS data to be used for diagnostic classification of 3T spectra, and perhaps also the combination of 1.5T and 3T databases. Materials and methods: Brain tumour classifiers trained with 154 1.5T spectra to discriminate among high grade malignant tumours and common grade II glial tumours were evaluated with a subsequently-acquired set of 155 1.5T and 37 3T spectra. A similarity study between spectra and main brain tumour metabolite ratios for both field strengths (1.5T and 3T) was also performed. Results: Our results showed that classifiers trained with 1.5T samples had similar accuracy for both test datasets (0.87 ± 0.03 for 1.5T and 0.88 ± 0.03 for 3.0T). Moreover, non-significant differences were observed with most metabolite ratios and spectral patterns. Conclusion: These results encourage the use of existing classifiers based on 1.5T datasets for diagnosis with 3T 1H SV-MRS. The large 1.5T databases compiled throughout many years and the prediction models based on 1.5T acquisitions can therefore continue to be used with data from the new 3T instruments. © 2011 ESMRMB. | es_ES |
dc.description.sponsorship | We would like to thank Miriam Camison-Sanchez for help in the quality control of the MRS data and diagnosis validation. This work was partially funded by the European Commission (FP6-2002-LIFESCIHEALTH 503094) and (IST-2004-27214), the I + D support program of the Universitat Politecnica de Valencia and by the Health Institute Carlos III through the RETICS Combiomed, RD07/0067/2001. CIBER-BBN is an initiative funded by the VI National R&D&D&I Plan 2008-2011, CIBER Actions and financed by the Instituto de Salud Carlos III with assistance from the European Regional Development Fund. The authors acknowledge to Programa Torres Quevedo from Ministerio de Educacion y Ciencia, co-founded by the European Social Fund (PTQ05-02-03386, PTQ-08-01-06802, PTQ-08-01-06817). | en_EN |
dc.language | Inglés | es_ES |
dc.publisher | Springer Verlag (Germany) | es_ES |
dc.relation.ispartof | Magnetic Resonance Materials in Physics, Biology and Medicine | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | Brain tumours | es_ES |
dc.subject | Clinical decision support systems | es_ES |
dc.subject | Magnetic resonance spectroscopy | es_ES |
dc.subject.classification | FISICA APLICADA | es_ES |
dc.title | Compatibility between 3T 1H SV-MRS data and automatic brain tumour diagnosis support systems based on databases of 1.5T 1H SV-MRS spectra | es_ES |
dc.type | Artículo | es_ES |
dc.embargo.terms | forever | es_ES |
dc.identifier.doi | 10.1007/s10334-010-0241-8 | |
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/MICINN//RD07%2F0067%2F2001/ES/RED TEMÁTICA DE INVESTIGACIÓN COOPERATIVA EN BIOMEDICINA COMPUTACIONAL/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MEC//PTQ05-02-03386/ES/PTQ05-02-03386/ / | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MICINN//PTQ-08-01-06802/ES/PTQ-08-01-06802/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MICINN//PTQ-08-01-06817/ES/PTQ-08-01-06817/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Instituto Universitario de Aplicaciones de las Tecnologías de la Información - Institut Universitari d'Aplicacions de les Tecnologies de la Informació | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Física Aplicada - Departament de Física Aplicada | es_ES |
dc.description.bibliographicCitation | Fuster García, E.; Navarro., C.; Vicente Robledo, J.; Tortajada Velert, S.; García Gómez, JM.; Sáez Silvestre, C.; Calvar ., J.... (2011). Compatibility between 3T 1H SV-MRS data and automatic brain tumour diagnosis support systems based on databases of 1.5T 1H SV-MRS spectra. Magnetic Resonance Materials in Physics, Biology and Medicine. 24(1):35-42. https://doi.org/10.1007/s10334-010-0241-8 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | http://link.springer.com/article/10.1007/s10334-010-0241-8 | es_ES |
dc.description.upvformatpinicio | 35 | es_ES |
dc.description.upvformatpfin | 42 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 24 | es_ES |
dc.description.issue | 1 | es_ES |
dc.relation.senia | 217489 | |
dc.contributor.funder | Ministerio de Ciencia e Innovación | es_ES |
dc.contributor.funder | European Commission | es_ES |
dc.contributor.funder | Universitat Politècnica de València | es_ES |
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