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dc.contributor.author | Aguado-Sarrió, Eric | es_ES |
dc.contributor.author | Prats-Montalbán, José Manuel | es_ES |
dc.contributor.author | Sanz-Requena, R. | es_ES |
dc.contributor.author | Garcia-Marti, G. | es_ES |
dc.contributor.author | Marti-Bonmati, L. | es_ES |
dc.contributor.author | Ferrer, Alberto | es_ES |
dc.date.accessioned | 2018-07-09T06:59:50Z | |
dc.date.available | 2018-07-09T06:59:50Z | |
dc.date.issued | 2017 | es_ES |
dc.identifier.issn | 0169-7439 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/105558 | |
dc.description.abstract | [EN] In this work, the capability of imaging biomarkers obtained from multivariate curve resolution-alternating least squares (MCR-ALS), in combination with those obtained from first and second-generation pharmacokinetic models, have been studied for improving prostate cancer tumor depiction using partial least squares- discriminant analysis (PLS-DA). The main goal of this work is to improve tissue classification properties selecting the best biomarkers in terms of prediction. A wrapped double cross-validation method has been applied for the variable selection process. Using the best PLS-DA model, prostate tissues can be classified obtaining 13.4% of false negatives and 7.4% of false positives. Using MCR-ALS biomarkers yields the best models in terms of parsimony and classification performance. | es_ES |
dc.description.sponsorship | This research has been supported by "Generalitat Valenciana (Conselleria d'Educacio, Investigacio, Cultura I Esport)" under the project AICO/2016/061. | en_EN |
dc.language | Inglés | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation.ispartof | Chemometrics and Intelligent Laboratory Systems | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | MCR-ALS | es_ES |
dc.subject | DCE-MRI | es_ES |
dc.subject | Biomarkers | es_ES |
dc.subject | Prostate Tumors | es_ES |
dc.subject.classification | ESTADISTICA E INVESTIGACION OPERATIVA | es_ES |
dc.title | Biomarker comparison and selection for prostate cancer detection in Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1016/j.chemolab.2017.04.003 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/UPV//UPV-FE-16-B18/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/GVA//AICO%2F2016%2F061/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.date.embargoEndDate | 2019-06-15 | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Física Aplicada - Departament de Física Aplicada | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat | es_ES |
dc.description.bibliographicCitation | Aguado-Sarrió, E.; Prats-Montalbán, JM.; Sanz-Requena, R.; Garcia-Marti, G.; Marti-Bonmati, L.; Ferrer, A. (2017). Biomarker comparison and selection for prostate cancer detection in Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI). Chemometrics and Intelligent Laboratory Systems. 165:38-45. https://doi.org/10.1016/j.chemolab.2017.04.003 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1016/j.chemolab.2017.04.003 | es_ES |
dc.description.upvformatpinicio | 38 | es_ES |
dc.description.upvformatpfin | 45 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 165 | es_ES |
dc.relation.pasarela | S\337551 | es_ES |
dc.contributor.funder | Generalitat Valenciana | es_ES |
dc.contributor.funder | Universitat Politècnica de València | es_ES |