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dc.contributor.author | Larroza, A![]() |
es_ES |
dc.contributor.author | Materka, A![]() |
es_ES |
dc.contributor.author | López-Lereu, MP![]() |
es_ES |
dc.contributor.author | Monmeneu-Menadas, JV![]() |
es_ES |
dc.contributor.author | Bodi, V![]() |
es_ES |
dc.contributor.author | Moratal, D![]() |
es_ES |
dc.date.accessioned | 2020-04-17T12:49:10Z | |
dc.date.available | 2020-04-17T12:49:10Z | |
dc.date.issued | 2017 | es_ES |
dc.identifier.issn | 0720-048X | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/140871 | |
dc.description.abstract | [EN] The purpose of this study was to differentiate acute from chronic myocardial infarction using machine learning techniques and texture features extracted from cardiac magnetic resonance imaging (MRI). The study group comprised 22 cases with acute myocardial infarction (AMI) and 22 cases with chronic myocardial infarction (CMI). Cine and late gadolinium enhancement (LGE) MRI were analyzed independently to differentiate AMI from CMI. A total of 279 texture features were extracted from predefined regions of interest (ROIs): the infarcted area on LGE MRI, and the entire myocardium on cine MRI. Classification performance was evaluated by a nested cross-validation approach combining a feature selection technique with three predictive models: random forest, support vector machine (SVM) with Gaussian Kernel, and SVM with polynomial kernel. The polynomial SVM yielded the best classification performance. Receiver operating characteristic curves provided area-under-thecurve (AUC) (mean +/- standard deviation) of 0.86 +/- 0.06 on LGE MRI using 72 features; AMI sensitivity = 0.81 +/- 0.08 and specificity = 0.84 +/- 0.09. On cine MRI, AUC = 0.82 +/- 0.06 using 75 features; AMI sensitivity = 0.79 +/- 0.10 and specificity = 0.80 +/- 0.10. We concluded that texture analysis can be used for differentiation of AMI from CMI on cardiac LGE MRI, and also on standard cine sequences in which the infarction is visually imperceptible in most cases. | es_ES |
dc.description.sponsorship | This work was supported in part by the Spanish Ministerio de Economia y Competitividad (MINECO) and FEDER funds under grant BFU2015-64380-C2-2-R, by Instituto de Salud Carlos III and FEDER funds under grants FIS PI14/00271 and PIE15/00013 and by the Generalitat Valenciana under grant PROMETEO/2013/007. The first author, Andres Larroza, was supported by grant FPU12/01140 from the Spanish Ministerio de Educacion, Cultura y Deporte (MECD). | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation.ispartof | European Journal of Radiology | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | Cardiac MRI | es_ES |
dc.subject | Classification Myocardial infarction | es_ES |
dc.subject | Texture analysis | es_ES |
dc.subject.classification | TECNOLOGIA ELECTRONICA | es_ES |
dc.title | Differentiation between acute and chronic myocardial infarction by means of texture analysis of late gadolinium enhancement and cine cardiac magnetic resonance imaging | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1016/j.ejrad.2017.04.024 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MINECO//PI14%2F00271/ES/Fibrosis miocárdica tras un infarto de miocardio. Estudio traslacional para la innovación diagnóstica con resonancia magnética y para el entendimiento de los mecanismos reguladores/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MINECO//PIE15%2F00013/ES/A multidisciplinary project to advance in basic mechanisms, diagnosis, prediction, and prevention of cardiac damage in reperfused acute myocardial infarction/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/GVA//PROMETEO%2F2013%2F007/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MECD//FPU12%2F01140/ES/FPU12%2F01140/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MINECO//BFU2015-64380-C2-2-R/ES/ANALISIS DE TEXTURAS EN IMAGEN CEREBRAL MULTIMODAL POR RESONANCIA MAGNETICA PARA UNA DETECCION TEMPRANA DE ALTERACIONES EN LA RED Y BIOMARCADORES DE ENFERMEDAD/ | es_ES |
dc.rights.accessRights | Cerrado | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Ingeniería Electrónica - Departament d'Enginyeria Electrònica | es_ES |
dc.description.bibliographicCitation | Larroza, A.; Materka, A.; López-Lereu, M.; Monmeneu-Menadas, J.; Bodi, V.; Moratal, D. (2017). Differentiation between acute and chronic myocardial infarction by means of texture analysis of late gadolinium enhancement and cine cardiac magnetic resonance imaging. European Journal of Radiology. 92:78-83. https://doi.org/10.1016/j.ejrad.2017.04.024 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1016/j.ejrad.2017.04.024 | es_ES |
dc.description.upvformatpinicio | 78 | es_ES |
dc.description.upvformatpfin | 83 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 92 | es_ES |
dc.relation.pasarela | S\353066 | es_ES |
dc.contributor.funder | Generalitat Valenciana | es_ES |
dc.contributor.funder | Ministerio de Educación, Cultura y Deporte | es_ES |
dc.contributor.funder | Ministerio de Economía y Competitividad | es_ES |