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A radiomics evaluation of 2D and 3D MRI texture features to classify brain metastases from lung cancer and melanoma

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A radiomics evaluation of 2D and 3D MRI texture features to classify brain metastases from lung cancer and melanoma

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dc.contributor.author Ortiz-Ramón, Rafael es_ES
dc.contributor.author Larroza-Santacruz, Andrés es_ES
dc.contributor.author Arana Fernandez de Moya, Estanislao es_ES
dc.contributor.author Moratal, David es_ES
dc.date.accessioned 2018-09-27T04:30:23Z
dc.date.available 2018-09-27T04:30:23Z
dc.date.issued 2017 es_ES
dc.identifier.isbn 978-1-5090-2809-2
dc.identifier.issn 1557-170X es_ES
dc.identifier.uri http://hdl.handle.net/10251/108347
dc.description © 2017 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.description.abstract [EN] Brain metastases are occasionally detected before diagnosing their primary site of origin. In these cases, simple visual examination of medical images of the metastases is not enough to identify the primary cancer, so an extensive evaluation is needed. To avoid this procedure, a radiomics approach on magnetic resonance (MR) images of the metastatic lesions is proposed to classify two of the most frequent origins (lung cancer and melanoma). In this study, 50 T1-weighted MR images of brain metastases from 30 patients were analyzed: 27 of lung cancer and 23 of melanoma origin. A total of 43 statistical texture features were extracted from the segmented lesions in 2D and 3D. Five predictive models were evaluated using a nested cross-validation scheme. The best classification results were achieved using 3D texture features for all the models, obtaining an average AUC > 0.9 in all cases and an AUC = 0.947 +/- 0.067 when using the best model (naive Bayes). es_ES
dc.description.sponsorship Research supported in part by the Spanish Ministerio de Economia y Competitividad (MINECO) and FEDER funds under Grant BFU2015-64380-C2-2-R.
dc.language Inglés es_ES
dc.publisher IEEE Engineering in Medicine and Biology Society es_ES
dc.relation MINISTERIO DE ECONOMIA INDUSTRIA Y COMPETITIVIDAD /BFU2015-64380-C2-2-R es_ES
dc.relation.ispartof Proceedings Intenational Anual Conference of IEEE Engineering in Medicine and Biology Society es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject.classification TECNOLOGIA ELECTRONICA es_ES
dc.title A radiomics evaluation of 2D and 3D MRI texture features to classify brain metastases from lung cancer and melanoma es_ES
dc.type Artículo es_ES
dc.type Comunicación en congreso es_ES
dc.identifier.doi 10.1109/EMBC.2017.8036869 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Electrónica - Departament d'Enginyeria Electrònica es_ES
dc.description.bibliographicCitation Ortiz-Ramón, R.; Larroza-Santacruz, A.; Arana Fernandez De Moya, E.; Moratal, D. (2017). A radiomics evaluation of 2D and 3D MRI texture features to classify brain metastases from lung cancer and melanoma. Proceedings Intenational Anual Conference of IEEE Engineering in Medicine and Biology Society. 493-496. doi:10.1109/EMBC.2017.8036869 es_ES
dc.description.accrualMethod S es_ES
dc.relation.conferencename 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2017) es_ES
dc.relation.conferencedate Julio 11-15,2017 es_ES
dc.relation.conferenceplace Jeju Island, South Korea es_ES
dc.relation.publisherversion http://doi.org/10.1109/EMBC.2017.8036869 es_ES
dc.description.upvformatpinicio 493 es_ES
dc.description.upvformatpfin 496 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.identifier.pmid 29059917
dc.relation.pasarela S\342923 es_ES
dc.contributor.funder Ministerio de Economía, Industria y Competitividad es_ES


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