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Identification of the presence of ischaemic stroke lesions by means of texture analysis on brain magnetic resonance images

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Identification of the presence of ischaemic stroke lesions by means of texture analysis on brain magnetic resonance images

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dc.contributor.author Ortiz-Ramón, Rafael es_ES
dc.contributor.author Valdés Hernández, Maria del C. es_ES
dc.contributor.author González-Castro, Victor es_ES
dc.contributor.author Makin, Stephen es_ES
dc.contributor.author Armitage, Paul A. es_ES
dc.contributor.author Aribisala, Benjamin S. es_ES
dc.contributor.author Bastin, Mark E. es_ES
dc.contributor.author Deary, Ian J. es_ES
dc.contributor.author Wardlaw, Joanna M. es_ES
dc.contributor.author Moratal, David es_ES
dc.date.accessioned 2020-11-20T04:31:16Z
dc.date.available 2020-11-20T04:31:16Z
dc.date.issued 2019-06 es_ES
dc.identifier.issn 0895-6111 es_ES
dc.identifier.uri http://hdl.handle.net/10251/155397
dc.description.abstract [EN] Background: The differential quantification of brain atrophy, white matter hyperintensities (WMH) and stroke lesions is important in studies of stroke and dementia. However, the presence of stroke lesions is usually overlooked by automatic neuroimage processing methods and the-state-of-the-art deep learning schemes, which lack sufficient annotated data. We explore the use of radiomics in identifying whether a brain magnetic resonance imaging (MRI) scan belongs to an individual that had a stroke or not. Materials and methods: We used 1800 3D sets of MRI data from three prospective studies: one of stroke mechanisms and two of cognitive ageing, evaluated 114 textural features in WMH, cerebrospinal fluid, deep grey and normal-appearing white matter, and attempted to classify the scans using a random forest and support vector machine classifiers with and without feature selection. We evaluated the discriminatory power of each feature independently in each population and corrected the result against Type 1 errors. We also evaluated the influence of clinical parameters in the classification results. Results: Subtypes of ischaemic strokes (i.e. lacunar vs. cortical) cannot be discerned using radiomics, but the presence of a stroke-type lesion can be ascertained with accuracies ranging from 0.7 < AUC < 0.83. Feature selection, tissue type, stroke subtype and MRI sequence did not seem to determine the classification results. From all clinical variables evaluated, age correlated with the proportion of images classified correctly using either different or the same descriptors (Pearson r = 0.31 and 0.39 respectively, p < 0.001). Conclusions: Texture features in conventionally automatically segmented tissues may help in the identification of the presence of previous stroke lesions on an MRI scan, and should be taken into account in transfer learning strategies of the-state-of-the-art deep learning schemes. es_ES
dc.description.sponsorship This work was funded by the Row Fogo Charitable Trust (MVH, VGC) grant no. BRO-D.FID3668413, and the Wellcome Trust (patient recruitment, scanning, primary study Ref No. 088134/Z/09). The study was conducted independently of the funders who do not hold the data and did not participate in the study design or analyses. The Lothian Birth Cohort 1936 is funded by Age UK (Disconnected Mind grant) and the Medical Research Council (MRC; MR/M01311/1, G1001245, 82800), and the latter supported BSA. IJD was supported by the Centre for Cognitive Ageing and Cognitive Epidemiology, which is funded by the MRC and the Biotechnology and Biological Sciences Research Council (MR/K026992/1). David Moratal acknowledges financial support from the Spanish Ministerio de Economia y Competitividad (MINECO) and FEDER funds under Grant BFU2015-64380-C2-2-R, and from the Conselleria d'Educacio, Investigacio, Cultura i Esport, Generalitat Valenciana (grants AEST/2017/013 and AEST/2018/021). Rafael Ortiz-Ramon was supported by grant ACIF/2015/078 and grant BEFPI/2017/004 from the Conselleria d'Educacio, Investigacio, Cultura i Esport of the Valencian Community (Spain). We thank participants in the studies that provided data for this study, the radiographers and staff at the Brain Research Imaging Centre Edinburgh (http://www.bric.ed.ac.uk/), a SINAPSE (Scottish Imaging Network A Platform for Scientific Excellence, www. sinapse.ac.uk/) collaboration centre. We especially thank the following members of the research teams, who contributed to the recruitment, data collection or image processing in the primary studies, but did not participate in the analysis or writing of this report: Dr. Susana Munoz Maniega, Dr. Natalie Royle, Mrs. Eleni Sakka, Miss. Catherine Murray, Miss Kirsten Shuler. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Computerized Medical Imaging and Graphics es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Texture analysis es_ES
dc.subject Radiomics es_ES
dc.subject Stroke es_ES
dc.subject Small vessel disease es_ES
dc.subject White matter hyperintensities es_ES
dc.subject.classification TECNOLOGIA ELECTRONICA es_ES
dc.title Identification of the presence of ischaemic stroke lesions by means of texture analysis on brain magnetic resonance images es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.compmedimag.2019.02.006 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MRC//82800/GB es_ES
dc.relation.projectID info:eu-repo/grantAgreement/WT/Neuroscience and Mental Health/088134/Blood brain barrier dysfunction and cerebral small vessel disease/
dc.relation.projectID info:eu-repo/grantAgreement/UKRI//G1001245/GB/Brain white matter imaging and cognitive ageing in the Lothian Birth Cohort of 1936: II/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GVA//AEST%2F2018%2F021/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/UKRI//MR%2FM01311%2F1/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/Row Fogo Charitable Trust//BRO-D.FID3668413/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/UKRI//MR%2FK026992%2F1/GB/Centre for Cognitive Ageing & Cognitive Epidemiology/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GVA//BEFPI%2F2017%2F004/ 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.relation.projectID info:eu-repo/grantAgreement/GVA//ACIF%2F2015%2F078/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GVA//AEST%2F2017%2F013/ 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.; Valdés Hernández, MDC.; González-Castro, V.; Makin, S.; Armitage, PA.; Aribisala, BS.; Bastin, ME.... (2019). Identification of the presence of ischaemic stroke lesions by means of texture analysis on brain magnetic resonance images. Computerized Medical Imaging and Graphics. 74:12-24. https://doi.org/10.1016/j.compmedimag.2019.02.006 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.compmedimag.2019.02.006 es_ES
dc.description.upvformatpinicio 12 es_ES
dc.description.upvformatpfin 24 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 74 es_ES
dc.identifier.pmid 30921550 es_ES
dc.identifier.pmcid PMC6553681 es_ES
dc.relation.pasarela S\405801 es_ES
dc.contributor.funder Age UK es_ES
dc.contributor.funder UK Research and Innovation es_ES
dc.contributor.funder Wellcome Trust es_ES
dc.contributor.funder Generalitat Valenciana es_ES
dc.contributor.funder Row Fogo Charitable Trust es_ES
dc.contributor.funder European Regional Development Fund es_ES
dc.contributor.funder Medical Research Council, Reino Unido es_ES
dc.contributor.funder Biotechnology and Biological Sciences Research Council, Reino Unido es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES
dc.subject.ods 03.- Garantizar una vida saludable y promover el bienestar para todos y todas en todas las edades es_ES


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