New imaging signatures of cardiac alterations in ischaemic heart disease and cerebrovascular disease using CMR radiomics

Background: Ischaemic heart disease (IHD) and cerebrovascular disease are two closely inter-related clinical entities. Cardiovascular magnetic resonance (CMR) radiomics may capture subtle cardiac changes associated with these two diseases providing new insights into the brain-heart interactions. Obj...

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Autores: Rauseo, Elisa, Izquierdo Morcillo, Cristian, Raisi-Estabragh, Zahra, Gkontra, Polyxeni, Aung, Nay, Lekadir, Karim, 1977-, Petersen, Steffen E.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2445/227637
Acceso en línea:https://hdl.handle.net/2445/227637
Access Level:acceso abierto
Palabra clave:Imatges per ressonància magnètica
Malalties coronàries
Aprenentatge automàtic
Magnetic resonance imaging
Coronary diseases
Machine learning
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spelling New imaging signatures of cardiac alterations in ischaemic heart disease and cerebrovascular disease using CMR radiomicsRauseo, ElisaIzquierdo Morcillo, CristianRaisi-Estabragh, ZahraGkontra, PolyxeniAung, NayLekadir, Karim, 1977-Petersen, Steffen E.Imatges per ressonància magnèticaMalalties coronàriesAprenentatge automàticMagnetic resonance imagingCoronary diseasesMachine learningBackground: Ischaemic heart disease (IHD) and cerebrovascular disease are two closely inter-related clinical entities. Cardiovascular magnetic resonance (CMR) radiomics may capture subtle cardiac changes associated with these two diseases providing new insights into the brain-heart interactions. Objective: To define the CMR radiomics signatures for IHD and cerebrovascular disease and study their incremental value for disease discrimination over conventional CMR indices. Methods: We analysed CMR images of UK Biobank's subjects with pre-existing IHD, ischaemic cerebrovascular disease, myocardial infarction (MI), and ischaemic stroke (IS) (n = 779, 267, 525, and 107, respectively). Each disease group was compared with an equal number of healthy controls. We extracted 446 shape, first-order, and texture radiomics features from three regions of interest (right ventricle, left ventricle, and left ventricular myocardium) in end-diastole and end-systole defined from segmentation of short-axis cine images. Systematic feature selection combined with machine learning (ML) algorithms (support vector machine and random forest) and 10-fold cross-validation tests were used to build the radiomics signature for each condition. We compared the discriminatory power achieved by the radiomics signature with conventional indices for each disease group, using the area under the curve (AUC), receiver operating characteristic (ROC) analysis, and paired t-test for statistical significance. A third model combining both radiomics and conventional indices was also evaluated. Results: In all the study groups, radiomics signatures provided a significantly better disease discrimination than conventional indices, as suggested by AUC (IHD:0.82 vs. 0.75; cerebrovascular disease: 0.79 vs. 0.77; MI: 0.87 vs. 0.79, and IS: 0.81 vs. 0.72). Similar results were observed with the combined models. In IHD and MI, LV shape radiomics were dominant. However, in IS and cerebrovascular disease, the combination of shape and intensity-based features improved the disease discrimination. A notable overlap of the radiomics signatures of IHD and cerebrovascular disease was also found. Conclusions: This study demonstrates the potential value of CMR radiomics over conventional indices in detecting subtle cardiac changes associated with chronic ischaemic processes involving the brain and heart, even in the presence of more heterogeneous clinical pictures. Radiomics analysis might also improve our understanding of the complex mechanisms behind the brain-heart interactions during ischaemia.Frontiers Media2026202620212026info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion16 p.application/pdfhttps://hdl.handle.net/2445/227637Articles publicats en revistes (Matemàtiques i Informàtica)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésReproducció del document publicat a: https://doi.org/10.3389/fcvm.2021.716577Frontiers in Cardiovascular Medicine, 2021, vol. 8, num.1https://doi.org/10.3389/fcvm.2021.716577cc-by (c) Rauseo, E. et al., 2021http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:2445/2276372026-05-29T05:05:01Z
dc.title.none.fl_str_mv New imaging signatures of cardiac alterations in ischaemic heart disease and cerebrovascular disease using CMR radiomics
title New imaging signatures of cardiac alterations in ischaemic heart disease and cerebrovascular disease using CMR radiomics
spellingShingle New imaging signatures of cardiac alterations in ischaemic heart disease and cerebrovascular disease using CMR radiomics
Rauseo, Elisa
Imatges per ressonància magnètica
Malalties coronàries
Aprenentatge automàtic
Magnetic resonance imaging
Coronary diseases
Machine learning
title_short New imaging signatures of cardiac alterations in ischaemic heart disease and cerebrovascular disease using CMR radiomics
title_full New imaging signatures of cardiac alterations in ischaemic heart disease and cerebrovascular disease using CMR radiomics
title_fullStr New imaging signatures of cardiac alterations in ischaemic heart disease and cerebrovascular disease using CMR radiomics
title_full_unstemmed New imaging signatures of cardiac alterations in ischaemic heart disease and cerebrovascular disease using CMR radiomics
title_sort New imaging signatures of cardiac alterations in ischaemic heart disease and cerebrovascular disease using CMR radiomics
dc.creator.none.fl_str_mv Rauseo, Elisa
Izquierdo Morcillo, Cristian
Raisi-Estabragh, Zahra
Gkontra, Polyxeni
Aung, Nay
Lekadir, Karim, 1977-
Petersen, Steffen E.
author Rauseo, Elisa
author_facet Rauseo, Elisa
Izquierdo Morcillo, Cristian
Raisi-Estabragh, Zahra
Gkontra, Polyxeni
Aung, Nay
Lekadir, Karim, 1977-
Petersen, Steffen E.
author_role author
author2 Izquierdo Morcillo, Cristian
Raisi-Estabragh, Zahra
Gkontra, Polyxeni
Aung, Nay
Lekadir, Karim, 1977-
Petersen, Steffen E.
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Imatges per ressonància magnètica
Malalties coronàries
Aprenentatge automàtic
Magnetic resonance imaging
Coronary diseases
Machine learning
topic Imatges per ressonància magnètica
Malalties coronàries
Aprenentatge automàtic
Magnetic resonance imaging
Coronary diseases
Machine learning
description Background: Ischaemic heart disease (IHD) and cerebrovascular disease are two closely inter-related clinical entities. Cardiovascular magnetic resonance (CMR) radiomics may capture subtle cardiac changes associated with these two diseases providing new insights into the brain-heart interactions. Objective: To define the CMR radiomics signatures for IHD and cerebrovascular disease and study their incremental value for disease discrimination over conventional CMR indices. Methods: We analysed CMR images of UK Biobank's subjects with pre-existing IHD, ischaemic cerebrovascular disease, myocardial infarction (MI), and ischaemic stroke (IS) (n = 779, 267, 525, and 107, respectively). Each disease group was compared with an equal number of healthy controls. We extracted 446 shape, first-order, and texture radiomics features from three regions of interest (right ventricle, left ventricle, and left ventricular myocardium) in end-diastole and end-systole defined from segmentation of short-axis cine images. Systematic feature selection combined with machine learning (ML) algorithms (support vector machine and random forest) and 10-fold cross-validation tests were used to build the radiomics signature for each condition. We compared the discriminatory power achieved by the radiomics signature with conventional indices for each disease group, using the area under the curve (AUC), receiver operating characteristic (ROC) analysis, and paired t-test for statistical significance. A third model combining both radiomics and conventional indices was also evaluated. Results: In all the study groups, radiomics signatures provided a significantly better disease discrimination than conventional indices, as suggested by AUC (IHD:0.82 vs. 0.75; cerebrovascular disease: 0.79 vs. 0.77; MI: 0.87 vs. 0.79, and IS: 0.81 vs. 0.72). Similar results were observed with the combined models. In IHD and MI, LV shape radiomics were dominant. However, in IS and cerebrovascular disease, the combination of shape and intensity-based features improved the disease discrimination. A notable overlap of the radiomics signatures of IHD and cerebrovascular disease was also found. Conclusions: This study demonstrates the potential value of CMR radiomics over conventional indices in detecting subtle cardiac changes associated with chronic ischaemic processes involving the brain and heart, even in the presence of more heterogeneous clinical pictures. Radiomics analysis might also improve our understanding of the complex mechanisms behind the brain-heart interactions during ischaemia.
publishDate 2021
dc.date.none.fl_str_mv 2021
2026
2026
2026
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/227637
url https://hdl.handle.net/2445/227637
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.3389/fcvm.2021.716577
Frontiers in Cardiovascular Medicine, 2021, vol. 8, num.1
https://doi.org/10.3389/fcvm.2021.716577
dc.rights.none.fl_str_mv cc-by (c) Rauseo, E. et al., 2021
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by (c) Rauseo, E. et al., 2021
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 16 p.
application/pdf
dc.publisher.none.fl_str_mv Frontiers Media
publisher.none.fl_str_mv Frontiers Media
dc.source.none.fl_str_mv Articles publicats en revistes (Matemàtiques i Informàtica)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,81155

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