Repeatability of cardiac magnetic resonance radiomics: a multi-centre multi-vendor test-retest study

Aims: To evaluate the repeatability of cardiac magnetic resonance (CMR) radiomics features on test-retest scanning using a multi-centre multi-vendor dataset with a varied case-mix. Methods and Results: The sample included 54 test-retest studies from the VOLUMES resource (thevolumesresource.com). Ima...

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Autores: Raisi-Estabragh, Zahra, Gkontra, Polyxeni, Jaggi, Akshay, Cooper, Jackie, Augusto, João, Bhuva, Anish N., Davies, Rhodri H., Manisty, Charlotte H., Moon, James C., Munroe, Patricia B., Harvey, Nicholas C., Lekadir, Karim, 1977-, Petersen, Steffen E.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2020
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:2445/193924
Acceso en línea:https://hdl.handle.net/2445/193924
Access Level:acceso abierto
Palabra clave:Imatges per ressonància magnètica
Dades massives
Algorismes computacionals
Magnetic resonance imaging
Big data
Computer algorithms
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spelling Repeatability of cardiac magnetic resonance radiomics: a multi-centre multi-vendor test-retest studyRaisi-Estabragh, ZahraGkontra, PolyxeniJaggi, AkshayCooper, JackieAugusto, JoãoBhuva, Anish N.Davies, Rhodri H.Manisty, Charlotte H.Moon, James C.Munroe, Patricia B.Harvey, Nicholas C.Lekadir, Karim, 1977-Petersen, Steffen E.Imatges per ressonància magnèticaDades massivesAlgorismes computacionalsMagnetic resonance imagingBig dataComputer algorithmsAims: To evaluate the repeatability of cardiac magnetic resonance (CMR) radiomics features on test-retest scanning using a multi-centre multi-vendor dataset with a varied case-mix. Methods and Results: The sample included 54 test-retest studies from the VOLUMES resource (thevolumesresource.com). Images were segmented according to a pre-defined protocol to select three regions of interest (ROI) in end-diastole and end-systole: right ventricle, left ventricle (LV), and LV myocardium. We extracted radiomics shape features from all three ROIs and, additionally, first-order and texture features from the LV myocardium. Overall, 280 features were derived per study. For each feature, we calculated intra-class correlation coefficient (ICC), within-subject coefficient of variation, and mean relative difference. We ranked robustness of features according to mean ICC stratified by feature category, ROI, and cardiac phase, demonstrating a wide range of repeatability. There were features with good and excellent repeatability (ICC ≥ 0.75) within all feature categories and ROIs. A high proportion of first-order and texture features had excellent repeatability (ICC ≥ 0.90), however, these categories also contained features with the poorest repeatability (ICC < 0.50). Conclusion: CMR radiomic features have a wide range of repeatability. This paper is intended as a reference for future researchers to guide selection of the most robust features for clinical CMR radiomics models. Further work in larger and richer datasets is needed to further define the technical performance and clinical utility of CMR radiomics.Frontiers Media2023202320202023info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2445/193924Articles 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.2020.586236Frontiers in Cardiovascular Medicine, 2020, vol. 7https://doi.org/10.3389/fcvm.2020.586236cc-by (c) Raisi-Estabragh, Zahra et al., 2020https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:2445/1939242026-05-29T05:05:01Z
dc.title.none.fl_str_mv Repeatability of cardiac magnetic resonance radiomics: a multi-centre multi-vendor test-retest study
title Repeatability of cardiac magnetic resonance radiomics: a multi-centre multi-vendor test-retest study
spellingShingle Repeatability of cardiac magnetic resonance radiomics: a multi-centre multi-vendor test-retest study
Raisi-Estabragh, Zahra
Imatges per ressonància magnètica
Dades massives
Algorismes computacionals
Magnetic resonance imaging
Big data
Computer algorithms
title_short Repeatability of cardiac magnetic resonance radiomics: a multi-centre multi-vendor test-retest study
title_full Repeatability of cardiac magnetic resonance radiomics: a multi-centre multi-vendor test-retest study
title_fullStr Repeatability of cardiac magnetic resonance radiomics: a multi-centre multi-vendor test-retest study
title_full_unstemmed Repeatability of cardiac magnetic resonance radiomics: a multi-centre multi-vendor test-retest study
title_sort Repeatability of cardiac magnetic resonance radiomics: a multi-centre multi-vendor test-retest study
dc.creator.none.fl_str_mv Raisi-Estabragh, Zahra
Gkontra, Polyxeni
Jaggi, Akshay
Cooper, Jackie
Augusto, João
Bhuva, Anish N.
Davies, Rhodri H.
Manisty, Charlotte H.
Moon, James C.
Munroe, Patricia B.
Harvey, Nicholas C.
Lekadir, Karim, 1977-
Petersen, Steffen E.
author Raisi-Estabragh, Zahra
author_facet Raisi-Estabragh, Zahra
Gkontra, Polyxeni
Jaggi, Akshay
Cooper, Jackie
Augusto, João
Bhuva, Anish N.
Davies, Rhodri H.
Manisty, Charlotte H.
Moon, James C.
Munroe, Patricia B.
Harvey, Nicholas C.
Lekadir, Karim, 1977-
Petersen, Steffen E.
author_role author
author2 Gkontra, Polyxeni
Jaggi, Akshay
Cooper, Jackie
Augusto, João
Bhuva, Anish N.
Davies, Rhodri H.
Manisty, Charlotte H.
Moon, James C.
Munroe, Patricia B.
Harvey, Nicholas C.
Lekadir, Karim, 1977-
Petersen, Steffen E.
author2_role author
author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Imatges per ressonància magnètica
Dades massives
Algorismes computacionals
Magnetic resonance imaging
Big data
Computer algorithms
topic Imatges per ressonància magnètica
Dades massives
Algorismes computacionals
Magnetic resonance imaging
Big data
Computer algorithms
description Aims: To evaluate the repeatability of cardiac magnetic resonance (CMR) radiomics features on test-retest scanning using a multi-centre multi-vendor dataset with a varied case-mix. Methods and Results: The sample included 54 test-retest studies from the VOLUMES resource (thevolumesresource.com). Images were segmented according to a pre-defined protocol to select three regions of interest (ROI) in end-diastole and end-systole: right ventricle, left ventricle (LV), and LV myocardium. We extracted radiomics shape features from all three ROIs and, additionally, first-order and texture features from the LV myocardium. Overall, 280 features were derived per study. For each feature, we calculated intra-class correlation coefficient (ICC), within-subject coefficient of variation, and mean relative difference. We ranked robustness of features according to mean ICC stratified by feature category, ROI, and cardiac phase, demonstrating a wide range of repeatability. There were features with good and excellent repeatability (ICC ≥ 0.75) within all feature categories and ROIs. A high proportion of first-order and texture features had excellent repeatability (ICC ≥ 0.90), however, these categories also contained features with the poorest repeatability (ICC < 0.50). Conclusion: CMR radiomic features have a wide range of repeatability. This paper is intended as a reference for future researchers to guide selection of the most robust features for clinical CMR radiomics models. Further work in larger and richer datasets is needed to further define the technical performance and clinical utility of CMR radiomics.
publishDate 2020
dc.date.none.fl_str_mv 2020
2023
2023
2023
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/2445/193924
url https://hdl.handle.net/2445/193924
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.2020.586236
Frontiers in Cardiovascular Medicine, 2020, vol. 7
https://doi.org/10.3389/fcvm.2020.586236
dc.rights.none.fl_str_mv cc-by (c) Raisi-Estabragh, Zahra et al., 2020
https://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by (c) Raisi-Estabragh, Zahra et al., 2020
https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 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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