Cardiac magnetic resonance radiomics reveal differential impact of sex, age, and vascular risk factors on cardiac structure and myocardial tissue

Background: Cardiovascular magnetic resonance (CMR) radiomics analysis provides multiple quantifiers of ventricular shape and myocardial texture, which may be used for detailed cardiovascular phenotyping. Objectives: We studied variation in CMR radiomics phenotypes by age and sex in healthy UK Bioba...

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Autores: Raisi-Estabragh, Zahra, Jaggi, Akshay, Gkontra, Polyxeni, McCracken, Celeste, Aung, Nay, Munroe, Patricia B., Neubauer, Stefan, Harvey, Nicholas C., 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:2445/227634
Acceso en línea:https://hdl.handle.net/2445/227634
Access Level:acceso abierto
Palabra clave:Imatges per ressonància magnètica
Visió per ordinador
Aprenentatge automàtic
Magnetic resonance imaging
Computer vision
Machine learning
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spelling Cardiac magnetic resonance radiomics reveal differential impact of sex, age, and vascular risk factors on cardiac structure and myocardial tissueRaisi-Estabragh, ZahraJaggi, AkshayGkontra, PolyxeniMcCracken, CelesteAung, NayMunroe, Patricia B.Neubauer, StefanHarvey, Nicholas C.Lekadir, Karim, 1977-Petersen, Steffen E.Imatges per ressonància magnèticaVisió per ordinadorAprenentatge automàticMagnetic resonance imagingComputer visionMachine learningBackground: Cardiovascular magnetic resonance (CMR) radiomics analysis provides multiple quantifiers of ventricular shape and myocardial texture, which may be used for detailed cardiovascular phenotyping. Objectives: We studied variation in CMR radiomics phenotypes by age and sex in healthy UK Biobank participants. Then, we examined independent associations of classical vascular risk factors (VRFs: smoking, diabetes, hypertension, high cholesterol) with CMR radiomics features, considering potential sex and age differential relationships. Design: Image acquisition was with 1.5 Tesla scanners (MAGNETOM Aera, Siemens). Three regions of interest were segmented from short axis stack images using an automated pipeline: right ventricle, left ventricle, myocardium. We extracted 237 radiomics features from each study using Pyradiomics. In a healthy subset of participants (n = 14,902) without cardiovascular disease or VRFs, we estimated independent associations of age and sex with each radiomics feature using linear regression models adjusted for body size. We then created a sample comprising individuals with at least one VRF matched to an equal number of healthy participants (n = 27,400). We linearly modelled each radiomics feature against age, sex, body size, and all the VRFs. Bonferroni adjustment for multiple testing was applied to all p-values. To aid interpretation, we organised the results into six feature clusters. Results: Amongst the healthy subset, men had larger ventricles with dimmer and less texturally complex myocardium than women. Increasing age was associated with smaller ventricles and greater variation in myocardial intensities. Broadly, all the VRFs were associated with dimmer, less varied signal intensities, greater uniformity of local intensity levels, and greater relative presence of low signal intensity areas within the myocardium. Diabetes and high cholesterol were also associated with smaller ventricular size, this association was of greater magnitude in men than women. The pattern of alteration of radiomics features with the VRFs was broadly consistent in men and women. However, the associations between intensity based radiomics features with both diabetes and hypertension were more prominent in women than men. Conclusions: We demonstrate novel independent associations of sex, age, and major VRFs with CMR radiomics phenotypes. Further studies into the nature and clinical significance of these phenotypes are needed.Frontiers Media2026202620212026info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion15 p.application/pdfhttps://hdl.handle.net/2445/227634Articles 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.2021.763361Frontiers in Cardiovascular Medicine, 2021, vol. 8, p. 1972https://doi.org/10.3389/fcvm.2021.763361cc-by (c) Raisi-Estabragh, Z. et al., 2021http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:2445/2276342026-05-29T05:05:01Z
dc.title.none.fl_str_mv Cardiac magnetic resonance radiomics reveal differential impact of sex, age, and vascular risk factors on cardiac structure and myocardial tissue
title Cardiac magnetic resonance radiomics reveal differential impact of sex, age, and vascular risk factors on cardiac structure and myocardial tissue
spellingShingle Cardiac magnetic resonance radiomics reveal differential impact of sex, age, and vascular risk factors on cardiac structure and myocardial tissue
Raisi-Estabragh, Zahra
Imatges per ressonància magnètica
Visió per ordinador
Aprenentatge automàtic
Magnetic resonance imaging
Computer vision
Machine learning
title_short Cardiac magnetic resonance radiomics reveal differential impact of sex, age, and vascular risk factors on cardiac structure and myocardial tissue
title_full Cardiac magnetic resonance radiomics reveal differential impact of sex, age, and vascular risk factors on cardiac structure and myocardial tissue
title_fullStr Cardiac magnetic resonance radiomics reveal differential impact of sex, age, and vascular risk factors on cardiac structure and myocardial tissue
title_full_unstemmed Cardiac magnetic resonance radiomics reveal differential impact of sex, age, and vascular risk factors on cardiac structure and myocardial tissue
title_sort Cardiac magnetic resonance radiomics reveal differential impact of sex, age, and vascular risk factors on cardiac structure and myocardial tissue
dc.creator.none.fl_str_mv Raisi-Estabragh, Zahra
Jaggi, Akshay
Gkontra, Polyxeni
McCracken, Celeste
Aung, Nay
Munroe, Patricia B.
Neubauer, Stefan
Harvey, Nicholas C.
Lekadir, Karim, 1977-
Petersen, Steffen E.
author Raisi-Estabragh, Zahra
author_facet Raisi-Estabragh, Zahra
Jaggi, Akshay
Gkontra, Polyxeni
McCracken, Celeste
Aung, Nay
Munroe, Patricia B.
Neubauer, Stefan
Harvey, Nicholas C.
Lekadir, Karim, 1977-
Petersen, Steffen E.
author_role author
author2 Jaggi, Akshay
Gkontra, Polyxeni
McCracken, Celeste
Aung, Nay
Munroe, Patricia B.
Neubauer, Stefan
Harvey, Nicholas C.
Lekadir, Karim, 1977-
Petersen, Steffen E.
author2_role author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Imatges per ressonància magnètica
Visió per ordinador
Aprenentatge automàtic
Magnetic resonance imaging
Computer vision
Machine learning
topic Imatges per ressonància magnètica
Visió per ordinador
Aprenentatge automàtic
Magnetic resonance imaging
Computer vision
Machine learning
description Background: Cardiovascular magnetic resonance (CMR) radiomics analysis provides multiple quantifiers of ventricular shape and myocardial texture, which may be used for detailed cardiovascular phenotyping. Objectives: We studied variation in CMR radiomics phenotypes by age and sex in healthy UK Biobank participants. Then, we examined independent associations of classical vascular risk factors (VRFs: smoking, diabetes, hypertension, high cholesterol) with CMR radiomics features, considering potential sex and age differential relationships. Design: Image acquisition was with 1.5 Tesla scanners (MAGNETOM Aera, Siemens). Three regions of interest were segmented from short axis stack images using an automated pipeline: right ventricle, left ventricle, myocardium. We extracted 237 radiomics features from each study using Pyradiomics. In a healthy subset of participants (n = 14,902) without cardiovascular disease or VRFs, we estimated independent associations of age and sex with each radiomics feature using linear regression models adjusted for body size. We then created a sample comprising individuals with at least one VRF matched to an equal number of healthy participants (n = 27,400). We linearly modelled each radiomics feature against age, sex, body size, and all the VRFs. Bonferroni adjustment for multiple testing was applied to all p-values. To aid interpretation, we organised the results into six feature clusters. Results: Amongst the healthy subset, men had larger ventricles with dimmer and less texturally complex myocardium than women. Increasing age was associated with smaller ventricles and greater variation in myocardial intensities. Broadly, all the VRFs were associated with dimmer, less varied signal intensities, greater uniformity of local intensity levels, and greater relative presence of low signal intensity areas within the myocardium. Diabetes and high cholesterol were also associated with smaller ventricular size, this association was of greater magnitude in men than women. The pattern of alteration of radiomics features with the VRFs was broadly consistent in men and women. However, the associations between intensity based radiomics features with both diabetes and hypertension were more prominent in women than men. Conclusions: We demonstrate novel independent associations of sex, age, and major VRFs with CMR radiomics phenotypes. Further studies into the nature and clinical significance of these phenotypes are needed.
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/2445/227634
url https://hdl.handle.net/2445/227634
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.2021.763361
Frontiers in Cardiovascular Medicine, 2021, vol. 8, p. 1972
https://doi.org/10.3389/fcvm.2021.763361
dc.rights.none.fl_str_mv cc-by (c) Raisi-Estabragh, Z. et al., 2021
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by (c) Raisi-Estabragh, Z. et al., 2021
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 15 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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