Radiomics-Derived Brain Age Predicts Functional Outcome After Acute Ischemic Stroke.

Bretzner, M; Bonkhoff, AK; Schirmer, MD; Hong, S; Dalca, A; Donahue, K; Giese, A-K; Etherton, MR; Rist, PM; Nardin, M; Regenhardt, RW; Leclerc, X; Lopes, R; Gautherot, M; Wang, C; Benavente, OR; Cole, JW; Donatti, A; Griessenauer, C; Heitsch, L; Holmegaard, L; Jood, K; Jimenez-Conde, J; Kittner, SJ; Lemmens, R; Levi, CR; McArdle, PF; McDonough, CW; Meschia, JF; Phuah, C-L; Rolfs, A; Ropele, S; Rosand, J; Roquer, J; Rundek, T; Sacco, RL; Schmidt, R; Sharma, P; Slowik, A; Sousa, A; Stanne, TM; Strbian, D; Tatlisumak, T; Thijs, V; Vagal, A; Wasselius, J; Woo, D; Wu, O; Zand, R; Worrall, BB; Maguire, J; Lindgren, AG; Jern, C; Golland, P; Kuchcinski, G; Rost, NS

Radiomics-Derived Brain Age Predicts Functional Outcome After Acute Ischemic Stroke.

Bretzner, M Bonkhoff, AK Schirmer, MD Hong, S Dalca, A Donahue, K Giese, A-K Etherton, MR Rist, PM Nardin, M Regenhardt, RW Leclerc, X Lopes, R Gautherot, M Wang, C Benavente, OR Cole, JW Donatti, A Griessenauer, C Heitsch, L Holmegaard, L Jood, K Jimenez-Conde, J Kittner, SJ Lemmens, R Levi, CR McArdle, PF McDonough, CW Meschia, JF Phuah, C-L Rolfs, A Ropele, S Rosand, J Roquer, J Rundek, T Sacco, RL Schmidt, R Sharma, P Slowik, A Sousa, A Stanne, TM Strbian, D Tatlisumak, T Thijs, V Vagal, A Wasselius, J Woo, D Wu, O Zand, R Worrall, BB Maguire, J

Lindgren, AG Jern, C Golland, P Kuchcinski, G Rost, NS

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Publisher:: LIPPINCOTT WILLIAMS & WILKINS
Publication Type:: Journal Article
Citation:: Neurology, 2023, 100, (8), pp. e822-e833
Issue Date:: 2023

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Field	Value	Language
dc.contributor.author	Bretzner, M
dc.contributor.author	Bonkhoff, AK
dc.contributor.author	Schirmer, MD
dc.contributor.author	Hong, S
dc.contributor.author	Dalca, A
dc.contributor.author	Donahue, K
dc.contributor.author	Giese, A-K
dc.contributor.author	Etherton, MR
dc.contributor.author	Rist, PM
dc.contributor.author	Nardin, M
dc.contributor.author	Regenhardt, RW
dc.contributor.author	Leclerc, X
dc.contributor.author	Lopes, R
dc.contributor.author	Gautherot, M
dc.contributor.author	Wang, C
dc.contributor.author	Benavente, OR
dc.contributor.author	Cole, JW
dc.contributor.author	Donatti, A
dc.contributor.author	Griessenauer, C
dc.contributor.author	Heitsch, L
dc.contributor.author	Holmegaard, L
dc.contributor.author	Jood, K
dc.contributor.author	Jimenez-Conde, J
dc.contributor.author	Kittner, SJ
dc.contributor.author	Lemmens, R
dc.contributor.author	Levi, CR
dc.contributor.author	McArdle, PF
dc.contributor.author	McDonough, CW
dc.contributor.author	Meschia, JF
dc.contributor.author	Phuah, C-L
dc.contributor.author	Rolfs, A
dc.contributor.author	Ropele, S
dc.contributor.author	Rosand, J
dc.contributor.author	Roquer, J
dc.contributor.author	Rundek, T
dc.contributor.author	Sacco, RL
dc.contributor.author	Schmidt, R
dc.contributor.author	Sharma, P
dc.contributor.author	Slowik, A
dc.contributor.author	Sousa, A
dc.contributor.author	Stanne, TM
dc.contributor.author	Strbian, D
dc.contributor.author	Tatlisumak, T
dc.contributor.author	Thijs, V
dc.contributor.author	Vagal, A
dc.contributor.author	Wasselius, J
dc.contributor.author	Woo, D
dc.contributor.author	Wu, O
dc.contributor.author	Zand, R
dc.contributor.author	Worrall, BB
dc.contributor.author	Maguire, J https://orcid.org/0000-0001-5722-8311
dc.contributor.author	Lindgren, AG
dc.contributor.author	Jern, C
dc.contributor.author	Golland, P
dc.contributor.author	Kuchcinski, G
dc.contributor.author	Rost, NS
dc.date.accessioned	2024-01-25T05:32:06Z
dc.date.available	2022-10-06
dc.date.available	2024-01-25T05:32:06Z
dc.date.issued	2023
dc.identifier.citation	Neurology, 2023, 100, (8), pp. e822-e833
dc.identifier.issn	0028-3878
dc.identifier.issn	1526-632X
dc.identifier.uri	http://hdl.handle.net/10453/174947
dc.description.abstract	BACKGROUND AND OBJECTIVES: While chronological age is one of the most influential determinants of post-stroke outcomes, little is known of the impact of neuroimaging-derived biological "brain age". We hypothesized that radiomics analyses of T2-FLAIR images texture would provide brain age estimates and that advanced brain age of stroke patients will be associated with cardiovascular risk factors and worse functional outcomes. METHODS: We extracted radiomics from T2-FLAIR images acquired during acute stroke clinical evaluation. Brain age was determined from brain parenchyma radiomics using an ElasticNet linear regression model. Subsequently, relative brain age (RBA), which expresses brain age in comparison to chronological age-matched peers, was estimated. Finally, we built a linear regression model of RBA using clinical cardiovascular characteristics as inputs, and a logistic regression model of favorable functional outcomes taking RBA as input. RESULTS: We reviewed 4,163 patients from a large multisite ischemic stroke cohort (mean age=62.8 years, 42.0% females). T2-FLAIR radiomics predicted chronological ages (mean absolute error=6.9 years, r=0.81). After adjustment for covariates, RBA was higher and therefore described older-appearing brains in patients with hypertension, diabetes mellitus, a history of smoking, and a history of a prior stroke. In multivariate analyses, age, RBA, NIHSS, and a history of prior stroke were all significantly associated with functional outcome (respective adjusted Odds-Ratios: 0.58, 0.76, 0.48, 0.55; all p-values<0.001). Moreover, the negative effect of RBA on outcome was especially pronounced in minor strokes. DISCUSSION: T2-FLAIR radiomics can be used to predict brain age and derive RBA. Older appearing brains, characterized by a higher RBA, reflect cardiovascular risk factor accumulation and are linked to worse outcomes after stroke.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	LIPPINCOTT WILLIAMS & WILKINS
dc.relation.ispartof	Neurology
dc.relation.isbasedon	10.1212/WNL.0000000000201596
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	1103 Clinical Sciences, 1109 Neurosciences, 1702 Cognitive Sciences
dc.subject.classification	Neurology & Neurosurgery
dc.subject.classification	3202 Clinical sciences
dc.subject.classification	3209 Neurosciences
dc.subject.mesh	Child
dc.subject.mesh	Female
dc.subject.mesh	Humans
dc.subject.mesh	Male
dc.subject.mesh	Middle Aged
dc.subject.mesh	Brain
dc.subject.mesh	Brain Ischemia
dc.subject.mesh	Ischemic Stroke
dc.subject.mesh	Magnetic Resonance Imaging
dc.subject.mesh	Stroke
dc.subject.mesh	Brain
dc.subject.mesh	Humans
dc.subject.mesh	Brain Ischemia
dc.subject.mesh	Magnetic Resonance Imaging
dc.subject.mesh	Middle Aged
dc.subject.mesh	Child
dc.subject.mesh	Female
dc.subject.mesh	Male
dc.subject.mesh	Stroke
dc.subject.mesh	Ischemic Stroke
dc.subject.mesh	Child
dc.subject.mesh	Female
dc.subject.mesh	Humans
dc.subject.mesh	Male
dc.subject.mesh	Middle Aged
dc.subject.mesh	Brain
dc.subject.mesh	Brain Ischemia
dc.subject.mesh	Ischemic Stroke
dc.subject.mesh	Magnetic Resonance Imaging
dc.subject.mesh	Stroke
dc.title	Radiomics-Derived Brain Age Predicts Functional Outcome After Acute Ischemic Stroke.
dc.type	Journal Article
utslib.citation.volume	100
utslib.location.activity	United States
utslib.for	1103 Clinical Sciences
utslib.for	1109 Neurosciences
utslib.for	1702 Cognitive Sciences
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Health
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2024-01-25T05:32:03Z
pubs.issue	8
pubs.publication-status	Published online
pubs.volume	100
utslib.citation.issue	8

Abstract:

BACKGROUND AND OBJECTIVES: While chronological age is one of the most influential determinants of post-stroke outcomes, little is known of the impact of neuroimaging-derived biological "brain age". We hypothesized that radiomics analyses of T2-FLAIR images texture would provide brain age estimates and that advanced brain age of stroke patients will be associated with cardiovascular risk factors and worse functional outcomes. METHODS: We extracted radiomics from T2-FLAIR images acquired during acute stroke clinical evaluation. Brain age was determined from brain parenchyma radiomics using an ElasticNet linear regression model. Subsequently, relative brain age (RBA), which expresses brain age in comparison to chronological age-matched peers, was estimated. Finally, we built a linear regression model of RBA using clinical cardiovascular characteristics as inputs, and a logistic regression model of favorable functional outcomes taking RBA as input. RESULTS: We reviewed 4,163 patients from a large multisite ischemic stroke cohort (mean age=62.8 years, 42.0% females). T2-FLAIR radiomics predicted chronological ages (mean absolute error=6.9 years, r=0.81). After adjustment for covariates, RBA was higher and therefore described older-appearing brains in patients with hypertension, diabetes mellitus, a history of smoking, and a history of a prior stroke. In multivariate analyses, age, RBA, NIHSS, and a history of prior stroke were all significantly associated with functional outcome (respective adjusted Odds-Ratios: 0.58, 0.76, 0.48, 0.55; all p-values<0.001). Moreover, the negative effect of RBA on outcome was especially pronounced in minor strokes. DISCUSSION: T2-FLAIR radiomics can be used to predict brain age and derive RBA. Older appearing brains, characterized by a higher RBA, reflect cardiovascular risk factor accumulation and are linked to worse outcomes after stroke.

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http://hdl.handle.net/10453/174947