Radiomics-Derived Brain Age Predicts Functional Outcome after Acute Ischemic Stroke

Martin Bretzner, Anna K. Bonkhoff, Markus D. Schirmer, Sungmin Hong, Adrian Dalca, Kathleen Donahue, Anne Katrin Giese, Mark R. Etherton, Pamela M. Rist, Marco Nardin, Robert W. Regenhardt, Xavier Leclerc, Renaud Lopes, Morgan Gautherot, Clinton Wang, Oscar R. Benavente, John W. Cole, Amanda Donatti, Christoph Griessenauer, Laura HeitschLukas Holmegaard, Katarina Jood, Jordi Jimenez-Conde, Steven J. Kittner, Robin Lemmens, Christopher R. Levi, Patrick F. McArdle, Caitrin W. McDonough, James F. Meschia, Chia Ling Phuah, Arndt Rolfs, Stefan Ropele, Jonathan Rosand, Jaume Roquer, Tatjana Rundek, Ralph L. Sacco, Reinhold Schmidt, Pankaj Sharma, Agnieszka Slowik, Alessandro Sousa, Tara M. Stanne, Daniel Strbian, Turgut Tatlisumak, Vincent Thijs, Achala Vagal, Johan Wasselius, Daniel Woo, Ona Wu, Ramin Zand, Bradford B. Worrall, Jane Maguire, Arne G. Lindgren, Christina Jern, Polina Golland, Grégory Kuchcinski, Natalia S. Rost

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Background and ObjectivesWhile chronological age is one of the most influential determinants of poststroke 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 patients with stroke will be associated with cardiovascular risk factors and worse functional outcomes.MethodsWe 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 with 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.ResultsWe reviewed 4,163 patients from a large multisite ischemic stroke cohort (mean age = 62.8 years, 42.0% female patients). 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.DiscussionT2-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.

Original languageEnglish
Pages (from-to)E822-E833
JournalNeurology
Volume100
Issue number8
DOIs
StatePublished - Feb 21 2023

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