MRI Radiomic Signature of White Matter Hyperintensities Is Associated With Clinical Phenotypes

dc.contributor.author
Bretzner, Martin
dc.contributor.author
Bonkhoff, Anna K.
dc.contributor.author
Schirmer, Markus D.
dc.contributor.author
Hong, Sungmin
dc.contributor.author
Dalca, Adrian V.
dc.contributor.author
Donahue, Kathleen L.
dc.contributor.author
Giese, Anne-Katrin
dc.contributor.author
Etherton, Mark R.
dc.contributor.author
Rist, Pamela M.
dc.contributor.author
Nardin, Marco
dc.contributor.author
Marinescu, Razvan
dc.contributor.author
Wang, Clinton
dc.contributor.author
Regenhardt, Robert W.
dc.contributor.author
Leclerc, Xavier
dc.contributor.author
Lopes, Renaud
dc.contributor.author
Benavente, Oscar R.
dc.contributor.author
Cole, John W.
dc.contributor.author
Donatti, Amanda
dc.contributor.author
Griessenauer, Christoph J.
dc.contributor.author
Heitsch, Laura
dc.contributor.author
Holmegaard, Lukas
dc.contributor.author
Jood, Katarina
dc.contributor.author
Jimenez-Conde, Jordi
dc.contributor.author
Kittner, Steven J.
dc.contributor.author
Lemmens, Robin
dc.contributor.author
Levi, Christopher R.
dc.contributor.author
McArdle, Patrick F.
dc.contributor.author
McDonough, Caitrin W.
dc.contributor.author
Meschia, James F.
dc.contributor.author
Phuah, Chia-Ling
dc.contributor.author
Rolfs, Arndt
dc.contributor.author
Ropele, Stefan
dc.contributor.author
Rosand, Jonathan
dc.contributor.author
Roquer González, Jaume
dc.contributor.author
Rundek, Tatjana
dc.contributor.author
Sacco, Ralph L.
dc.contributor.author
Schmidt, Reinhold
dc.contributor.author
Sharma, Pankaj
dc.contributor.author
Slowik, Agnieszka
dc.contributor.author
Sousa, Alessandro
dc.contributor.author
Stanne, Tara M.
dc.contributor.author
Strbian, Daniel
dc.contributor.author
Tatlisumak, Turgut
dc.contributor.author
Thijs, Vincent
dc.contributor.author
Vagal, Achala
dc.contributor.author
Wasselius, Johan
dc.contributor.author
Woo, Daniel
dc.contributor.author
Wu, Ona
dc.contributor.author
Zand, Ramin
dc.contributor.author
Worrall, Bradford B.
dc.contributor.author
Maguire, Jane M.
dc.contributor.author
Lindgren, Arne
dc.contributor.author
Jern, Christina
dc.contributor.author
Golland, Polina
dc.contributor.author
Kuchcinski, Grégory
dc.contributor.author
Rost, Natalia S.
dc.contributor.author
Universitat Autònoma de Barcelona
dc.date.issued
2021
dc.identifier
https://ddd.uab.cat/record/283311
dc.identifier
urn:10.3389/fnins.2021.691244
dc.identifier
urn:oai:ddd.uab.cat:283311
dc.identifier
urn:pmcid:PMC8312571
dc.identifier
urn:pmc-uid:8312571
dc.identifier
urn:pmid:34321995
dc.identifier
urn:oai:pubmedcentral.nih.gov:8312571
dc.identifier
urn:oai:egreta.uab.cat:publications/bc6b5f4d-f913-4994-8def-8f98f2193cdf
dc.description.abstract
Neuroimaging measurements of brain structural integrity are thought to be surrogates for brain health, but precise assessments require dedicated advanced image acquisitions. By means of quantitatively describing conventional images, radiomic analyses hold potential for evaluating brain health. We sought to: (1) evaluate radiomics to assess brain structural integrity by predicting white matter hyperintensities burdens (WMH) and (2) uncover associations between predictive radiomic features and clinical phenotypes. We analyzed a multi-site cohort of 4,163 acute ischemic strokes (AIS) patients with T2-FLAIR MR images with total brain and WMH segmentations. Radiomic features were extracted from normal-appearing brain tissue (brain mask-WMH mask). Radiomics-based prediction of personalized WMH burden was done using ElasticNet linear regression. We built a radiomic signature of WMH with stable selected features predictive of WMH burden and then related this signature to clinical variables using canonical correlation analysis (CCA). Radiomic features were predictive of WMH burden (R 2 = 0.855 ± 0.011). Seven pairs of canonical variates (CV) significantly correlated the radiomics signature of WMH and clinical traits with respective canonical correlations of 0.81, 0.65, 0.42, 0.24, 0.20, 0.15, and 0.15 (FDR-corrected p -values < 0.001, p -value = 0.012). The clinical CV1 was mainly influenced by age, CV2 by sex, CV3 by history of smoking and diabetes, CV4 by hypertension, CV5 by atrial fibrillation (AF) and diabetes, CV6 by coronary artery disease (CAD), and CV7 by CAD and diabetes. Radiomics extracted from T2-FLAIR images of AIS patients capture microstructural damage of the cerebral parenchyma and correlate with clinical phenotypes, suggesting different radiographical textural abnormalities per cardiovascular risk profile. Further research could evaluate radiomics to predict the progression of WMH and for the follow-up of stroke patients' brain health.
dc.format
application/pdf
dc.language
eng
dc.publisher
dc.relation
Frontiers in Neuroscience ; Vol. 15 (july 2021)
dc.rights
open access
dc.rights
Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
dc.rights
https://creativecommons.org/licenses/by/4.0/
dc.subject
Stroke
dc.subject
Cerebrovascular disease (CVD)
dc.subject
MRI
dc.subject
Radiomics
dc.subject
Machine learning
dc.subject
Brain health
dc.title
MRI Radiomic Signature of White Matter Hyperintensities Is Associated With Clinical Phenotypes
dc.type
Article


Ficheros en el ítem

FicherosTamañoFormatoVer

No hay ficheros asociados a este ítem.

Este ítem aparece en la(s) siguiente(s) colección(ones)