MRI Radiomic Signature of White Matter Hyperintensities Is Associated With Clinical Phenotypes
Bretzner, Martin (University of Lille)
Bonkhoff, Anna K. (Massachusetts General Hospital)
Schirmer, Markus D. (Massachusetts General Hospital)
Hong, Sungmin (Massachusetts General Hospital)
Dalca, Adrian V. (Massachusetts Institute of Technology)
Donahue, Kathleen L. (Massachusetts General Hospital)
Giese, Anne-Katrin (Massachusetts General Hospital)
Etherton, Mark R. (Massachusetts General Hospital)
Rist, Pamela M. (Brigham and Women's Hospital (Boston, Estats Units d'Amèrica))
Nardin, Marco (Massachusetts General Hospital)
Marinescu, Razvan (Massachusetts Institute of Technology)
Wang, Clinton (Massachusetts Institute of Technology)
Regenhardt, Robert W. (Massachusetts General Hospital)
Leclerc, Xavier (University of Lille)
Lopes, Renaud (Institut Pasteur de Lille)
Benavente, Oscar R. (Department of Medicine, University of British Columbia)
Cole, John W. (Department of Neurology, University of Maryland School of Medicine a)
Donatti, Amanda (School of Medical Sciences, University of Campinas (Brasil))
Griessenauer, Christoph J. (Paracelsus Medical University)
Heitsch, Laura (Department of Neurology, Washington University School of Medicine)
Holmegaard, Lukas (Sahlgrenska Academy at University of Gothenburg)
Jood, Katarina (University of Gothenburg)
Jimenez-Conde, Jordi (Institut Hospital del Mar d'Investigacions Mèdiques)
Kittner, Steven J. (Department of Neurology, University of Maryland School of Medicine)
Lemmens, Robin (University Hospitals Leuven (Bèlgica))
Levi, Christopher R. (Department of Neurology, John Hunter Hospital)
McArdle, Patrick F. (Department of Medicine, University of Maryland School of Medicine)
McDonough, Caitrin W. (Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, University of Florida)
Meschia, James F. (Mayo Clinic (Estats Units d'Amèrica))
Phuah, Chia-Ling (Department of Neurology, Washington University School of Medicine)
Rolfs, Arndt (Centogene (Alemanya))
Ropele, Stefan (Department of Neurology, Clinical Division of Neurogeriatrics, Medical University of Graz)
Rosand, Jonathan (Massachusetts General Hospital)
Roquer, Jaume (Department of Neurology, University of Miami)
Rundek, Tatjana (Department of Neurology, University of Miami)
Sacco, Ralph L. (Department of Neurology University of Miami)
Schmidt, Reinhold (Department of Neurology, Medical University of Graz)
Sharma, Pankaj (St. Peter's Hospitals (Regne Unit))
Slowik, Agnieszka (Department of Neurology, Jagiellonian University Medical College)
Sousa, Alessandro (Department of Neurology, University of Maryland School of Medicine)
Stanne, Tara M. (University of Gothenburg)
Strbian, Daniel (Department of Neurology, Helsinki University Central Hospital)
Tatlisumak, Turgut (Department of Neurology, Sahlgrenska University Hospital)
Thijs, Vincent (Florey Institute of Neuroscience and Mental Health, Department of Neurology Austin Health)
Vagal, Achala (Department of Radiology, University of Cincinnati College of Medicine)
Wasselius, Johan (Skåne University Hospital (Suècia))
Woo, Daniel (Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine)
Wu, Ona (Massachusetts General Hospital)
Zand, Ramin (Department of Neurology, Geisinger)
Worrall, Bradford B. (Department of Neurology and Public Health Sciences, University of Virginia)
Maguire, Jane M. (University of Technology Sydney)
Lindgren, Arne (Department of Clinical Sciences Lund, Lund University)
Jern, Christina (Sahlgrenska Academy at University of Gothenburg)
Golland, Polina (Massachusetts Institute of Technology)
Kuchcinski, Grégory (University of Lille)
Rost, Natalia S. (Massachusetts General Hospital)
Universitat Autònoma de Barcelona

Fecha:	2021
Resumen:	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.
Derechos:	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.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	Stroke ; Cerebrovascular disease (CVD) ; MRI ; Radiomics ; Machine learning ; Brain health
Publicado en:	Frontiers in Neuroscience, Vol. 15 (july 2021) , ISSN 1662-453X

DOI: 10.3389/fnins.2021.691244
PMID: 34321995

11 p, 897.2 KB

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