Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/27114
Title: MRI Radiomic Signature of White Matter Hyperintensities Is Associated With Clinical Phenotypes.
Austin Authors: Bretzner, Martin;Bonkhoff, Anna K;Schirmer, Markus D;Hong, Sungmin;Dalca, Adrian V;Donahue, Kathleen L;Giese, Anne-Katrin;Etherton, Mark R;Rist, Pamela M;Nardin, Marco;Marinescu, Razvan;Wang, Clinton;Regenhardt, Robert W;Leclerc, Xavier;Lopes, Renaud;Benavente, Oscar R;Cole, John W;Donatti, Amanda;Griessenauer, Christoph J;Heitsch, Laura;Holmegaard, Lukas;Jood, Katarina;Jimenez-Conde, Jordi;Kittner, Steven J;Lemmens, Robin;Levi, Christopher R;McArdle, Patrick F;McDonough, Caitrin W;Meschia, James F;Phuah, Chia-Ling;Rolfs, Arndt;Ropele, Stefan;Rosand, Jonathan;Roquer, Jaume;Rundek, Tatjana;Sacco, Ralph L;Schmidt, Reinhold;Sharma, Pankaj;Slowik, Agnieszka;Sousa, Alessandro;Stanne, Tara M;Strbian, Daniel;Tatlisumak, Turgut;Thijs, Vincent N ;Vagal, Achala;Wasselius, Johan;Woo, Daniel;Wu, Ona;Zand, Ramin;Worrall, Bradford B;Maguire, Jane M;Lindgren, Arne;Jern, Christina;Golland, Polina;Kuchcinski, Grégory;Rost, Natalia S
Affiliation: Department of Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden
Centogene AG, Rostock, Germany
Institute of Biomedicine, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
Department of Clinica Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden
Department of Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden
Department of Radiology, Neuroradiology, Skåne University Hospital, Malmö, Sweden
Department of Neurology and Rehabilitation Medicine, Skåne University Hospital, Lund, Sweden
Department of Neurosciences, Experimental Neurology and Leuven Research Institute for Neuroscience and Disease (LIND), KU Leuven - University of Leuven, Leuven, Belgium
VIB, Vesalius Research Center, Laboratory of Neurobiology, Department of Neurology, University Hospitals Leuven, Leuven, Belgium
School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia
Department of Neurology, John Hunter Hospital, Newcastle, NSW, Australia
The Florey Institute of Neuroscience and Mental Health
Faculty of Health, University of Technology Sydney, Ultimo, NSW, Australia
Department of Medicine, Division of Neurology, University of British Columbia, Vancouver, BC, Canada
J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA, United States
Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States
Department of Neurology, University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore, MD, United States
Department of Neurosurgery, Geisinger, Danville, PA, United States
Research Institute of Neurointervention, Paracelsus Medical University, Salzburg, Austria
Division of Emergency Medicine, Washington University School of Medicine, St. Louis, MO, United States
Department of Neurology, Washington University School of Medicine and Barnes-Jewish Hospital, St. Louis, MO, United States
Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, United States
Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, University of Florida, Gainesville, FL, United States
Department of Neurology, Mayo Clinic, Jacksonville, FL, United States
Henry and Allison McCance Center for Brain Health, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States
Department of Neurology and Evelyn F. McKnight Brain Institute, Miller School of Medicine, University of Miami, Miami, FL, United States
Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States
Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, United States
A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
Department of Neurology, Geisinger, Danville, PA, United States
Department of Neurology and Public Health Sciences, University of Virginia, Charlottesville, VA, United States
Inserm, CHU Lille, U1172 - LilNCog (JPARC) - Lille Neurosciences and Cognition, University of Lille, Lille, France
CNRS, Institut Pasteur de Lille, US 41 - UMS 2014 - PLBS, Lille, France
Institute of Cardiovascular Research, Royal Holloway University of London (ICR2UL), Egham, United Kingdom
Ashford and St. Peter's Hospitals, Chertsey and Ashford, United Kingdom
School of Medical Sciences, University of Campinas (UNICAMP) and the Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Brazil
Department of Neurology, Neurovascular Research Group (NEUVAS), Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Universitat Autonoma de Barcelona, Barcelona, Spain
Department of Neurology, Clinical Division of Neurogeriatrics, Medical University of Graz, Graz, Austria
Department of Neurology, Jagiellonian University Medical College, Krakow, Poland
Division of Neurocritical Care and Emergency Neurology, Department of Neurology, Helsinki University Central Hospital, Helsinki, Finland
Neurology
Issue Date: 12-Jul-2021
Date: 2021-07-12
Publication information: Frontiers in Neuroscience 2021; 15: 691244
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 CV 1 - 6 < 0.001, p-value CV 7 = 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.
URI: https://ahro.austin.org.au/austinjspui/handle/1/27114
DOI: 10.3389/fnins.2021.691244
Journal: Frontiers in Neuroscience
PubMed URL: 34321995
ISSN: 1662-4548
Type: Journal Article
Subjects: MRI
brain health
cerebrovascular disease (CVD)
machine learning
radiomics
stroke
Appears in Collections:Journal articles

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