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MRI Radiomic Signature of White Matter Hyperintensities Is Associated With Clinical Phenotypes.
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; Vagal, Achala; Wasselius, Johan; Woo, Daniel; Wu, Ona; Zand, Ramin; Worrall, Bradford B.
Afiliação
  • Bretzner M; J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA, United States.
  • Bonkhoff AK; Inserm, CHU Lille, U1172 - LilNCog (JPARC) - Lille Neurosciences and Cognition, University of Lille, Lille, France.
  • Schirmer MD; J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA, United States.
  • Hong S; J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA, United States.
  • Dalca AV; J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA, United States.
  • Donahue KL; J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA, United States.
  • Giese AK; A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.
  • Etherton MR; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States.
  • Rist PM; J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA, United States.
  • Nardin M; J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA, United States.
  • Marinescu R; J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA, United States.
  • Wang C; J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA, United States.
  • Regenhardt RW; Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States.
  • Leclerc X; J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA, United States.
  • Lopes R; J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA, United States.
  • Benavente OR; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States.
  • Cole JW; J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA, United States.
  • Donatti A; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States.
  • Griessenauer CJ; J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA, United States.
  • Heitsch L; Inserm, CHU Lille, U1172 - LilNCog (JPARC) - Lille Neurosciences and Cognition, University of Lille, Lille, France.
  • Holmegaard L; Inserm, CHU Lille, U1172 - LilNCog (JPARC) - Lille Neurosciences and Cognition, University of Lille, Lille, France.
  • Jood K; CNRS, Institut Pasteur de Lille, US 41 - UMS 2014 - PLBS, Lille, France.
  • Jimenez-Conde J; Department of Medicine, Division of Neurology, University of British Columbia, Vancouver, BC, Canada.
  • Kittner SJ; Department of Neurology, University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore, MD, United States.
  • Lemmens R; School of Medical Sciences, University of Campinas (UNICAMP) and the Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Brazil.
  • Levi CR; Department of Neurosurgery, Geisinger, Danville, PA, United States.
  • McArdle PF; Research Institute of Neurointervention, Paracelsus Medical University, Salzburg, Austria.
  • McDonough CW; Division of Emergency Medicine, Washington University School of Medicine, St. Louis, MO, United States.
  • Meschia JF; Department of Neurology, Washington University School of Medicine and Barnes-Jewish Hospital, St. Louis, MO, United States.
  • Phuah CL; Institute of Biomedicine, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden.
  • Rolfs A; Institute of Biomedicine, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden.
  • Ropele S; Department of Neurology, Neurovascular Research Group (NEUVAS), Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Universitat Autonoma de Barcelona, Barcelona, Spain.
  • Rosand J; Department of Neurology, University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore, MD, United States.
  • Roquer J; Department of Neurosciences, Experimental Neurology and Leuven Research Institute for Neuroscience and Disease (LIND), KU Leuven - University of Leuven, Leuven, Belgium.
  • Rundek T; VIB, Vesalius Research Center, Laboratory of Neurobiology, Department of Neurology, University Hospitals Leuven, Leuven, Belgium.
  • Sacco RL; School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia.
  • Schmidt R; Department of Neurology, John Hunter Hospital, Newcastle, NSW, Australia.
  • Sharma P; Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, United States.
  • Slowik A; Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, University of Florida, Gainesville, FL, United States.
  • Sousa A; Department of Neurology, Mayo Clinic, Jacksonville, FL, United States.
  • Stanne TM; Department of Neurology, Washington University School of Medicine and Barnes-Jewish Hospital, St. Louis, MO, United States.
  • Strbian D; Centogene AG, Rostock, Germany.
  • Tatlisumak T; Department of Neurology, Clinical Division of Neurogeriatrics, Medical University of Graz, Graz, Austria.
  • Thijs V; Henry and Allison McCance Center for Brain Health, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States.
  • Vagal A; Department of Neurology and Evelyn F. McKnight Brain Institute, Miller School of Medicine, University of Miami, Miami, FL, United States.
  • Wasselius J; Department of Neurology and Evelyn F. McKnight Brain Institute, Miller School of Medicine, University of Miami, Miami, FL, United States.
  • Woo D; Department of Neurology and Evelyn F. McKnight Brain Institute, Miller School of Medicine, University of Miami, Miami, FL, United States.
  • Wu O; Department of Neurology, Clinical Division of Neurogeriatrics, Medical University of Graz, Graz, Austria.
  • Zand R; Institute of Cardiovascular Research, Royal Holloway University of London (ICR2UL), Egham, United Kingdom.
  • Worrall BB; Ashford and St. Peter's Hospitals, Chertsey and Ashford, United Kingdom.
Front Neurosci ; 15: 691244, 2021.
Article em En | MEDLINE | ID: mdl-34321995
OBJECTIVE: 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. METHODS: 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). RESULTS: 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. CONCLUSION: 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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Neurosci Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Neurosci Ano de publicação: 2021 Tipo de documento: Article