TY - JOUR
T1 - MRI Radiomic Signature of White Matter Hyperintensities Is Associated With Clinical Phenotypes
AU - the MRI-GENIE GISCOME Investigators the International and Stroke Genetics Consortium
AU - Bretzner, Martin
AU - Bonkhoff, Anna K.
AU - Schirmer, Markus D.
AU - Hong, Sungmin
AU - Dalca, Adrian V.
AU - Donahue, Kathleen L.
AU - Giese, Anne Katrin
AU - Etherton, Mark R.
AU - Rist, Pamela M.
AU - Nardin, Marco
AU - Marinescu, Razvan
AU - Wang, Clinton
AU - Regenhardt, Robert W.
AU - Leclerc, Xavier
AU - Lopes, Renaud
AU - Benavente, Oscar R.
AU - Cole, John W.
AU - Donatti, Amanda
AU - Griessenauer, Christoph J.
AU - Heitsch, Laura
AU - Holmegaard, Lukas
AU - Jood, Katarina
AU - Jimenez-Conde, Jordi
AU - Kittner, Steven J.
AU - Lemmens, Robin
AU - Levi, Christopher R.
AU - McArdle, Patrick F.
AU - McDonough, Caitrin W.
AU - Meschia, James F.
AU - Phuah, Chia Ling
AU - Rolfs, Arndt
AU - Ropele, Stefan
AU - Rosand, Jonathan
AU - Roquer, Jaume
AU - Rundek, Tatjana
AU - Sacco, Ralph L.
AU - Schmidt, Reinhold
AU - Sharma, Pankaj
AU - Slowik, Agnieszka
AU - Sousa, Alessandro
AU - Stanne, Tara M.
AU - Strbian, Daniel
AU - Tatlisumak, Turgut
AU - Thijs, Vincent
AU - Vagal, Achala
AU - Wasselius, Johan
AU - Woo, Daniel
AU - Wu, Ona
AU - Zand, Ramin
AU - Worrall, Bradford B.
N1 - Publisher Copyright: © Copyright © 2021 Bretzner, Bonkhoff, Schirmer, Hong, Dalca, Donahue, Giese, Etherton, Rist, Nardin, Marinescu, Wang, Regenhardt, Leclerc, Lopes, Benavente, Cole, Donatti, Griessenauer, Heitsch, Holmegaard, Jood, Jimenez-Conde, Kittner, Lemmens, Levi, McArdle, McDonough, Meschia, Phuah, Rolfs, Ropele, Rosand, Roquer, Rundek, Sacco, Schmidt, Sharma, Slowik, Sousa, Stanne, Strbian, Tatlisumak, Thijs, Vagal, Wasselius, Woo, Wu, Zand, Worrall, Maguire, Lindgren, Jern, Golland, Kuchcinski and Rost.
PY - 2021/7/12
Y1 - 2021/7/12
N2 - 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 (R2 = 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-valuesCV1–6 < 0.001, p-valueCV7 = 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.
AB - 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 (R2 = 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-valuesCV1–6 < 0.001, p-valueCV7 = 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.
KW - MRI
KW - brain health
KW - cerebrovascular disease (CVD)
KW - machine learning
KW - radiomics
KW - stroke
UR - https://www.scopus.com/pages/publications/85111389288
U2 - 10.3389/fnins.2021.691244
DO - 10.3389/fnins.2021.691244
M3 - Article
SN - 1662-4548
VL - 15
JO - Frontiers in Neuroscience
JF - Frontiers in Neuroscience
M1 - 691244
ER -