- Title
- MRI Radiomic Signature of White Matter Hyperintensities Is Associated With Clinical Phenotypes
- Creator
- Bretzner, Martin; Bonkhoff, Anna K.; Marinescu, Razvan; Wang, Clinton; Regenhardt, Robert W.; Leclerc, Xavier; Lopes, Renaud; Benavente, Oscar R.; Cole, John W.; Donatti, Amanda; Griessenauer, Christoph J.; Heitsch, Laura; Schirmer, Markus D.; Levi, Christopher R.; Jood, K; Jimenez-Conde, J; Kittner, SJ; Lemmens, R; Levi, CR; McArdle, PF; McDonough, CW; Meschia, JF; Phuah, C-L; Hong, Sungmin; Rolfs, A; Ropele, S; Rosand, J; Roquer, J; Rundek, T; Sacco, RL; Schmidt, R; Sharma, P; Slowik, A; Sousa, A; Dalca, Adrian V.; Stanne, TM; Strbian, D; Tatlisumak, T; Thijs, V; Vagal, A; Wasselius, J; Woo, D; Wu, O; Zand, R; Worrall, BB; Donahue, Kathleen L.; Maguire, JM; Lindgren, A; Jern, C; Golland, P; Kuchcinski, G; Rost, NS; Giese, Anne-Katrin; Etherton, Mark R.; Rist, Pamela M.; Nardin, Marco
- Relation
- Frontiers in Neuroscience Vol. 15, no. 691244
- Publisher Link
- http://dx.doi.org/10.3389/fnins.2021.691244
- Publisher
- Frontiers
- Resource Type
- journal article
- Date
- 2021
- Description
- 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.
- Subject
- stroke; cerebrovascular disease (CVD); MRI; radiomics; machine learning; brain health; SDG 3; Sustainable Development Goals
- Identifier
- http://hdl.handle.net/1959.13/1471639
- Identifier
- uon:48705
- Identifier
- ISSN:1662-4548
- Rights
- © 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. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
- Language
- eng
- Full Text
- Reviewed
- Hits: 6642
- Visitors: 6783
- Downloads: 164
Collections
Goal 3: Good Health and Well-Being
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | ATTACHMENT02 | Publisher version (open access) | 897 KB | Adobe Acrobat PDF | View Details Download |