MRI Radiomic Signature of White Matter Hyperintensities Is Associated With Clinical Phenotypes

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MRI-GENIE & GISCOME Investigators , Int Stroke Genetics Consortium , Bretzner , M , Bonkhoff , A K , Schirmer , M D , Strbian , D , Tatlisumak , T & Worrall , B B 2021 , ' MRI Radiomic Signature of White Matter Hyperintensities Is Associated With Clinical Phenotypes ' , Frontiers in Neuroscience , vol. 15 , 691244 . https://doi.org/10.3389/fnins.2021.691244

Title: MRI Radiomic Signature of White Matter Hyperintensities Is Associated With Clinical Phenotypes
Author: MRI-GENIE & GISCOME Investigators; Int Stroke Genetics Consortium; Bretzner, Martin; Bonkhoff, Anna K.; Schirmer, Markus D.; Strbian, Daniel; Tatlisumak, Turgut; Worrall, Bradford B.
Other contributor: University of Helsinki, Neurologian yksikkö
University of Helsinki, HUS Neurocenter

Date: 2021-07-12
Language: eng
Number of pages: 11
Belongs to series: Frontiers in Neuroscience
ISSN: 1662-453X
DOI: https://doi.org/10.3389/fnins.2021.691244
URI: http://hdl.handle.net/10138/334430
Abstract: 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(CV1-6) < 0.001, p-value(CV7) = 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
SMALL VESSEL DISEASE
INTEGRITY
OUTCOMES
IMAGES
VOLUME
BRAIN
3112 Neurosciences
3124 Neurology and psychiatry
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