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

2021-07-12

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

MRI

brain health

cerebrovascular disease (CVD)

machine learning

radiomics

stroke

Journal Article

10.3389/fnins.2021.691244

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.

link

Frontiers in Neuroscience 2021; 15: 691244

Frontiers in Neuroscience

1662-4548