2026-04-17T20:30:56Zhttps://recercat.cat/oai/request

oai:recercat.cat:2072/4706842025-07-29T22:07:00Zcom_2072_98col_2072_378192

00925njm 22002777a 4500 dc Bretzner, Martin author Bonkhoff, Anna K. author Schirmer, Markus D. author Hong, Sungmin author Dalca, Adrian V. author Donahue, Kathleen L. author Giese, Anne-Katrin author Etherton, Mark R. author Rist, Pamela M. author Nardin, Marco author Marinescu, Razvan author Wang, Clinton author Regenhardt, Robert W. author Leclerc, Xavier author Lopes, Renaud author Benavente, Oscar R. author Cole, John W. author Donatti, Amanda author Griessenauer, Christoph J. author Heitsch, Laura author Holmegaard, Lukas author Jood, Katarina author Jimenez-Conde, Jordi author Kittner, Steven J. author Lemmens, Robin author Levi, Christopher R. author McArdle, Patrick F. author McDonough, Caitrin W. author Meschia, James F. author Phuah, Chia-Ling author Rolfs, Arndt author Ropele, Stefan author Rosand, Jonathan author Roquer González, Jaume author Rundek, Tatjana author Sacco, Ralph L. author Schmidt, Reinhold author Sharma, Pankaj author Slowik, Agnieszka author Sousa, Alessandro author Stanne, Tara M. author Strbian, Daniel author Tatlisumak, Turgut author Thijs, Vincent author Vagal, Achala author Wasselius, Johan author Woo, Daniel author Wu, Ona author Zand, Ramin author Worrall, Bradford B. author Maguire, Jane M. author Lindgren, Arne author Jern, Christina author Golland, Polina author Kuchcinski, Grégory author Rost, Natalia S. author Universitat Autònoma de Barcelona author 2021 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 < 0.001, p -value = 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. Stroke Cerebrovascular disease (CVD) MRI Radiomics Machine learning Brain health MRI Radiomic Signature of White Matter Hyperintensities Is Associated With Clinical Phenotypes