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In Frontiers in neuroscience ; h5-index 72.0

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 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.

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.

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, 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