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

Abstract

ObjectiveNeuroimaging 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.MethodsWe 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).ResultsRadiomic 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.ConclusionRadiomics 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.

Deep profiling of multiple ischemic lesions in a large, multi-center cohort: Frequency, spatial distribution, and associations to clinical characteristics

“…Lastly, we here focused on estimating 10.3389/fnins.2022.994458 effects for total DWI and WMH lesions volumes and compared stroke lesion distributions only qualitatively (Figure 2). Future work could go one step further even and evaluate lesion distributions and location in quantitative ways and additionally employ techniques such as radiomics (Bretzner et al, 2021;Regenhardt et al, 2021) or image contrast enhancement (Versaci et al, 2015) to augment the imaging-based lesion information.…”

Precision medicine in stroke: towards personalized outcome predictions using artificial intelligence

“… 41 However, ‘big’ imaging datasets of stroke patients are still at least an order of magnitude smaller (e.g. 2800 structural scans in the MRI-GENIE study, 42 , 43 2950 scans of in Meta VCI map consortium, 44 1800 scans in ENIGMA 45 or 1333 scans in an unicentre study 46 , 47 ). All in all, there have been calls to accumulate and exploit regularly obtained clinical, imaging and genetic stroke patient data in a collaborative fashion.…”

Association of Stroke Lesion Pattern and White Matter Hyperintensity Burden With Stroke Severity and Outcome

“…WMH burden is well known to correlate with a multitude of cardiovascular risk factors, such as hypertension (HTN), diabetes mellitus (DM), atrial fibrillation (AF), and smoking in both healthy and stroke populations. [8][9][10] A growing body of literature additionally points toward deleterious effects of WHM burden specifically on stroke outcomes. WMH burden and decreased white matter integrity negatively influence both early 11 and long-term neurologic outcomes.…”