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Comparing the Performance of Two Radiomic Models to Predict Progression and Progression Speed of White Matter Hyperintensities.

ABSTRACT: Purpose: The aim of this study was to compare two radiomic models in predicting the progression of white matter hyperintensity (WMH) and the speed of progression from conventional magnetic resonance images. Methods: In this study, 232 people were retrospectively analyzed at Medical Center A (training and testing groups) and Medical Center B (external validation group). A visual rating scale was used to divide all patients into WMH progression and non-progression groups. Two regions of interest (ROIs)-ROI whole-brain white matter (WBWM) and ROI WMH penumbra (WMHp)-were segmented from the baseline image. For predicting WMH progression, logistic regression was applied to create radiomic models in the two ROIs. Then, age, sex, clinical course, vascular risk factors, and imaging factors were incorporated into a stepwise regression analysis to construct the combined diagnosis model. Finally, the presence of a correlation between radiomic findings and the speed of progression was analyzed. Results: The area under the curve (AUC) was higher for the WMHp-based radiomic model than the WBWM-based radiomic model in training, testing, and validation groups (0.791, 0.768, and 0.767 vs. 0.725, 0.693, and 0.691, respectively). The WBWM-based combined model was established by combining age, hypertension, and rad-score of the ROI WBWM. Also, the WMHp-based combined model is built by combining the age and rad-score of the ROI WMHp. Compared with the WBWM-based model (AUC = 0.779, 0.716, 0.673 in training, testing, and validation groups, respectively), the WMHp-based combined model has higher diagnostic efficiency and better generalization ability (AUC = 0.793, 0.774, 0.777 in training, testing, and validation groups, respectively). The speed of WMH progression was related to the rad-score from ROI WMHp (r = 0.49) but not from ROI WBWM. Conclusion: The heterogeneity of the penumbra could help identify the individuals at high risk of WMH progression and the rad-score of it was correlated with the speed of progression.

SUBMITTER: Shao Y

PROVIDER: S-EPMC8671609 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description: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 (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.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.

Project description:Frontal lobe structures decline faster than most other brain regions in older adults. Age-related change in the frontal lobe is associated with poorer executive function (e.g., working memory, switching/set-shifting, and inhibitory control). The effects and presence of frontal lobe white matter hyperintensities (WMH) on executive function in normal aging is relatively unknown. The current study assessed relationships between region-specific frontal WMH load and cognitive performance in healthy older adults using three executive function tasks from the NIH Toolbox (NIHTB) Cognition Battery. A cohort of 279 healthy older adults ages 65-88 completed NIHTB and 3T T1-weighted and FLAIR MRI. Lesion Segmentation Toolbox quantified WMH volume and generated lesion probability maps. Individual lesion maps were registered to the Desikan-Killiany atlas in FreeSurfer 6.0 to define regions of interest (ROI). Independent linear regressions assessed relationships between executive function performance and region-specific WMH in frontal lobe ROIs. All models included age, sex, education, estimated total intracranial volume, multi-site scanner differences, and cardiovascular disease risk using Framingham criteria as covariates. Poorer set-shifting performance was associated with greater WMH load in three frontal ROIs including bilateral superior frontal (left β = -0.18, FDR-p = 0.02; right β = -0.20, FDR-p = 0.01) and right medial orbitofrontal (β = -0.17, FDR-p = 0.02). Poorer inhibitory performance associated with higher WMH load in one frontal ROI, the right superior frontal (right β = -0.21, FDR-p = 0.01). There were no significant associations between working memory and WMH in frontal ROIs. Our study demonstrates that location and pattern of frontal WMH may be important to assess when examining age-related differences in cognitive functions involving switching/set-shifting and inhibition. On the other hand, working memory performance was not related to presence of frontal WMH in this sample. These data suggest that WMH may contribute selectively to age-related declines in executive function. Findings emerged beyond predictors known to be associated with WMH presence, including age and cardiovascular disease risk. The spread of WMH within the frontal lobes may play a key role in the neuropsychological profile of cognitive aging. Further research should explore whether early intervention on modifiable vascular factors or cognitive interventions targeted for executive abilities may help mitigate the effect of frontal WMH on executive function.

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Comparing the Performance of Two Radiomic Models to Predict Progression and Progression Speed of White Matter Hyperintensities.

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