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.
Highlights
MATERIALS AND METHODSWhite matter hyperintensity (WMH) is a common feature found in the periventricular and deep white matter of the elderly (Longstreth et al, 2005)
This study revealed the pattern of WMH progression, which was found to extend from the focus to the penumbra
Previous studies have suggested that the fluid-attenuated inversion recovery (FLAIR) intensity of a single voxel assists diffusion tensor imaging (DTI) in predicting WMH progression, indicating that the intensity of an individual voxel in conventional Magnetic resonance imaging (MRI) possibly contains influential information regarding the integrity of the white matter (Maillard et al, 2013)
Summary
MATERIALS AND METHODSWhite matter hyperintensity (WMH) is a common feature found in the periventricular and deep white matter of the elderly (Longstreth et al, 2005). This study revealed the pattern of WMH progression, which was found to extend from the focus to the penumbra. Our study was aimed at comparing the performance of two radiomic models to predict the progression and progression speed of white matter hyperintensities. Previous studies have suggested that the FLAIR intensity of a single voxel assists DTI in predicting WMH progression, indicating that the intensity of an individual voxel in conventional Magnetic resonance imaging (MRI) possibly contains influential information regarding the integrity of the white matter (Maillard et al, 2013). Our previous research found that radiomic findings could reflect the heterogeneity and complexity of the white matter, which may result from less uniform MRI signal intensities caused by reduced myelin content or increased water content There have been no studies comparing the predictive power of the WBWM and penumbra
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