Dataset Information

Diffusion tensor free water MRI predicts progression of FLAIR white matter hyperintensities after ischemic stroke.

ABSTRACT:

Background

The progression of FLAIR white matter hyperintensities (WMHs) on MRI heralds vascular-mediated cognitive decline. Even before FLAIR WMH progression, adjacent normal appearing white matter (NAWM) already demonstrates microstructural deterioration on diffusion tensor imaging (DTI). We hypothesized that elevated DTI free water (FW) would precede FLAIR WMH progression, implicating interstitial fluid accumulation as a key pathological step in the progression of cerebral small vessel disease.

Methods

Participants at least 3 months after an ischemic stroke or TIA with WMH on MRI underwent serial brain MRIs every 3 months over the subsequent year. For each participant, the WMHs were automatically segmented, serial MRIs were aligned, and a region of WMH penumbra tissue at risk was defined by dilating lesions at any time point and subtracting baseline lesions. Penumbra voxels were classified as either stable or progressing to WMH if they were segmented as new lesions and demonstrated increasing FLAIR intensity over time. Aligned DTI images included FW and FW-corrected fractional anisotropy (FA_Tissue) and mean diffusivity (MD_Tissue). Logistic regression and area under the receiver-operator characteristic curve (AUC) were used to test whether baseline DTI predicted voxel-wise classification of stable penumbra or progression to WMH while covarying for clinical risk factors.

Results

In the included participants (n = 26, mean age 71 ± 9 years, 31% female), we detected a median annual voxel-wise WMH growth of 2.9 ± 2.6 ml. Each baseline DTI metric was associated with lesion progression in the penumbra, but FW had the greatest AUC of 0.732 (0.730 - 0.733) for predicting voxel-wise WMH progression pooled across participants.

Discussion

Baseline increased interstitial fluid, estimated as FW on DTI, predicted the progression of NAWM to WMH over the following year. These results implicate the presence of FW in the pathogenesis of cerebral small vessel disease progression.

SUBMITTER: Kern KC

PROVIDER: S-EPMC10559725 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Diffusion tensor free water MRI predicts progression of FLAIR white matter hyperintensities after ischemic stroke.

Kern Kyle C KC Zagzoug Marwah S MS Gottesman Rebecca F RF Wright Clinton B CB Leigh Richard R

Frontiers in neurology 20230922

<h4>Background</h4>The progression of FLAIR white matter hyperintensities (WMHs) on MRI heralds vascular-mediated cognitive decline. Even before FLAIR WMH progression, adjacent normal appearing white matter (NAWM) already demonstrates microstructural deterioration on diffusion tensor imaging (DTI). We hypothesized that elevated DTI free water (FW) would precede FLAIR WMH progression, implicating interstitial fluid accumulation as a key pathological step in the progression of cerebral small vesse ...[more]

PMID: 37808483

Similar Datasets

Project description:ObjectiveThe purpose of the current study is to explore the value of a nomogram that integrates clinical factors and MRI white matter hyperintensities (WMH) radiomics features in predicting the prognosis at 90 days for patients with acute ischemic stroke (AIS).MethodsA total of 202 inpatients with acute anterior circulation ischemic stroke from the Department of Neurology, Xuzhou Central Hospital between September 2023 and March 2024 were retrospectively enrolled. Inpatient clinical data and cranial MRI images were acquired. In this study, the sample was randomly divided into a training cohort comprising 141 cases and a validation cohort of 61 cases in a 7:3 ratio. WMH lesions on fluid-attenuated inversion recovery (FLAIR) sequences were automatically segmented and manually adjusted using Matlab and ITK-SNAP software. The segmentation led to the identification of total white matter hyperintensity (TWMH), periventricular white matter hyperintensity (PWMH), and deep white matter hyperintensity (DWMH). Subsequently, radiomics features were meticulously extracted from these three distinct regions of interest (ROIs). Radiomic models for the three ROIs were developed using six machine learning algorithms. The clinical model was built by identifying clinical risk factors through univariate and multivariate logistic regression analyses. A combined model was subsequently developed incorporating the best radiomics model with significant clinical factors. To illustrate these risk factors, a graphical representation known as a nomogram was devised.ResultsAge, previous stroke history, coronary artery disease, admission blood glucose levels, homocysteine levels, and infarct volume were identified as independent clinical predictors of AIS prognosis. A total of 16, 21, and 22 radiomics features were selected from TWMH, PWMH, and DWMH, respectively. The TWMH radiomics model using the SVM classifier exhibited the best predictive performance for AIS prognosis, achieving a sensitivity of 90.0%, a specificity of 81.3%, an accuracy of 85.3%, and an AUC of 0.916 in the validation set. The combined model outperformed both the clinical and radiomics models, exhibiting exceptional predictive capabilities with a validation cohort sensitivity of 89.3%, specificity of 84.8%, accuracy of 86.9%, and AUC of 0.939.ConclusionThe FLAIR sequence-based WMH radiomics approach demonstrates effective prediction of the 90-day functional prognosis in patients with AIS. The integration of TWMH radiomics and clinical factors in a combined model exhibits superior performance. This innovative model shows potential in aiding clinicians to enhance their assessment of patient prognosis and tailor personalized treatment strategies.Clinical trial numberNot applicable.

Dataset Information

Diffusion tensor free water MRI predicts progression of FLAIR white matter hyperintensities after ischemic stroke.

Background

Methods

Results

Discussion

Publications

Diffusion tensor free water MRI predicts progression of FLAIR white matter hyperintensities after ischemic stroke.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets