Dataset Information

Sensitivity of Diffusion-Weighted STEAM MRI and EPI-DWI to Infratentorial Ischemic Stroke.

ABSTRACT: OBJECTIVES:To assess the sensitivity of stimulated echo acquisition mode diffusion weighted imaging (STEAM-DWI) to ischemic stroke in comparison to echo-planar imaging diffusion weighted imaging (EPI-DWI) in the infratentorial compartment. METHODS:Fifty-seven patients presenting with clinical features of infratentorial stroke underwent STEAM-DWI, high-resolution EPI-DWI (HR-DWI, 2.5 mm slice thickness) and low-resolution EPI-DWI (LR-DWI, 5 mm slice thickness). Four readers assessed the presence of ischemic lesions and artifacts. Agreement between sequences and interobserver agreement on the presence of ischemia were calculated. The sensitivities of the DWI sequences were calculated in 45 patients with a confirmed diagnosis of infratentorial stroke. RESULTS:Median time from symptom onset to imaging was 24 hours. STEAM-DWI agreed with LR-DWI in 89.5% of cases (kappa = 0.72, p<0.0001) and with HR-DWI in 89.5% of cases (kappa = 0.68, p<0.0001). STEAM-DWI showed fewer intraparenchymal artifacts (1/57) than HR-DWI (44/57) and LR-DWI (41/57). Ischemia was visible in 87% of cases for LR-DWI, 93% of cases for HR-DWI, and 89% of cases for STEAM-DWI. Interobserver agreement was good for STEAM-DWI (kappa = 0.62, p<0.0001). CONCLUSIONS:Compared to the best currently available MR sequence for detecting ischemia (HR-DWI), STEAM-DWI shows fewer artifacts and a similar sensitivity to infratentorial stroke.

SUBMITTER: Khalil AA

PROVIDER: S-EPMC4987060 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Sensitivity of Diffusion-Weighted STEAM MRI and EPI-DWI to Infratentorial Ischemic Stroke.

Khalil Ahmed A AA Hohenhaus Marc M Kunze Claudia C Schmidt Wolf W Brunecker Peter P Villringer Kersten K Merboldt Klaus-Dietmar KD Frahm Jens J Fiebach Jochen B JB

PloS one 20160816 8

<h4>Objectives</h4>To assess the sensitivity of stimulated echo acquisition mode diffusion weighted imaging (STEAM-DWI) to ischemic stroke in comparison to echo-planar imaging diffusion weighted imaging (EPI-DWI) in the infratentorial compartment.<h4>Methods</h4>Fifty-seven patients presenting with clinical features of infratentorial stroke underwent STEAM-DWI, high-resolution EPI-DWI (HR-DWI, 2.5 mm slice thickness) and low-resolution EPI-DWI (LR-DWI, 5 mm slice thickness). Four readers assesse ...[more]

PMID: 27529697

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Project description:BackgroundAn accurate prediction of tissue outcome in acute ischemic stroke patients is of high interest for treatment decision making. To date, various machine learning models have been proposed that combine multi-parametric imaging data for this purpose. However, most of these machine learning models were trained using voxel information extracted from the whole brain, without taking differences in susceptibility to ischemia into account that exist between brain regions. The aim of this study was to develop and evaluate a local tissue outcome prediction approach, which makes predictions using locally trained machine learning models and thus accounts for regional differences.Material and methodsMulti-parametric MRI data from 99 acute ischemic stroke patients were used for the development and evaluation of the local tissue outcome prediction approach. Diffusion (ADC) and perfusion parameter maps (CBF, CBV, MTT, Tmax) and corresponding follow-up lesion masks for each patient were registered to the MNI brain atlas. Logistic regression (LR) and random forest (RF) models were trained employing a local approach, which makes predictions using models individually trained for each specific voxel position using the corresponding local data. A global approach, which uses a single model trained using all voxels of the brain, was used for comparison. Tissue outcome predictions resulting from the global and local RF and LR models, as well as a combined (hybrid) approach were quantitatively evaluated and compared using the area under the receiver operating characteristic curve (ROC AUC), the Dice coefficient, and the sensitivity and specificity metrics.ResultsStatistical analysis revealed the highest ROC AUC and Dice values for the hybrid approach. With 0.872 (ROC AUC; LR) and 0.353 (Dice; RF), these values were significantly higher (p < 0.01) than the values of the two other approaches. In addition, the local approach achieved the highest sensitivity of 0.448 (LR). Overall, the hybrid approach was only outperformed in sensitivity (LR) by the local approach and in specificity by both other approaches. However, in these cases the effect sizes were comparatively small.ConclusionThe results of this study suggest that using locally trained machine learning models can lead to better lesion outcome prediction results compared to a single global machine learning model trained using all voxel information independent of the location in the brain.

Project description:PurposeTo investigate radiomics based on DWI (diffusion-weighted imaging) for predicting 1-year ischemic stroke recurrence.MethodsA total of 1,580 ischemic stroke patients were enrolled in this retrospective study conducted from January 2018 to April 2021. Demographic and clinical characteristics were compared between recurrence and non-recurrence groups. On DWI, lesions were segmented using a 2D U-Net automatic segmentation network. Further, radiomics feature extraction was done using the segmented mask matrix on DWI and the corresponding ADC map. Additionally, radiomics features were extracted. The study participants were divided into a training cohort (n = 157, 57 recurrence patients, and 100 non-recurrence patients) and a test cohort (n = 846, 28 recurrence patients, 818 non-recurrence patients). A sparse representation feature selection model was performed to select features. Further classification was accomplished using a recurrent neural network (RNN). The area under the receiver operating characteristic curve values was obtained for model performance.ResultsA total of 1,003 ischemic stroke patients (682 men and 321 women; mean age: 65.90 ± 12.44 years) were included in the final analysis. About 85 patients (8.5%) recurred in 1 year, and patients in the recurrence group were older than the non-recurrence group (P = 0.003). The stroke subtype was significantly different between recurrence and non-recurrence groups, and cardioembolic stroke (11.3%) and large artery atherosclerosis patients (10.3%) showed a higher recurrence percentage (P = 0.005). Secondary prevention after discharge (statins, antiplatelets, and anticoagulants) was found significantly different between the two groups (P = 0.004). The area under the curve (AUC) of clinical-based model and radiomics-based model were 0.675 (95% CI: 0.643-0.707) and 0.779 (95% CI: 0.750-0.807), respectively. With an AUC of 0.847 (95% CI: 0.821-0.870), the model that combined clinical and radiomic characteristics performed better.ConclusionDWI-based radiomics could help to predict 1-year ischemic stroke recurrence.

Dataset Information

Sensitivity of Diffusion-Weighted STEAM MRI and EPI-DWI to Infratentorial Ischemic Stroke.

Publications

Sensitivity of Diffusion-Weighted STEAM MRI and EPI-DWI to Infratentorial Ischemic Stroke.

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