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Asymmetry of deep medullary veins on susceptibility weighted MRI in patients with acute MCA stroke is associated with poor outcome.

ABSTRACT: Due to its sensitivity to deoxyhemoglobin, susceptibility weighted imaging (SWI) enables the visualization of deep medullary veins (DMV) in patients with acute stroke, which are difficult to depict under physiological circumstances. This study assesses the asymmetric appearance of prominent DMV as an independent predictor for stroke severity and outcome.SWI of 86 patients with acute middle cerebral artery (MCA) stroke were included. A scoring system from 0 (no visible DMV) to 3 (very prominent DMV) was applied for both hemispheres separately. A difference of scores between ipsi- and contralateral side was defined as asymmetric (AMV+). Occurrence of AMV+ was correlated with the National Institute of Health Stroke Scale (NIHSS) Score on admission and discharge, as well as the modified Rankin Scale (mRS) at discharge. Ordinal regression analysis was used to evaluate NIHSS and mRS as predictors of stroke severity, clinical course of disease and outcome.55 patients displayed AMV+ while 31 did not show an asymmetry (AMV-). Median NIHSS on admission was 17 (11-21) in the AMV+ group and 9 (5-15) in the AMV- group (p = 0.001). On discharge median NIHSS was 11 (5-20) for AMV+ and 5 (2-14) for AMV- (p = 0.005). The median mRS at discharge was 4 (3-5) in the AMV+ group and 3 (1-4) in AMV- (p = 0.001). Odds ratio was 3.19 (95% CI: 1.24-8.21) for AMV+ to achieve a higher mRS than AMV- (p = 0.016).The asymmetric appearance of DMV on SWI is a fast and easily evaluable parameter for the prediction of stroke severity and can be used as an additional imaging parameter in patients with acute MCA stroke.

SUBMITTER: Mucke J

PROVIDER: S-EPMC4388537 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Asymmetry of deep medullary veins on susceptibility weighted MRI in patients with acute MCA stroke is associated with poor outcome.

Mucke Johanna J Möhlenbruch Markus M Kickingereder Philipp P Kieslich Pascal J PJ Bäumer Philipp P Gumbinger Christoph C Purrucker Jan J Mundiyanapurath Sibu S Schlemmer Heinz-Peter HP Bendszus Martin M Radbruch Alexander A

PloS one 20150407 4

<h4>Background and purpose</h4>Due to its sensitivity to deoxyhemoglobin, susceptibility weighted imaging (SWI) enables the visualization of deep medullary veins (DMV) in patients with acute stroke, which are difficult to depict under physiological circumstances. This study assesses the asymmetric appearance of prominent DMV as an independent predictor for stroke severity and outcome.<h4>Materials and methods</h4>SWI of 86 patients with acute middle cerebral artery (MCA) stroke were included. A ...[more]

PMID: 25849958

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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.

Dataset Information

Asymmetry of deep medullary veins on susceptibility weighted MRI in patients with acute MCA stroke is associated with poor outcome.

Publications

Asymmetry of deep medullary veins on susceptibility weighted MRI in patients with acute MCA stroke is associated with poor outcome.

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