Dataset Information

Impact of Ischemic Lesion Location on the mRS Score in Patients with Ischemic Stroke: A Voxel-Based Approach.

ABSTRACT: BACKGROUND AND PURPOSE:Previous studies indicated that ischemic lesion volume might be a useful surrogate marker for functional outcome in ischemic stroke but should be considered in the context of lesion location. In contrast to previous studies using the ROI approach, which has several drawbacks, the present study aimed to measure the impact of ischemic lesion location on functional outcome using a more precise voxelwise approach. MATERIALS AND METHODS:Datasets of patients with acute ischemic strokes from the Multicenter Randomized Clinical Trial of Endovascular Therapy for Acute Ischemic Stroke in the Netherlands (MR CLEAN) were used. Primary outcome was functional outcome as assessed by the modified Rankin Scale 3 months after stroke. Ischemic lesion volume was determined on CT scans 3-9 days after stroke. Voxel-based lesion-symptom mapping techniques, including covariates that are known to be associated with functional outcome, were used to determine the impact of ischemic lesion location for outcome. RESULTS:Of the 500 patients in the MR CLEAN trial, 216 were included for analysis. The mean age was 63 years. Lesion-symptom mapping with inclusion of covariates revealed that especially left-hemispheric lesions in the deep periventricular white matter and adjacent internal capsule showed a great influence on functional outcome. CONCLUSIONS:Our study confirms that infarct location has an important impact on functional outcome of patients with stroke and should be considered in prediction models. After we adjusted for covariates, the left-hemispheric corticosubcortical fiber tracts seemed to be of higher functional importance compared with cortical lesions.

SUBMITTER: Ernst M

PROVIDER: S-EPMC7655370 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Impact of Ischemic Lesion Location on the mRS Score in Patients with Ischemic Stroke: A Voxel-Based Approach.

Ernst M M Boers A M M AMM Forkert N D ND Berkhemer O A OA Roos Y B YB Dippel D W J DWJ van der Lugt A A van Oostenbrugge R J RJ van Zwam W H WH Vettorazzi E E Fiehler J J Marquering H A HA Majoie C B L M CBLM Gellissen S S

AJNR. American journal of neuroradiology 20181004 11

<h4>Background and purpose</h4>Previous studies indicated that ischemic lesion volume might be a useful surrogate marker for functional outcome in ischemic stroke but should be considered in the context of lesion location. In contrast to previous studies using the ROI approach, which has several drawbacks, the present study aimed to measure the impact of ischemic lesion location on functional outcome using a more precise voxelwise approach.<h4>Materials and methods</h4>Datasets of patients with ...[more]

PMID: 30287456

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Project description:IntroductionIt is well known that post-stroke depression (PSD) is a psychiatric complication after stroke which leads to worse functional outcome and poorer quality of life. Some risk factors including gender, stroke severity, lesion location, homocysteine (HCY), and so on are associated with PSD. This study aims to further explore the possible relationship between serum levels of HCY and early-onset PSD and the predictive value of HCY combined with stroke characteristics for early-onset PSD.MethodsTwo hundred forty-five patients with acute ischemic stroke who met the criteria were included in this study from March 2015 to March 2017. PSD was diagnosed at 2 weeks after stroke. The severity of depressive symptoms was evaluated with the Hamilton depression scale 17 items (HAMD-17), and patients with HAMD scores ≥7 were included in the PSD group. The demographic data, clinical characteristics, serum levels of HCY, and detailed radiological variables (e.g., lesion location and quantity of the brain infarct) were also examined.ResultsIn total, 97 (39.6%) patients of the 245 patients were diagnosed with depression. The univariate analyses suggested that patients in PSD group had a higher NIHSS score, modified Rankin Scale score, and HCY levels than patients in non-PSD group (p < .001). The patients with PSD had higher proportion of multiple-site acute infarcts and frontal lobe lesion (p < .05). In multivariate logistic regression analysis, NIHSS score at admission, serum levels of HCY, and multiple-site lesions were independently related to early-onset PSD. Based on receiver operating characteristic curves analysis, the combination of HCY, NIHSS scores, multiple-site lesions, and lesion location revealed a highest area under the curve of 0.807 (95% confidence interval [CI]: 0.748-0.865, p < .001). Furthermore, there was a significantly increased risk of early-onset PSD associated with serum levels of HCY ≥16.98 μmol/L (odds ratio [OR] = 10.976, 95% CI: 5.585-21.573, p < .001).ConclusionsOur study indicated that higher NIHSS score, elevated serum levels of HCY, and multiple-site lesions may be independent risk factors of early-onset PSD. The combination of HCY, NIHSS scores, multiple-site lesions, and lesion location may provide greater predictive value than HCY alone for early-onset PSD. Early intervention for elevated serum levels of HCY may be a potential target for the intervention and prevention of PSD.

Project description:BackgroundMagnetic Resonance Spectroscopy (MRS) can measure in vivo brain tissue metabolism that exhibits unique biochemical characteristics in brain tumors. For clinical application, an efficient and versatile quantification method of MRS would be an important tool for medical research, particularly for exploring the scientific problem of tumor monitoring. The objective of our study is to propose an automated MRS quantitative approach and assess the feasibility of this approach for glioma grading, prognosis and boundary detection.MethodsAn automated quantitative approach based on a convex envelope (AQoCE) is proposed in this paper, including preprocessing, convex-envelope based baseline fitting, bias correction, sectional baseline removal, and peak detection, in a total of 5 steps. Some metabolic ratios acquired by this quantification are selected for statistical analysis. An independent sample t-test and the Kruskal-Wallis test are used for distinguishing low-grade gliomas (LGG) and high-grade gliomas (HGG) and for detecting the tumor, peritumoral and contralateral areas, respectively. Seventy-eight cases of pre-operative brain gliomas with pathological reports are included in this study.ResultsCho/NAA, Cho/Cr and Lip-Lac/Cr (LL/Cr) calculated by AQoCE in the tumor area differ significantly between LGG and HGG, with p≤0.005. Using logistic regression combining Cho/NAA, Cho/Cr and LL/Cr to generate a ROC curve, AQoCE achieves a sensitivity of 92.9%, a specificity of 72.2%, and an area under ROC curve (AUC) of 0.860. Moreover, both Cho/NAA and Cho/Cr in the AQoCE approach show a significant difference (p≤0.019) between tumoral, peritumoral, and contralateral areas. The comparison between the results of AQoCE and Siemens MRS processing software are also discussed in this paper.ConclusionsThe AQoCE approach is an automated method of residual water removal and metabolite quantification. It can be applied to multi-voxel 1H-MRS for evaluating brain glioma grading and demonstrating characteristics of brain glioma metabolism. It can also detect infiltration in the peritumoral area. Under the limited clinical data used, AQoCE is significantly more versatile and efficient compared to the reference approach of Siemens.

Project description:PurposeThe aim of this study was to investigate if ischemic stroke final infarction volume and location can be used to predict the associated functional outcome using a multi-class support vector machine (SVM).Material and methodsSixty-eight follow-up MR FLAIR datasets of ischemic stroke patients with known modified Rankin Scale (mRS) functional outcome after 30 days were used. The infarct regions were segmented and used to calculate the percentage of lesioned voxels in the predefined MNI, Harvard-Oxford cortical and subcortical atlas regions as well as using four problem-specific VOIs, which were identified from the database using voxel-based lesion symptom mapping. An overall of 12 SVM classification models for predicting the corresponding mRS score were generated using the lesion overlap values from the different brain region definitions, stroke laterality information, and the optional parameters infarct volume, admission NIHSS, and patient age.ResultsLeave-one-out cross validations revealed that including information about the stroke location in terms of lesion overlap measurements led to improved mRS prediction results compared to classification models not utilizing the stroke location information. Furthermore, integration of the optional features led to improved mRS prediction results in all cases tested. The problem-specific brain regions and additional integration of the optional features led to the best mRS predictions with a precise multi-value mRS prediction accuracy of 56%, sliding window multi-value mRS prediction accuracy (mRS±1) of 82%, and binary mRS (0-2 vs. 3-5) prediction accuracy of 85%.ConclusionTherefore, a graded SVM-based functional stroke outcome prediction using the problem-specific brain regions for lesion overlap quantification leads to promising results but needs to be further validated using an independent database to rule out a potential methodical bias and overfitting effects. The prediction of the graded mRS functional outcome could be a valuable tool if combined with voxel-wise tissue outcome predictions based on multi-parametric datasets acquired at the acute phase.

Dataset Information

Impact of Ischemic Lesion Location on the mRS Score in Patients with Ischemic Stroke: A Voxel-Based Approach.

Publications

Impact of Ischemic Lesion Location on the mRS Score in Patients with Ischemic Stroke: A Voxel-Based Approach.

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