Diffusion-weighted imaging (DWI) ischemic volume is related to FLAIR hyperintensity-DWI mismatch and functional outcome after endovascular therapy

Jiang, Liang; Peng, Mingyang; Chen, Huiyou; Geng, Wen; Zhao, Baoliang; Xia, Yin; Chen, Yu‐Chen; Su, Haobo

doi:10.21037/qims.2019.12.05

Cited by 10 publications

(7 citation statements)

References 46 publications

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“…An FVH–DWI mismatch was associated with a smaller initial infarct and greater infarct growth after thrombolysis, even though the final infarcts remained smaller. Jiang et al (2020a) also reported that an FVH–DWI mismatch group had smaller DWI volumes on admission and on follow-up but lower DWI volume growth than a group with no FVH–DWI mismatch.…”

Section: Clinical Applicationsmentioning

confidence: 90%

Fluid-Attenuated Inversion Recovery Vascular Hyperintensity in Cerebrovascular Disease: A Review for Radiologists and Clinicians

Zeng

Chen

Liao

et al. 2021

Front. Aging Neurosci.

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Neuroradiological methods play important roles in neurology, especially in cerebrovascular diseases. Fluid-attenuated inversion recovery (FLAIR) vascular hyperintensity (FVH) is frequently encountered in patients with acute ischemic stroke and significant intracranial arterial stenosis or occlusion. The mechanisms underlying this phenomenon and the clinical implications of FVH have been a matter of debate. FVH is associated with large-vessel occlusion or severe stenosis, as well as impaired hemodynamics. Possible explanations suggested for its appearance include stationary blood and slow antegrade or retrograde filling of the leptomeningeal collateral circulation. However, the prognostic value of the presence of FVH has been controversial. FVH can also be observed in patients with transient ischemic attack (TIA), which may have different pathomechanisms. Its presence can help clinicians to identify patients who have a higher risk of stroke after TIA. In this review article, we aim to describe the mechanism and influencing factors of FVH, as well as its clinical significance in patients with cerebrovascular disease.

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Section: Clinical Applicationsmentioning

confidence: 90%

Fluid-Attenuated Inversion Recovery Vascular Hyperintensity in Cerebrovascular Disease: A Review for Radiologists and Clinicians

Zeng

Chen

Liao

et al. 2021

Front. Aging Neurosci.

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“…DWI shows lesions within a short time (a few hours) after onset (Chalela et al, 2007), which represents the core of ischaemic stroke. Many studies have shown that the volume of stroke is correlated with clinical functional outcome (Jiang et al, 2020). In addition, the boundary between the core area and the penumbra is clear, which is easy to identify and delineate, and the consistency is good.…”

Section: Discussionmentioning

confidence: 99%

Clinical features and FLAIR radiomics nomogram for predicting functional outcomes after thrombolysis in ischaemic stroke

Zhu²,

Zhang³

et al. 2023

Front. Neurosci.

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ObjectiveWe explored whether radiomics features extracted from diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) images can predict the clinical outcome of patients with acute ischaemic stroke. This study was conducted to investigate and validate a radiomics nomogram for predicting acute ischaemic stroke prognosis.MethodsA total of 257 patients with acute ischaemic stroke from three clinical centres were retrospectively assessed from February 2019 to July 2022. According to the modified Rankin scale (mRS) at 3 months, the patients were divided into a favourable outcome group (mRS of 0–2) and an unfavourable outcome group (mRS of 3−6). The high-throughput features from the regions of interest (ROIs) within the radiologist-drawn contour by AK software were extracted. We used two feature selection methods, minimum redundancy and maximum (mRMR) and the least absolute shrinkage and selection operator algorithm (LASSO), to select the features. Three radiomics models (DWI, FLAIR, and DWI-FLAIR) were established. A radiomics nomogram with patient characteristics and radiomics signature was built using a multivariate logistic regression model. The performance of the nomogram was evaluated in the test and validation sets. Ultimately, decision curve analysis was implemented to assess the clinical value of the nomogram.ResultsThe FLAIR, DWI, and DWI-FLAIR radiomics model exhibited good prediction performance, with area under the curve (AUCs) of 0.922 (95% CI: 0.876−0.968), 0.875 (95% CI: 0.815−0.935), and 0.895 (95% CI: 0.840−0.950). The radiomics nomogram with clinical characteristics including the overall cerebral small vessel disease (CSVD) burden score, hemorrhagic transformation (HT) and admission National Institutes of Health Stroke Scale score (NIHSS) score and the FLAIR Radscore presented good discriminatory potential in the training set (AUC = 0.94; 95% CI: 0.90−0.98) and test set (AUC = 0.94; 95% CI: 0.87−1), which was validated in the validation set 1 (AUC = 0.95; 95% CI: 0.88−1) and validation set 2 (AUC = 0.90; 95% CI: 0.768−1). In addition, it demonstrated good calibration, and decision curve analysis confirmed the clinical value of this nomogram.ConclusionThis non-invasive clinical-FLIAR radiomics nomogram shows good performance in predicting ischaemic stroke prognosis after thrombolysis.

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“…Some studies also suggested that the functional outcomes of patients with LAA were worse than other TOAST criteria, which may be related to inflammation and metabolic response ( 38 , 39 ). Moreover, previous studies have shown that for patients with AIS, large infarct volume, poor collateral circulation, and high NIHSS score are significant predictors of functional outcomes and indicators of the severity of the neurological injury ( 6 – 8 ). However, unlike in other studies, our results show that the symptom onset time and interval from puncture to recanalization did not play a particularly strong role in predicting futile recanalization ( 12 , 40 ).…”

Section: Discussionmentioning

confidence: 99%

“…The incidence of futile recanalization after MT is approximately 49-67% (5). The primary risk factors for patients with AIS include large infarct volume, poor collateral circulation, and high National Institutes of Health Stroke Scale (NIHSS) score (6)(7)(8). While the mRS and NIHSS scores are among the methods used to evaluate AIS functional outcomes, few studies have focused on the functional outcomes and potentially lethal complications in patients with AIS who have undergone an MT and for whom recanalization was achieved.…”

Section: Introductionmentioning

confidence: 99%

Predicting futile recanalization, malignant cerebral edema, and cerebral herniation using intelligible ensemble machine learning following mechanical thrombectomy for acute ischemic stroke

Zeng

Li²,

Huang³

et al. 2022

Front. Neurol.

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PurposeTo establish an ensemble machine learning (ML) model for predicting the risk of futile recanalization, malignant cerebral edema (MCE), and cerebral herniation (CH) in patients with acute ischemic stroke (AIS) who underwent mechanical thrombectomy (MT) and recanalization.MethodsThis prospective study included 110 patients with premorbid mRS ≤ 2 who met the inclusion criteria. Futile recanalization was defined as a 90-day modified Rankin Scale score >2. Clinical and imaging data were used to construct five ML models that were fused into a logistic regression algorithm using the stacking method (LR-Stacking). We added the Shapley Additive Explanation method to display crucial factors and explain the decision process of models for each patient. Prediction performances were compared using area under the receiver operating characteristic curve (AUC), F1-score, and decision curve analysis (DCA).ResultsA total of 61 patients (55.5%) experienced futile recanalization, and 34 (30.9%) and 22 (20.0%) patients developed MCE and CH, respectively. In test set, the AUCs for the LR-Stacking model were 0.949, 0.885, and 0.904 for the three outcomes mentioned above. The F1-scores were 0.882, 0.895, and 0.909, respectively. The DCA showed that the LR-Stacking model provided more net benefits for predicting MCE and CH. The most important factors were the hypodensity volume and proportion in the corresponding vascular supply area.ConclusionUsing the ensemble ML model to analyze the clinical and imaging data of AIS patients with successful recanalization at admission and within 24 h after MT allowed for accurately predicting the risks of futile recanalization, MCE, and CH.

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Diffusion-weighted imaging (DWI) ischemic volume is related to FLAIR hyperintensity-DWI mismatch and functional outcome after endovascular therapy

Cited by 10 publications

References 46 publications

Fluid-Attenuated Inversion Recovery Vascular Hyperintensity in Cerebrovascular Disease: A Review for Radiologists and Clinicians

Fluid-Attenuated Inversion Recovery Vascular Hyperintensity in Cerebrovascular Disease: A Review for Radiologists and Clinicians

Clinical features and FLAIR radiomics nomogram for predicting functional outcomes after thrombolysis in ischaemic stroke

Predicting futile recanalization, malignant cerebral edema, and cerebral herniation using intelligible ensemble machine learning following mechanical thrombectomy for acute ischemic stroke

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