ArticleFrontiers in neurology2022
Predicting futile recanalization, malignant cerebral edema, and cerebral herniation using intelligible ensemble machine learning following mechanical thrombectomy for acute ischemic stroke.
Article in Frontiers in neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 6 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 6 syntheses or guidelines pooled it, 29 citations in OpenAlex.
- Neuroimaging predictors of malignant brain oedema after thrombectomy in ischemic stroke: a systematic review and meta-analysis.Annals of medicine · 2025Pooled it
- Systematic evaluation of predictive models for futile recanalization before thrombectomy in patients with acute ischemic stroke.Frontiers in neurology · 2025Pooled it
- Predictive Value of Machine Learning Models for Cerebral Edema Risk in Stroke Patients: A Meta-Analysis.Brain and behavior · 2025Pooled it
- Associations between basic physiological observations recorded pre-thrombectomy and functional outcome: a systematic review and meta-analysis.Frontiers in stroke · 2023Pooled it
- Prediction models for post-thrombectomy brain edema in patients with acute ischemic stroke: a systematic review and meta-analysis.Frontiers in neurology · 2023Pooled it
- Risk prediction models for malignant cerebral edema after endovascular therapy in patients with acute anterior circulation large vessel occlusion stroke: a systematic review and meta-analysis.Frontiers in neurologyPooled it
- Automated Diffusion-Weighted MRI Analysis for Exploratory Risk Stratification of Malignant Cerebral Edema After Acute Ischemic Stroke.Diagnostics (Basel, Switzerland) · 2026Article
- Risk factors and predictive model for futile recanalization after mechanical thrombectomy in patients with acute ischemic stroke: A multicenter retrospective study.The Journal of international medical research · 2026Article
- Comparing manual and automated methods for extracting perihematomal features in predicting hemorrhage expansion in deep intracerebral hemorrhage.Scientific reports · 2026Article
- Association of the C-reactive protein-albumin-lymphocyte index with clinical outcomes after mechanical thrombectomy for acute ischemic stroke.Frontiers in aging neuroscience · 2026Article
- Association between hyperglycemia and unfavorable outcome in patients with successful thrombectomy for acute ischemic stroke: a single-center study.BMC neurology · 2025Article
- Predicting malignant cerebral edema after acute ischemic stroke: a machine-learning model with multi-region radiomics.Quantitative imaging in medicine and surgery · 2025Article
- Clinical, radiological, and radiomics feature-based explainable machine learning models for prediction of neurological deterioration and 90-day outcomes in mild intracerebral hemorrhage.BMC medical imaging · 2025Article
- Predicting the efficacy of bevacizumab on peritumoral edema based on imaging features and machine learning.Scientific reports · 2025Article
- Hyponatremia is associated with malignant brain edema after mechanical thrombectomy in acute ischemic stroke.BMC neurology · 2025Article
- Development of a multimodal model combining radiomics and deep learning to predict malignant cerebral edema after endovascular thrombectomy.Frontiers in neurology · 2025Article
- Development and validation of an interpretable machine learning model to predict malignant cerebral edema after endovascular treatment in acute anterior circulation large vessel occlusion stroke.Frontiers in neurology · 2025Article
- Intra-arterial cocktail therapy for patients with anterior circulation large vessel occlusion who achieved endovascular reperfusion.Frontiers in neurology · 2024Article
- Predictive value of the systemic immune-inflammation index for outcomes in large artery occlusion treated with mechanical thrombectomy-a single-center study.Frontiers in neurology · 2024Article
- Role of artificial intelligence in oncologic emergencies: a narrative review.Exploration of targeted anti-tumor therapy · 2023Review
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To 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. Methods: This 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). Results: A 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. Conclusion: Using 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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.