Trial reportDiagnostic and interventional radiology (Ankara, Turkey)2023
Radiomics analysis for predicting malignant cerebral edema in patients undergoing endovascular treatment for acute ischemic stroke
Trial report in Diagnostic and interventional radiology (Ankara, Turkey), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 17 citations in OpenAlex.
- Predictive Value of Machine Learning Models for Cerebral Edema Risk in Stroke Patients: A Meta-Analysis.Brain and behavior · 2025Pooled 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
- Predicting malignant cerebral edema after acute ischemic stroke: a machine-learning model with multi-region radiomics.Quantitative imaging in medicine and surgery · 2025Article
- Leveraging Ensemble Models and Follow-up Data for Accurate Prediction of mRS Scores from Radiomic Features of DSC-PWI Images.Journal of imaging informatics in medicine · 2025Article
- Predicting the efficacy of bevacizumab on peritumoral edema based on imaging features and machine learning.Scientific reports · 2025Article
- Development of a multimodal model combining radiomics and deep learning to predict malignant cerebral edema after endovascular thrombectomy.Frontiers in neurology · 2025Article
- Radiomics signature and deep learning signature of intrathrombus and perithrombus for prediction of malignant cerebral edema after acute ischemic stroke: a multicenter CT study.Frontiers in neurology · 2025Article
- The impracticality of radiomics research in acute ischemic stroke: from the perspective of primary healthcare institutions.Frontiers in neurology · 2025Review
- Advancing ischemic stroke diagnosis and clinical outcome prediction using improved ensemble techniques in DSC-PWI radiomics.Scientific reports · 2024Article
- An integrated nomogram combining clinical and radiomic features of hyperattenuated imaging markers to predict malignant cerebral edema following endovascular thrombectomy.Quantitative imaging in medicine and surgery · 2024Article
- A CT-based machine learning model for using clinical-radiomics to predict malignant cerebral edema after stroke: a two-center study.Frontiers in neuroscience · 2024Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeRadiomics analysis is a promising image analysis technique. This study aims to extract a radiomics signature from baseline computed tomography (CT) to predict malignant cerebral edema (MCE) in patients with acute anterior circulation infarction after endovascular treatment (EVT).
methodsIn this retrospective study, 111 patients underwent EVT for acute ischemic stroke caused by middle cerebral artery (MCA) and/or internal carotid artery occlusion. The participants were randomly divided into two datasets: the training set (n = 77) and the test set (n = 34). The clinico-radiological profiles of all patients were collected, including cranial non-contrast-enhanced CT, CT angiography, and CT perfusion. The MCA territory on non-contrast-enhanced CT images was segmented, and the radiomics features associated with MCE were analyzed. The clinico-radiological parameters related to MCE were also identified. In addition, a routine visual radiological model based on radiological factors and a combined model comprising radiomics features and clinico-radiological factors were constructed to predict MCE.
resultsThe areas under the curve (AUCs) of the radiomics signature for predicting MCE were 0.870 (
conclusionA CT image-based radiomics signature is a promising tool for predicting MCE in patients with acute anterior circulation infarction after EVT. For clinicians, it may assist in diagnostic decision-making.
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