ArticleThe British journal of radiology2023
Prediction of hemorrhagic transformation via pre-treatment CT radiomics in acute ischemic stroke patients receiving endovascular therapy.
Article in The British journal of radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 4 of them syntheses that pooled it.
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Who cites it
10 citing papers in PubMed, 4 syntheses or guidelines pooled it, 14 citations in OpenAlex.
- Predicting hemorrhagic transformation in acute ischemic stroke: a systematic review, meta-analysis, and methodological quality assessment of CT/MRI-based deep learning and radiomics models.Emergency radiology · 2025Pooled it
- Early Predictive Accuracy of Machine Learning for Hemorrhagic Transformation in Acute Ischemic Stroke: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Traditional and machine learning models for predicting haemorrhagic transformation in ischaemic stroke: a systematic review and meta-analysis.Systematic reviews · 2025Pooled it
- Machine learning and deep learning algorithms in stroke medicine: a systematic review of hemorrhagic transformation prediction models.Journal of neurology · 2024Pooled it
- Prediction of hemorrhagic transformation in acute ischemic stroke patients using clinico-radiomics models.Scientific reports · 2025Article
- Delta radiomics modeling based on CTP for predicting hemorrhagic transformation after intravenous thrombolysis in acute cerebral infarction: an 8-year retrospective pilot study.Frontiers in neurology · 2025Article
- An Explainable Two-Stage Machine Learning Model for Predicting the Post-Thrombolysis Complications in Stroke Patients: A Multi-Center Study.Research (Washington, D.C.) · 2025Article
- The impracticality of radiomics research in acute ischemic stroke: from the perspective of primary healthcare institutions.Frontiers in neurology · 2025Review
- The value of radiomics-based hyperdense middle cerebral artery sign in predicting hemorrhagic transformation in acute ischemic stroke patients undergoing endovascular treatment.Frontiers in neurology · 2024Article
- Radiomic Feature Extraction Based on Computed Tomography for Stroke Patients.Journal of medical physicsArticle
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study aimed to extract radiomics features (RFs) from pre-treatment CT scans in patients with acute ischemic stroke (AIS), and to establish a radiomics model to predict hemorrhagic transformation (HT) after endovascular therapy (EVT).
methodsA total of 105 patients who were diagnosed with AIS [with occlusion of the M1 segment of the middle cerebral artery (MCA) and/or internal carotid artery] and received EVT were enrolled. They were randomly divided into the development cohort (
resultsThe sensitivity, specificity, and area under the curve of the CT radiomics model for predicting HT after EVT based on pre-treatment CT RFs was 0.806, 0.649, and 0.781 (95% confidence interval (CI): 0.675-0.886), respectively, in the development cohort. The sensitivity, specificity, and area under the curve in the validation cohort was 0.625, 0.875, and 0.797 (95% CI: 0.642-0.951), respectively.
conclusionCT radiomics analysis is a valuable tool for predicting HT in AIS patients receiving EVT. It may guide the selection of patients in practice and improve procedural safety and effectiveness. ADVANCES IN KNOWLEDGE: Identification of the importance of pre-treatment CT radiomics in the prediction of HT in AIS patients after EVT.
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