ArticleFrontiers in neuroscience2022
Radiomics Nomogram for Predicting Stroke Recurrence in Symptomatic Intracranial Atherosclerotic Stenosis.
Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The performance of machine learning for predicting the recurrent stroke: a systematic review and meta-analysis on 24,350 patients.Acta neurologica Belgica · 2025Pooled it
- Development of a recurrence risk prediction model for intracranial atherosclerotic stroke using HR-VWI combined with radiomics.Neuroradiology · 2026Article
- A hybrid model based on vessel wall magnetic resonance imaging predicts recurrence in posterior circulation ischemic stroke: a multi-institutional study.Journal of neurointerventional surgery · 2026Article
- Parent Artery Disease-Related Stroke: What Is the Impact on Endovascular Treatment? A Narrative Review.Journal of clinical medicine · 2026Review
- A clinical-radiomics nomogram to predict early neurological deterioration in patients with stroke undergoing intravenous thrombolysis.Journal of the Chinese Medical Association : JCMA · 2025Article
- Deep learning network based on high-resolution magnetic resonance vessel wall imaging combined with attention mechanism for predicting stroke recurrence in patients with symptomatic intracranial atherosclerosis.Quantitative imaging in medicine and surgery · 2025Article
- A radiomic model based on 7T intracranial vessel wall imaging for identification of culprit middle cerebral artery plaque associated with subcortical infarctions.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2025Article
- Intracranial stenting with the Neuroform Atlas Stent for symptomatic intracranial atherosclerotic stenosis: a bi-center retrospective analysis including stroke recurrence nomogram.Frontiers in neurology · 2025Article
- [Risk factors of recurrence of acute ischemic stroke and construction of a nomogram model for predicting the recurrence risk based on Lasso Regression].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2024Article
- Feasibility of a clinical-radiomics combined model to predict the occurrence of stroke-associated pneumonia.BMC neurology · 2024Article
- Article
- An interpretable machine learning model for stroke recurrence in patients with symptomatic intracranial atherosclerotic arterial stenosis.Frontiers in neuroscience · 2023Article
- Biomarker study of symptomatic intracranial atherosclerotic stenosis in patients with acute ischemic stroke.Frontiers in neurology · 2023Article
- Prognostic nomogram for the outcomes in acute stroke patients with intravenous thrombolysis.Frontiers in neuroscience · 2022Article
- A CT-based radiomics nomogram for classification of intraparenchymal hyperdense areas in patients with acute ischemic stroke following mechanical thrombectomy treatment.Frontiers in neuroscience · 2022Article
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To develop and validate a radiomics nomogram for predicting stroke recurrence in symptomatic intracranial atherosclerotic stenosis (SICAS). Methods: The data of 156 patients with SICAS were obtained from the hospital database. Those with and without stroke recurrence were identified. The 156 patients were separated into a training cohort ( Results: Diabetes mellitus, plaque burden, and enhancement ratio were independent risk factors for stroke recurrence [odds ratio (OR) = 1.24, 95% confidence interval (CI): 1.04-3.79, Conclusion: The radiomics features were helpful to predict stroke recurrence in patients with SICAS. The nomogram constructed by combining clinical high-risk factors, plaque radiological features, and radiomics features is a reliable tool for the individualized risk assessment of predicting the recurrence of SICAS stroke.
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