ArticleBMC medical imaging2020
Noninvasive model for predicting future ischemic strokes in patients with silent lacunar infarction using radiomics.
Article in BMC medical imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 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
- Machine learning increases the prediction of stroke for Chinese hypertensive patients.Frontiers in microbiology · 2026Article
- Application of radiomics in ischemic stroke.The Journal of international medical research · 2024Review
- Feasibility of a clinical-radiomics combined model to predict the occurrence of stroke-associated pneumonia.BMC neurology · 2024Article
- Development and Validation of a Nomogram for Predicting Lacunar Infarction in Patients with Hypertension.International journal of general medicine · 2024Article
- Texture analysis of apparent diffusion coefficient maps in predicting the clinical functional outcomes of acute ischemic stroke.Frontiers in neurology · 2023Article
- Prediction of recurrence of ischemic stroke within 1 year of discharge based on machine learning MRI radiomics.Frontiers in neuroscience · 2023Article
- High serum amyloid A predicts risk of cognitive impairment after lacunar infarction: Development and validation of a nomogram.Frontiers in neurology · 2022Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study aimed to investigate integrating radiomics with clinical factors in cranial computed tomography (CT) to predict ischemic strokes in patients with silent lacunar infarction (SLI).
methodsRadiomic features were extracted from baseline cranial CT images of patients with SLI. A least absolute shrinkage and selection operator (LASSO)-Cox regression analysis was used to select significant prognostic factors based on Model
resultsRadiomic signature (p < 0.01), age (p = 0.09), dyslipidemia (p = 0.03), and multiple infarctions (p = 0.02) were independently associated with future ischemic strokes. Model
conclusionsRadiomic analysis based on baseline CT images may provide a novel approach for predicting future ischemic strokes in patients with SLI. Older patients and those with dyslipidemia or multiple infarctions are at higher risk for ischemic stroke and require close monitoring and intensive intervention.
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