ArticleFrontiers in neurology2024
Machine learning-based nomogram: integrating MRI radiomics and clinical indicators for prognostic assessment in acute ischemic stroke.
Article in Frontiers in neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Radiomics features and clinical factors for predicting restenosis following endovascular therapy in patients with peripheral artery disease.BMC medical informatics and decision making · 2026Article
- Development and validation of a machine learning-based nomogram for survival prediction of patients with hilar cholangiocarcinoma after curative-intent resection.Scientific reports · 2025Article
- Machine learning prediction model for functional prognosis of acute ischemic stroke based on MRI radiomics of white matter hyperintensities.BMC medical imaging · 2025Article
- Progress of radiomics research on white matter hyperintensity lesions.Frontiers in neurology · 2025Review
- The impracticality of radiomics research in acute ischemic stroke: from the perspective of primary healthcare institutions.Frontiers in neurology · 2025Review
- Doctor, what is my risk of bleeding after cardiac surgery while on combined anticoagulant with antiplatelet therapy? A validated nomogram for risk assessment.Frontiers in pharmacology · 2024Article
- Multimodal data fusion of dual-modal DWI-ADC MRI and clinical variables for prognostic prediction in acute ischemic stroke.Frontiers in neurologyArticle
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Authors and funding
12 authors.
Funding
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Abstract
Background: Acute Ischemic Stroke (AIS) remains a leading cause of mortality and disability worldwide. Rapid and precise prognostication of AIS is crucial for optimizing treatment strategies and improving patient outcomes. This study explores the integration of machine learning-derived radiomics signatures from multi-parametric MRI with clinical factors to forecast AIS prognosis. Objective: To develop and validate a nomogram that combines a multi-MRI radiomics signature with clinical factors for predicting the prognosis of AIS. Methods: This retrospective study involved 506 AIS patients from two centers, divided into training (n = 277) and validation ( Results: Key findings highlight coronary heart disease, platelet-to-lymphocyte ratio, uric acid, glucose levels, homocysteine, and radiomics features as independent predictors of AIS outcomes. The clinical-radiomics model achieved a ROC-AUC of 0.940 (95% CI: 0.912-0.969) in the training set and 0.854 (95% CI: 0.781-0.926) in the validation set, underscoring its predictive reliability and clinical utility. Conclusion: The study underscores the efficacy of the clinical-radiomics model in forecasting AIS prognosis, showcasing the pivotal role of artificial intelligence in fostering personalized treatment plans and enhancing patient care. This innovative approach promises to revolutionize AIS management, offering a significant leap toward more individualized and effective healthcare solutions.
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