ArticleEuropean radiology2023
Development and validation of a novel radiomics-clinical model for predicting post-stroke epilepsy after first-ever intracerebral haemorrhage.
Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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13 citing papers in PubMed, 2 syntheses or guidelines pooled it, 25 citations in OpenAlex.
- Machine Learning-Based Prediction of Poor Outcomes in Intracerebral Hemorrhage: A Systematic Review and Meta-Analysis.Brain and behavior · 2026Pooled it
- Seizure risk prediction models after intracerebral hemorrhage: a systematic review and meta-analysis.Neurosurgical review · 2026Pooled it
- From Acute Symptomatic Seizures to Post-Stroke Epilepsy: A Narrative Review of Diagnosis and Management.Journal of clinical medicine · 2026Review
- Superlearner can predict in-hospital mortality risk in critically ill patients with ischemic stroke: development and international validation.BMC medical informatics and decision making · 2026Article
- Mediterranean diet adherence and epilepsy: the mediating role of stroke based on a cross-sectional study.European journal of medical research · 2025Article
- Comparisons among radiologist, MR findings and radiomics-clinical models in predicting placenta accreta spectrum disorders: a multicenter study.Archives of gynecology and obstetrics · 2025Article
- The value of radiomics features of white matter hyperintensities in diagnosing cognitive frailty: a study based on T2-FLAIR imaging.BMC medical imaging · 2025Article
- An automatic deep-learning approach for the prediction of post-stroke epilepsy after an initial intracerebral hemorrhage based on non-contrast computed tomography imaging.Quantitative imaging in medicine and surgery · 2025Article
- Prediction of acupuncture efficacy in acute ischemic stroke constructing a clinical-radiomics multimodal model.Frontiers in neurology · 2025Article
- Predictive models for secondary epilepsy in patients with acute ischemic stroke within one year.eLife · 2024Article
- An interpretable artificial intelligence model based on CT for prognosis of intracerebral hemorrhage: a multicenter study.BMC medical imaging · 2024Article
- Predicting long-term outcomes for acute ischemic stroke using multi-model MRI radiomics and clinical variables.Frontiers in medicine · 2024Article
- A machine learning model for predicting post-stroke epilepsy risk by integrating multimodal EEG-fMRI and clinical biomarkers.Frontiers in neurologyArticle
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Authors and funding
20 authors at 3 institutions in 2 countries.
Funding
Abstract
objectivesPost-stroke epilepsy (PSE) is associated with increased morbidity and mortality. This study aimed to develop and validate a novel prediction model combining clinical factors and radiomics features to accurately identify patients at high risk of developing PSE after intracerebral haemorrhage (ICH).
methodsResearchers performed a retrospective medical chart review to extract derivation and validation cohorts of patients with first-ever ICH that attended two tertiary hospitals in China between 2010 and 2020. Clinical data were extracted from electronic medical records and supplemented by tele-interview. Predictive clinical variables were selected by multivariable logistic regression to build the clinical model. Predictive radiomics features were identified, and a Rad-score was calculated according to the coefficient of the selected feature. Both clinical variables and radiomic features were combined to build the radiomics-clinical model. Performances of the clinical, Rad-score, and combined models were compared.
resultsA total of 1571 patients were included in the analysis. Cortical involvement, early seizures within 7 days of ICH, NIHSS score, and ICH volume were included in the clinical model. Rad-score, instead of ICH volume, was included in the combined model. The combined model exhibited better discrimination ability and achieved an overall better benefit against threshold probability than the clinical model in the decision curve analysis (DCA).
conclusionsThe combined radiomics-clinical model was better able to predict ICH-associated PSE compared to the clinical model. This can help clinicians better predict an individual patient's risk of PSE following a first-ever ICH and facilitate earlier PSE diagnosis and treatment. KEY POINTS: • Radiomics has not been used in predicting the risk of developing PSE. • Higher Rad-scores were associated with higher risk of developing PSE. • The combined model showed better performance of PSE prediction ability.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.