Development and validation of a novel radiomics-clinical model for predicting post-stroke epilepsy after first-ever intracerebral haemorrhage

Lin, Ru; Lin, Jiahe; Xu, Yuchen; Yu, Yaoyao; Foster, Emma; Lin, Mengqi; Xu, Huiqin; Li, Xianxian; Ye, Jiahe; Yao, Fei; Xu, Haoli; Chen, Mengjia; Chen, Jiayi; Zheng, Han; Li, Jia; Zheng, Mo; Huang, Shanshan; Zhu, Suiqiang; Yang, Yunjun; Wang, Xinshi

doi:10.1007/s00330-023-09429-y

Cited by 11 publications

(4 citation statements)

References 39 publications

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“…Alain et al found that cortical infarction was more likely to result in epilepsy in patients hospitalized with anterior circulation ischemic stroke [23]. Lin et al found that factors such as cortical involvement and intracerebral hemorrhage volume increased the likelihood of PSE, which is consistent with our research findings [8]. Al-Sahli et al also suggested that cortical brain injury and large-area lesions increased the risk of PSE [5][21].…”

Section: Discussionsupporting

confidence: 90%

“…However, subsequent studies have raised doubts regarding the application of radiomics, suggesting the need for further research [9]. Overall, there is still a relative scarcity of research on PSE prediction, with most studies focusing on the analysis of specific or certain risk factors [10][11][8][12]. No study has proposed or established a more comprehensive and scientifically accurate prediction model.…”

Section: Introductionmentioning

confidence: 99%

“…Last year, Lin et al developed a model based on radiomics that outperformed the conventional clinical model in predicting PSE related to intracerebral hemorrhage (ICH). They suggested that a combined radiomics-clinical model could better assist clinicians in assessing the individual risk of PSE after the first occurrence of ICH and facilitate early diagnosis and treatment of PSE [8]. However, subsequent studies have raised doubts regarding the application of radiomics, suggesting the need for further research [9].…”

Section: Introductionmentioning

confidence: 99%

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Predictive Models for Secondary Epilepsy in Patients with Acute Ischemic Stroke Within One Year

Jinxin,

Haoyue,

Yanglingxi

et al. 2024

Preprint

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ObjectivePost-stroke epilepsy (PSE) is a significant complication that has a negative impact on the prognosis and quality of life of ischemic stroke patients. We collected medical records from multiple hospitals and created an interpretable machine learning model for prediction.MethodsWe collected medical records, imaging reports, and laboratory tests from 21459 patients with a history of ischemic stroke in several hospitals. We conducted traditional univariable and multivariable statistics analyses to compare and identify important features. Then the data was divided into a 70% training set and a 30% testing set. We employed the Synthetic Minority Oversampling Technique method to augment the positive class in the training set. Nine commonly used methods were used to build machine learning models, and relevant prediction metrics were compared to select the best-performing model. Finally, we used SHAP(SHapley Additive exPlanations) for model interpretability analysis, assessing the contribution and clinical significance of different features to the prediction.ResultsIn the traditional regression analysis, complications such as hydrocephalus, cerebral hernia, uremia, deep vein thrombosis; significant brain regions included the involvement of the cortical regions including frontal lobe, parietal lobe, occipital lobe, temporal lobe, subcortical region of basal ganglia, thalamus and so on contributed to PSE. General features such as age, gender, and NIHSS(the National Institutes of Health Stroke Scale) score, as well as laboratory indicators including WBC count, D-dimer, lactate, HbA1c and so on were associated with a higher likelihood of PSE. Patients with conditions such as fatty liver, coronary heart disease, hyperlipidemia, and low HDL had a higher likelihood of developing PSE. The machine learning models, particularly tree models such as Random Forest, XGBoost, and LightGBM, demonstrated good predictive performance with an AUC of 0.99.ConclusionThe model built on a large dataset can effectively predict the likelihood of PSE, with tree-based models performing the best. The NIHSS score, WBC count and d-dimer were found to have the greatest impact.

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Section: Discussionsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predictive Models for Secondary Epilepsy in Patients with Acute Ischemic Stroke Within One Year

Jinxin,

Haoyue,

Yanglingxi

et al. 2024

Preprint

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“…There are studies on stroke onset time estimation with radiomics [174]. Finally, some studies are performed to train the radiomic models to predict post-stroke events such as recurrent stroke or epilepsy [175,176,177].…”

Section: Strokementioning

confidence: 99%

Quantitative neuroimaging with handcrafted and deep radiomics in neurological diseases

Lavrova

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Editorial comment on “Development and validation of a novel radiomics-clinical model for predicting post-stroke epilepsy after first-ever intracerebral haemorrhage”

Pszczółkowski

Law

2023

Eur Radiol

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Development and validation of a novel radiomics-clinical model for predicting post-stroke epilepsy after first-ever intracerebral haemorrhage

Cited by 11 publications

References 39 publications

Predictive Models for Secondary Epilepsy in Patients with Acute Ischemic Stroke Within One Year

Predictive Models for Secondary Epilepsy in Patients with Acute Ischemic Stroke Within One Year

Quantitative neuroimaging with handcrafted and deep radiomics in neurological diseases

Editorial comment on “Development and validation of a novel radiomics-clinical model for predicting post-stroke epilepsy after first-ever intracerebral haemorrhage”

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