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

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A Commentary to this article was published on 28 March 2023

Abstract

Objectives

Post-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).

Methods

Researchers 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.

Results

A 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).

Conclusions

The 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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Data Availability

The raw data supporting the conclusions of this article will be made available by the authors upon request by suitably qualified researchers.

Abbreviations

AI:

Artificial intelligence

CI:

Confidence interval

CT:

Computed tomography

DCA:

Decision curve analysis

GCS:

Glasgow coma scale

GLCM:

Grey-level co-occurrence matrix

GLDM:

Grey-level dependence matrix

GLRLM:

Grey-level run-length matrix

GLSZM:

Grey-level size zone matrix

ICC:

Intraclass correlation coefficient

ICH:

Intracerebral haemorrhage

LASSO:

The least absolute shrinkage and selection operator

MRI:

Magnetic resonance imaging

mRMR:

Minimum redundancy maximum relevance

NGTDM:

Neighbouring grey-tone difference matrix

NIHSS:

National Institute of Health Stroke Scale

OR:

Odds ratio

PET:

Positron emission tomography

PSE:

Post-stroke epilepsy

ROC:

Receiver operating characteristic

ROI:

Region of interest

VIF:

Variance inflation factor

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Acknowledgements

We thank all the authors that contributed to this work.

Funding

This work was supported by National Natural Science Foundation of China (No. 82001363) and Zhejiang Provincial Natural Science Foundation (No. LY22H090016) through Xinshi Wang.

Author information

Author notes

  1. Ru Lin and Jiahe Lin contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China

    Ru Lin, Yaoyao Yu, Mengqi Lin, Fei Yao, Haoli Xu, Mo Zheng & Yunjun Yang

  2. Graduate School, Wenzhou Medical University, Wenzhou, Zhejiang, China

    Ru Lin

  3. Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hunan, China

    Jiahe Lin, Xianxian Li, Jiahe Ye, Shanshan Huang & Suiqiang Zhu

  4. Department of Neurology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China

    Jiahe Lin, Yuchen Xu, Huiqin Xu, Jia Li & Xinshi Wang

  5. Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia

    Emma Foster

  6. Key Laboratory of Alzheimer’s Disease of Zhejiang Province, Institute of Aging, Wenzhou Medical University, Wenzhou, Zhejiang, China

    Huiqin Xu & Xinshi Wang

  7. Suzhou Medical College of Soochow University, Suzhou, 215123, Jiangsu, China

    Haoli Xu

  8. First Clinical School of Medicine, Wenzhou Medical University, Wenzhou, Zhejiang, China

    Mengjia Chen & Han Zheng

  9. Alberta Institute, Wenzhou Medical University, Wenzhou, Zhejiang, China

    Jiayi Chen

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  1. Ru Lin
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Corresponding authors

Correspondence to Suiqiang Zhu, Yunjun Yang or Xinshi Wang.

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Guarantor

The scientific guarantors of this publication are Xinshi Wang (WXS), Yunjun Yang (YYJ), and Suiqiang Zhu (SZ).

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Verbal informed consent was obtained from the subject or subject’s legally authorised representative through tele-interview.

Ethical approval

This study was approved by the Ethics Committee of the First Affiliated Hospital of Wenzhou Medical University and Tongji Hospital affiliated to Tongji Medical College (No. 2020–185 and ChiCTR-ROC-2000039365).

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• retrospective

• case–control study

• multicentre study

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Lin, R., Lin, J., Xu, Y. et al. Development and validation of a novel radiomics-clinical model for predicting post-stroke epilepsy after first-ever intracerebral haemorrhage. Eur Radiol 33, 4526–4536 (2023). https://doi.org/10.1007/s00330-023-09429-y

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