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Clot-based radiomics model for cardioembolic stroke prediction with CT imaging before recanalization: a multicenter study

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To develop a clot-based radiomics model using CT imaging radiomic features and machine learning to identify cardioembolic (CE) stroke before mechanical thrombectomy (MTB) in patients with acute ischemic stroke (AIS).

Materials and methods

This retrospective four-center study consecutively included 403 patients with AIS who sequentially underwent CT and MTB between April 2016 and July 2021. These were grouped into training, testing, and external validation cohorts. Thrombus-extracted radiomic features and basic information were gathered to construct a machine learning model to predict CE stroke. The radiological characteristics and basic information were used to build a routine radiological model. A combined radiomics and radiological features model was also developed. The performances of all models were evaluated and compared in the validation cohort. A histological analysis helped further assess the proposed model in all patients.

Results

The radiomics model yielded an area under the curve (AUC) of 0.838 (95% confidence interval [CI], 0.771–0.891) for predicting CE stroke in the validation cohort, significantly higher than the radiological model (AUC, 0.713; 95% CI, 0.636–0.781; p = 0.007) but similar to the combined model (AUC, 0.855; 95% CI, 0.791–0.906; p = 0.14). The thrombus radiomic features achieved stronger correlations with red blood cells (|rmax|, 0.74 vs. 0.32) and fibrin and platelet (|rmax|, 0.68 vs. 0.18) than radiological characteristics.

Conclusion

The proposed CT-based radiomics model could reliably predict CE stroke in AIS, performing better than the routine radiological method.

Key Points

• Admission CT imaging could offer valuable information to identify the acute ischemic stroke source by radiomics analysis.

• The proposed CT imaging–based radiomics model yielded a higher area under the curve (0.838) than the routine radiological method (0.713; p = 0.007).

• Several radiomic features showed significantly stronger correlations with two main thrombus constituents (red blood cells, |r max |, 0.74; fibrin and platelet, |r max |, 0.68) than routine radiological characteristics.

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Abbreviations

AIS:

Acute ischemic stroke

AUC:

Area under the curve

CE:

Cardioembolic

CI:

Confidence interval

CTA:

Computed tomography angiography

DSA:

Digital subtraction angiography

FP:

Fibrin and platelet

MTB:

Mechanical thrombectomy

NCCT:

Non-contrast computed tomography

RBCs:

Red blood cells

WBCs:

White blood cells

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Funding

This work was supported by the National Natural Science Foundation of China (No. 81871329), the New Developing and Frontier Technologies of Shanghai Shen Kang Hospital Development Center (No. SHDC12018117), the Shanghai Municipal Education Commission-Gaofeng Clinical Medicine Grant Support (No. 2016427), the Shanghai Pujiang Program (No. 21PJ1411700), and the Fundamental Research Funds for the Shanghai Sixth People’s Hospital (No. x-2362).

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Author notes

  1. Jingxuan Jiang and Jianyong Wei contributed equally to this work.

Authors and Affiliations

  1. Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, No. 600 Yishan Road, Shanghai, 200233, China

    Jingxuan Jiang, Yueqi Zhu, Liming Wei, Xiaoer Wei, Qianqian Zhao, Zheng Sun, Haiyan Du, Yu Huang, Hui Liu & Yuehua Li

  2. Department of Radiology, Affiliated Hospital of Nantong University, Nantong, 226001, China

    Jingxuan Jiang & Hao Tian

  3. Clinical Research Center, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, 200233, China

    Jianyong Wei

  4. Department of Radiology, Wuxi Second People’s Hospital, Wuxi, 214000, China

    Lei Zhang & Yue Cheng

  5. Department of Radiology, Affiliated No. 1 People’s Hospital of Nantong University, Nantong, 226001, China

    Tianle Wang

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  1. Jingxuan Jiang
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Corresponding author

Correspondence to Yuehua Li.

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The scientific guarantor of this publication is Yuehua Li.

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.

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Informed consent was obtained from all subjects (patients) in this study.

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Institutional Review Board approval was obtained.

Methodology

• Retrospective

• Cross sectional study

• Multicenter study

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Jiang, J., Wei, J., Zhu, Y. et al. Clot-based radiomics model for cardioembolic stroke prediction with CT imaging before recanalization: a multicenter study. Eur Radiol 33, 970–980 (2023). https://doi.org/10.1007/s00330-022-09116-4

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