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Identification of high-risk intracranial plaques with 3D high-resolution magnetic resonance imaging-based radiomics and machine learning

Journal of Neurology volume 269, pages 6494–6503 (2022)Cite this article

Abstract

Background

Identifying high-risk intracranial plaques is significant for the treatment and prevention of stroke.

Objective

To develop a high-risk plaque model using three-dimensional (3D) high-resolution magnetic resonance imaging (HRMRI) based radiomics features and machine learning.

Methods

136 patients with documented symptomatic intracranial artery stenosis and available HRMRI data were included. Among these patients, 136 and 92 plaques were identified as symptomatic and asymptomatic plaques, respectively. A conventional model was developed by recording and quantifying the radiological plaque characteristics. Radiomics features from T1-weighted images (T1WI) and contrast-enhanced T1WI (CE-T1WI) were used to construct a high-risk plaque model with linear support vector classification (linear SVC). The radiological and radiomics features were combined to build a combined model. Receiver operating characteristic (ROC) curves were used to evaluate these models.

Results

Plaque length, burden, and enhancement were independently associated with clinical symptoms and were included in the conventional model, which had an AUC of 0.853 vs. 0.837 in the training and test sets. While the radiomics and the combined model showed an improved AUC: 0.923 vs. 0.925 for the training sets and 0.906 vs. 0.903 in the test sets. Both the radiomics model (p = 0.024, p = 0.018) and combined model (p = 0.042, p = 0.049) outperformed the conventional model in the two sets, whereas the performance of the combined model was not significantly different from that of the radiomics model in the two sets (p = 0.583 and p = 0.606).

Conclusion

The radiomics model based on 3D HRMRI can accurately differentiate symptomatic from asymptomatic intracranial arterial plaques and significantly outperforms the conventional model.

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Funding

This research was funded by the National Key R&D Program of China (Grant Nos 2020AAA0109505).

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Author notes

  1. Hongxia Li and Jia Liu contributed equally to this work.

Authors and Affiliations

  1. Department of Medical Imaging, The First School of Clinical Medicine, Jinling Hospital, Southern Medical University, Nanjing, 210002, Jiangsu, China

    Hongxia Li, Jia Liu, Yingle Li & Guangming Lu

  2. Department of Medical Imaging, Xuzhou Medical University, Xuzhou, Jiangsu, China

    Zheng Dong

  3. Department of Research Collaboration, R&D Center, Beijing Deepwise and League of PHD Technology Co., Ltd, Beijing, 100081, China

    Xingzhi Chen & Chencui Huang

  4. Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China

    Changsheng Zhou, Quanhui Liu, Xiaoqin Su & Xiaoqing Cheng

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  1. Hongxia Li
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  2. Jia Liu
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  3. Zheng Dong
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Contributions

Guarantors of integrity of entire study: HL, JL, GL, XC; study concepts/study design or data acquisition or data analysis/interpretation: HL, JL, GL, XC, ZD, XC, CZ, YL, CH, QL, XS; manuscript drafting or manuscript revision for important intellectual content: HL, JL, ZD, XC, CZ, YL, CH, QL, XS, GL, XC; approval of final version of submitted manuscript: HL, JL, GL, XC, ZD, XC, CZ, YL, CH, QL, XS.

Corresponding authors

Correspondence to Xiaoqing Cheng or Guangming Lu.

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All procedures performed in this study were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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Owing to the retrospective nature of the study, patients’ written informed consent was not required for this study.

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Li, H., Liu, J., Dong, Z. et al. Identification of high-risk intracranial plaques with 3D high-resolution magnetic resonance imaging-based radiomics and machine learning. J Neurol 269, 6494–6503 (2022). https://doi.org/10.1007/s00415-022-11315-4

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  • DOI: https://doi.org/10.1007/s00415-022-11315-4

Keywords

  • Intracranial atherosclerotic disease
  • High-resolution magnetic resonance imaging
  • Stroke
  • Radiomics
  • Machine learning