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Non-contrast CT-based radiomic signature for screening thoracic aortic dissections: a multicenter study

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

Abstract

Objective

To develop a non-contrast CT-based radiomic signature to effectively screen for thoracic aortic dissections (ADs).

Methods

We retrospectively enrolled 378 patients who underwent non-contrast chest CT scans along with CT angiography or MRI from 4 medical centers. The training and validation sets were from 3 centers, while the external test set was from a 4th center. Radiomic features were extracted from non-contrast CT images. The radiomic signature was created on the basis of selected features by a logistic regression algorithm. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve, accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were conducted to assess the predictive ability of radiomic signature.

Results

The radiomic signature demonstrated AUCs of 0.91 (95% confidence interval [CI], 0.86–0.95) in the training set, 0.92 (95% CI, 0.86–0.98) in the validation set, and 0.90 (95% CI, 0.82–0.98) in the external test set. The predicted diagnosis was in good agreement with the probability of thoracic AD. In the external test group, the diagnostic accuracy, sensitivity, specificity, PPV, and NPV were 90.5%, 85.7%, 91.7%, 70.6%, and 96.5%, respectively.

Conclusions

A radiomic signature based on non-contrast CT images can effectively predict thoracic ADs. This method may serve as a potential screening tool for thoracic ADs.

Key Points

• The non-contrast CT-based radiomic signature can effectively predict the thoracic aortic dissections.

• This radiomic signature shows better predictive performance compared to the current clinical model.

• This prediction method may be a potential tool for screening thoracic aortic dissections.

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Abbreviations

AD:

Aortic dissections

AUC:

Area under the curve

CI:

Confidence interval

CT:

Computed tomography

ESC:

European Society of Cardiology

GLCM:

Gray-level co-occurrence matrix

ICC:

Interclass correlation coefficients

LASSO:

Least absolute shrinkage and selection operator

MRI:

Magnetic resonance imaging

mRMR:

Max-Relevance and Min-Redundancy

NPV:

Negative predictive value

PPV:

Positive predictive value

Rad-score:

Radiomics score

ROC:

Receiver operating characteristic

ROI:

Region of interest

TOE:

Transesophageal echocardiography

TTE:

Transthoracic echocardiography

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Funding

This study has received funding by the National Natural Science Foundation of China (No. 81971600) and the Zhejiang Provincial Natural Science Foundation of China (LSY19H180003).

Author information

Author notes

  1. Yifan Guo and Xiaojun Chen contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Hangzhou, 310000, China

    Yifan Guo, Maosheng Xu & Zhichao Sun

  2. The First Clinical Medical College of Zhejiang Chinese Medical University, 548 Binwen Road, Hangzhou, 310000, China

    Yifan Guo, Maosheng Xu & Zhichao Sun

  3. Department of Radiology, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, 365 Renmin East Road, Jinhua, 321000, China

    Xiaojun Chen & Jiner Shu

  4. Department of Neurology, The Wenzhou Third Clinical Institute Affiliated To Wenzhou Medical University, 299 Gu’an Road, Wenzhou, 325000, China

    Xianda Lin

  5. Department of Radiology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, 109 Xueyuan West Road, Wenzhou, 325000, China

    Litian Chen & Jianmin Cheng

  6. Department of Pharmaceuticals Diagnosis, GE Healthcare, 122 Shuguang Road, Hangzhou, 310000, China

    Peipei Pang

Authors
  1. Yifan Guo
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  2. Xiaojun Chen
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  4. Litian Chen
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  7. Jianmin Cheng
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  8. Maosheng Xu
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  9. Zhichao Sun
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Corresponding authors

Correspondence to Maosheng Xu or Zhichao Sun.

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Guarantor

The scientific guarantor of this publication is Zhichao Sun.

Conflict of interest

The authors of this manuscript declare relationships with the following companies: GE Healthcare. Peipei Pang contributed to the development of radiomics models described in the study.

Statistics and biometry

One of the authors (Peipei Pang) has significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• Retrospective

• Diagnostic or prognostic study

• Multicenter study

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Zhichao Sun is the first corresponding author and Maosheng Xu is the second corresponding author of this work.

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Cite this article

Guo, Y., Chen, X., Lin, X. et al. Non-contrast CT-based radiomic signature for screening thoracic aortic dissections: a multicenter study. Eur Radiol 31, 7067–7076 (2021). https://doi.org/10.1007/s00330-021-07768-2

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  • DOI: https://doi.org/10.1007/s00330-021-07768-2

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