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Predicting Chronic Myocardial Ischemia Using CCTA-Based Radiomics Machine Learning Nomogram

  • ORIGINAL ARTICLE
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Journal of Nuclear Cardiology Aims and scope

Abstract

Background

Coronary computed tomography angiography (CCTA) is a well-established non-invasive diagnostic test for the assessment of coronary artery diseases (CAD). CCTA not only provides information on luminal stenosis but also permits non-invasive assessment and quantitative measurement of stenosis based on radiomics.

Purpose

This study is aimed to develop and validate a CT-based radiomics machine learning for predicting chronic myocardial ischemia (MIS).

Methods

CCTA and SPECT-myocardial perfusion imaging (MPI) of 154 patients with CAD were retrospectively analyzed and 94 patients were diagnosed with MIS. The patients were randomly divided into two sets: training (n = 107) and test (n = 47). Features were extracted for each CCTA cross-sectional image to identify myocardial segments. Multivariate logistic regression was used to establish a radiomics signature after feature dimension reduction. Finally, the radiomics nomogram was built based on a predictive model of MIS which in turn was constructed by machine learning combined with the clinically related factors. We then validated the model using data from 49 CAD patients and included 18 MIS patients from another medical center. The receiver operating characteristic curve evaluated the diagnostic accuracy of the nomogram based on the training set and was validated by the test and validation set. Decision curve analysis (DCA) was used to validate the clinical practicability of the nomogram.

Results

The accuracy of the nomogram for the prediction of MIS in the training, test and validation sets was 0.839, 0.832, and 0.816, respectively. The diagnosis accuracy of the nomogram, signature, and vascular stenosis were 0.824, 0.736 and 0.708, respectively. A significant difference in the number of patients with MIS between the high and low-risk groups was identified based on the nomogram (P < .05). The DCA curve demonstrated that the nomogram was clinically feasible.

Conclusion

The radiomics nomogram constructed based on the image of CCTA act as a non-invasive tool for predicting MIS that helps to identify high-risk patients with coronary artery disease.

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Disclosures

None.

Author information

Author notes

  1. Zhen-Yu Shu and Si-Jia Cui contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital of Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, Zhejiang, China

    Zhen-Yu Shu MD, Yu-Yun Xu MD, Xiang-Yang Gong MD, PhD & Qin-Yang Jin MD

  2. Second Clinical College, Zhejiang Chinese Medical University, Hangzhou, China

    Si-Jia Cui MD

  3. Department of Radiology, Shao-Yifu Hospital Affiliated to Zhejiang University, Hangzhou, China

    Yue-Qiao Zhang MD

  4. Division of Neuroradiology, Department of Radiology, University of North Carolina School of Medicine, Chapel Hill, NC, USA

    Shng-Che Hung MD

  5. Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Shng-Che Hung MD

  6. Department of Nuclear Medicine, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital of Hangzhou Medical College, Hangzhou, China

    Li-Ping Fu MD

  7. GE Healthcare China, Shanghai, China

    Pei-Pei Pang MD

  8. Institute of Artificial Intelligence and Remote Imaging, Hangzhou Medical College, Hangzhou, China

    Xiang-Yang Gong MD, PhD

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Correspondence to Xiang-Yang Gong MD, PhD or Qin-Yang Jin MD.

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Shu, ZY., Cui, SJ., Zhang, YQ. et al. Predicting Chronic Myocardial Ischemia Using CCTA-Based Radiomics Machine Learning Nomogram. J. Nucl. Cardiol. 29, 262–274 (2022). https://doi.org/10.1007/s12350-020-02204-2

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  • DOI: https://doi.org/10.1007/s12350-020-02204-2

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