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Background <p>At present, the diagnosis of coronary slow flow (CSF) relies on coronary angiography, and non-invasive imaging examinations for the diagnosis of CSF have not been fully studied. This study aimed to explore the value of diagnosing CSF based on epicardial adipose tissue (EAT) radiomics in chest computed tomography (CT).</p> Methods <p>This retrospective study included 211 patients who underwent coronary angiography showing coronary artery stenosis &lt; 40% from January 2020 to December 2021 and underwent chest CT within 2&#xa0;weeks before angiography. According to the thrombolysis in myocardial infarction flow grade, the patients were divided into CSF group (n = 103) and normal coronary flow group (n = 108). Establish an automatic method for segmenting EAT on chest CT images. Patients were randomly divided into a training set (n = 148) and a validation set (n = 63) at a ratio of 7:3, and then radiomics features were extracted. Features selected using the maximum relevance minimum redundancy and the least absolute shrinkage and selection operator were adopted to construct an EAT radiomics model. The diagnostic efficacy of the model for CSF was evaluated using the area under the receiver operating characteristic curve. The consistency between the model and the actual results was evaluated using calibration curves, and the clinical application value of the model was evaluated using decision curve analysis.</p> Results <p>16 radiomics features were retained to establish an EAT radiomics model for diagnosing CSF. The model had an AUC of 0.81, sensitivity of 0.72, specificity of 0.79, and accuracy of 0.76 for diagnosing CSF in the training set, and an AUC of 0.77, sensitivity of 0.82, specificity of 0.71, and accuracy of 0.77 in the validation set. The calibration curves showed good consistency between the model and the actual results, while the decision analysis curves showed good overall net benefits of the model within most reasonable threshold probability ranges.</p> Conclusions <p>The EAT radiomics model based on chest CT had good diagnostic efficacy for CSF and may become a potential non-invasive tool for diagnosing CSF.</p>

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The value of diagnosing coronary slow flow based on epicardial adipose tissue radiomics in chest computed tomography

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月1日

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  • Jing Tong,
  • Libo Zhang,
  • Guiguang Bei,
  • Wenyuan Liu,
  • Mingyu Zou,
  • Yuze Li,
  • Xiaogang Li,
  • Yu Sun,
  • Xinrui Wang,
  • Jingya Zhu,
  • Zhenguo Wang,
  • Benqiang Yang

摘要

Background

At present, the diagnosis of coronary slow flow (CSF) relies on coronary angiography, and non-invasive imaging examinations for the diagnosis of CSF have not been fully studied. This study aimed to explore the value of diagnosing CSF based on epicardial adipose tissue (EAT) radiomics in chest computed tomography (CT).

Methods

This retrospective study included 211 patients who underwent coronary angiography showing coronary artery stenosis < 40% from January 2020 to December 2021 and underwent chest CT within 2 weeks before angiography. According to the thrombolysis in myocardial infarction flow grade, the patients were divided into CSF group (n = 103) and normal coronary flow group (n = 108). Establish an automatic method for segmenting EAT on chest CT images. Patients were randomly divided into a training set (n = 148) and a validation set (n = 63) at a ratio of 7:3, and then radiomics features were extracted. Features selected using the maximum relevance minimum redundancy and the least absolute shrinkage and selection operator were adopted to construct an EAT radiomics model. The diagnostic efficacy of the model for CSF was evaluated using the area under the receiver operating characteristic curve. The consistency between the model and the actual results was evaluated using calibration curves, and the clinical application value of the model was evaluated using decision curve analysis.

Results

16 radiomics features were retained to establish an EAT radiomics model for diagnosing CSF. The model had an AUC of 0.81, sensitivity of 0.72, specificity of 0.79, and accuracy of 0.76 for diagnosing CSF in the training set, and an AUC of 0.77, sensitivity of 0.82, specificity of 0.71, and accuracy of 0.77 in the validation set. The calibration curves showed good consistency between the model and the actual results, while the decision analysis curves showed good overall net benefits of the model within most reasonable threshold probability ranges.

Conclusions

The EAT radiomics model based on chest CT had good diagnostic efficacy for CSF and may become a potential non-invasive tool for diagnosing CSF.