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. 2025 Jul 1;25(1):258.
doi: 10.1186/s12880-025-01792-0.

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

Jing Tong  1   2 , Libo Zhang  3   4 , Guiguang Bei  1   2 , Wenyuan Liu  1   2 , Mingyu Zou  1   2 , Yuze Li  5   6 , Xiaogang Li  1   2 , Yu Sun  1   2 , Xinrui Wang  1   2 , Jingya Zhu  1   2 , Zhenguo Wang  1   2 , Benqiang Yang  1   2
Affiliations

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

Jing Tong et al. BMC Med Imaging. .

Abstract

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.

Keywords: Computed tomography; Coronary slow flow; Epicardial adipose tissue; Radiomics.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: The study protocol was approved by the ethics committee of the General Hospital of Northern Theater Command (Y[2022]013) and the study complied with the Declaration of Helsinki. Informed consent was waived by the ethics committee of the General Hospital of Northern Theater Command because of the retrospective nature of this study. Consent for publication: Not applicable. Competing interests: The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Flow diagram of the participants. PCI percutaneous coronary intervention, PTCA percutaneous transluminal coronary angioplasty, CABG coronary artery bypass grafting, TIMI thrombolysis in myocardial infarction, CSF coronary slow flow, NCF normal coronary flow, BMI body mass index
Fig. 2
Fig. 2
The workflow of radiomics analysis. ROI region of interest, mRMR maximum relevance minimum redundancy, LASSO least absolute shrinkage and selection operator
Fig. 3
Fig. 3
The weights of the radiomics features selected. GLSZM gray level size zone matrix, LHH low high high, GLCM gray level co-occurrence matrix, GLRLM gray level run length matrix, Idm inverse difference moment
Fig. 4
Fig. 4
The ROC curves of the radiomics model for diagnosing CSF. The red curve represents the training set and the black curve represents the validation set
Fig. 5
Fig. 5
The calibration curves of the radiomics model. The blue curve represents the training set and the red curve represents the validation set
Fig. 6
Fig. 6
The decision analysis curves of the radiomics model in the training set (A) and the validation set (B). The gray curved line represents the assumption that all patients are CSF, while the black horizontal line represents the assumption that all patients are NCF
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