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Radiomics features of pericoronary adipose tissue improve CT-FFR performance in predicting hemodynamically significant coronary artery stenosis

  • Cardiac
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To evaluate the value of radiomics-based model of pericoronary adipose tissue (PCAT) combined with CT fractional flow reserve (CT-FFR) in predicting hemodynamically significant coronary stenosis.

Methods

Patients with suspected or known coronary artery disease, who had coronary computed tomography angiography (CCTA), invasive coronary angiography (ICA), and FFR within 1 month, were retrospectively included. Radiomics features of lesion-based PCAT were extracted. The lesion-specific CT-FFR values, CCTA-derived diameter stenosis, lesion length, and PCAT attenuation were also measured. FFR values were used as the reference standard to assess the diagnostic performance of radiomics model, CT-FFR, and combined model for detection of flow-limiting stenosis.

Results

A total of 146 patients with 180 lesions were included in the study. All lesions were divided into training and validation cohorts at a ratio of 2:1. CT-FFR model exhibited the highest area under the curve (AUC) (0.803 for training, 0.791 for validation) in predicting hemodynamically significant stenosis, followed by radiomics model (0.776 for training, 0.744 for validation). However, no statistically significant difference was found between the AUCs of the above two models (p > 0.05). When CT-FFR was combined with radiomics model, the AUC reached 0.900 for training cohort and 0.875 for validation cohort, which were significantly higher than that of CT-FFR and radiomics model alone (both p < 0.05).

Conclusion

The diagnostic performance of PCAT radiomics model was comparable to that of CT-FFR for identification of ischemic coronary stenosis. Adding PCAT radiomics model to CT-FFR showed incremental value in discriminating flow-limiting from non-flow-limiting lesions.

Key Points

• Radiomics analysis of lesion-based PCAT is potentially an alternative method to identify hemodynamic significance of coronary artery stenosis.

• Adding radiomics model of PCAT to CT-FFR improved diagnostic performance for the detection of flow-limiting coronary stenosis.

• Radiomics features + CT-FFR is a promising noninvasive method for comprehensive evaluation of hemodynamic significance of coronary artery stenosis.

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Abbreviations

CAD:

Coronary artery disease

CCTA:

Coronary computed tomography angiography

CT:

Computed tomography

DS:

Diameter stenosis

FFR:

Fractional flow reserve

ICA:

Invasive coronary angiography

MACE:

Major adverse cardiac events

ML:

Machine learning

PCAT:

Pericoronary adipose tissue

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Funding

This study is supported by The National Key Research and Development Program of China (Grant No.: 2021YFF0501402), Shenkang 3-year project of clinical innovation (Grant No.: SHDC2022CRD016), Shanghai Committee of Science and Technology (Grant No.: 21ZR1452200), and Shanghai Municipal Education Commission-Gaofeng Clinical Medicine Grant Support (Grant No.: 20161428).

Author information

Author notes

  1. Lihua Yu and Xiuyu Chen contributed equally to this study.

Authors and Affiliations

  1. Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, #85 Wujin Rd, Shanghai, 200080, China

    Lihua Yu, Yarong Yu & Jiayin Zhang

  2. Department of Magnetic Resonance Imaging, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Centre for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

    Xiuyu Chen

  3. Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, #600, Yishan Rd, Shanghai, China

    Runjianya Ling

  4. Department of Cardiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, #85 Wujin Rd, Shanghai, China

    Wenyi Yang

  5. Digital Solution, Central Research Institute, United Imaging Healthcare, Shanghai, China

    Jianqing Sun

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  1. Lihua Yu
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Correspondence to Jiayin Zhang.

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The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

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Yu, L., Chen, X., Ling, R. et al. Radiomics features of pericoronary adipose tissue improve CT-FFR performance in predicting hemodynamically significant coronary artery stenosis. Eur Radiol 33, 2004–2014 (2023). https://doi.org/10.1007/s00330-022-09175-7

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