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A 2-year investigation of the impact of the computed tomography–derived fractional flow reserve calculated using a deep learning algorithm on routine decision-making for coronary artery disease management

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

Abstract

Objective

This study aims to investigate the safety and feasibility of using a deep learning algorithm to calculate computed tomography angiography–based fractional flow reserve (DL-FFRCT) as an alternative to invasive coronary angiography (ICA) in the selection of patients for coronary intervention.

Materials and methods

Patients (N = 296) with symptomatic coronary artery disease identified by coronary computed tomography angiography (CTA) with stenosis over 50% were retrospectively enrolled from a single centre in this study. ICA-guided interventions were performed in patients at admission, and DL-FFRCT was conducted retrospectively. The influences on decision-making by using DL-FFRCT and the clinical outcome were compared to those of ICA-guided care for symptomatic CAD at the 2-year follow-up evaluation.

Result

Two hundred forty-three patients were evaluated. Up to 72% of diagnostic ICA studies could have been avoided by using a DL-FFRCT value > 0.8 as a cut-off for intervention. A similar major adverse cardiovascular event (MACE) rate was observed in patients who underwent revascularisation with a DL-FFRCT value ≤ 0.8 (2.9%) compared to that of ICA-guided interventions (3.3%) (stented lesions with ICA stenosis > 75%) (p = 0.838).

Conclusion

DL-FFRCT can reduce the need for diagnostic coronary angiography when identifying patients suitable for coronary intervention. A low MACE rate was found in a 2-year follow-up investigation.

Key Points

• Seventy-two percent of diagnostic ICA studies could have been avoided by using a DL-FFRCT value > 0.8 as a cut-off for intervention.

• Coronary artery stenting based on the diagnosis by using a 320-detector row CT scanner and a positive DL-FFRCT value could potentially be associated with a lower occurrence rate of major adverse cardiovascular events (2.9%) within the first 2 years.

• A low event rate was found when intervention was performed in tandem lesions with haemodynamic significance based on DL-FFRCT < 0.8 as a cut-off value.

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Abbreviations

CABG:

Coronary artery bypass surgery

CAD:

Coronary artery disease

CCTA:

Coronary computed tomography angiography

CFD:

Computed fluid dynamics

DL-FFRCT:

Deep learning–based FFRCT

FFR:

Fractional flow reserve

FFRCT:

Coronary computed tomography angiography (CCTA)-derived fractional flow reserve

ICA:

Invasive coronary angiography

LAD:

Left anterior descending artery

MACEs:

Major adverse cardiovascular events

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Acknowledgements

We thank the clinical support by Dr. Zeyu Xiao. We thank Keya medical (Shenzhen, Guangdong, China) for providing the support of deep learning-based CT-FFR product.

Funding

This study is supported by grand from Guangzhou Science and Technology Plan Project (Grant Number: 201807010046), Guangzhou Key Laboratory of Molecular and Functional Imaging for Clinical Translation (Grant Number:201905010003), Natural Science Foundation of Guangdong Province, China (Grant Number: 2019A1515011463), the Science and Technology Planning Project of Guangdong Province, China (Grant Number: 2018A050506031, 2019B010110001), the National Natural Science Foundation of China (Grant Number: 61771464, U1801265, 82001910), and Medical Scientific Research Foundation of Guangdong Province of China (Grant Number: A2018132).

Author information

Author notes

  1. Xin Liu and Xukai Mo contributed equally to this work.

Authors and Affiliations

  1. Guangdong Academy Research on VR Industry, Foshan University, #18 Jiangwan 1st Road, Foshan, 528000, Guangdong, China

    Xin Liu

  2. Medical Imaging Center, The First Affiliated Hospital of Jinan University, No 613 Huangpu Dadao West, Guangzhou, 510630, China

    Xukai Mo & Changzheng Shi

  3. Engineering Research Center of Medical Imaging Artificial Intelligence for Precision Diagnosis and Treatment, No 613 Huangpu Dadao West, Guangzhou, 610630, China

    Xukai Mo & Changzheng Shi

  4. School of Biomedical Engineering, Sun Yat-sen University, No. 135, Xingang Xi Road, Guangzhou, 510275, China

    Heye Zhang

  5. National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK

    Guang Yang

  6. Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, 30-32 Ngan Shing St., Sha Tin, Hong Kong, SAR, China

    William Kongtou Hau

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  1. Xin Liu
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Corresponding author

Correspondence to Changzheng Shi.

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The scientific guarantor of this publication is Changzheng Shi.

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The authors report no relationships that could be construed as a conflict of interest.

Statistics and biometry

No complex statistical methods were necessary for this paper.

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Written informed consent was obtained.

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Institutional Review Board approved the present study.

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• retrospective

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• performed at one institution

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Liu, X., Mo, X., Zhang, H. et al. A 2-year investigation of the impact of the computed tomography–derived fractional flow reserve calculated using a deep learning algorithm on routine decision-making for coronary artery disease management. Eur Radiol 31, 7039–7046 (2021). https://doi.org/10.1007/s00330-021-07771-7

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  • DOI: https://doi.org/10.1007/s00330-021-07771-7

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