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The best predictor of ischemic coronary stenosis: subtended myocardial volume, machine learning–based FFRCT, or high-risk plaque features?

  • Cardiac
  • Published:
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Abstract

Objectives

The present study aimed to compare the diagnostic performance of a machine learning (ML)–based FFRCT algorithm, quantified subtended myocardial volume, and high-risk plaque features for predicting if a coronary stenosis is hemodynamically significant, with reference to FFRICA.

Methods

Patients who underwent both CCTA and FFRICA measurement within 2 weeks were retrospectively included. ML-based FFRCT, volume of subtended myocardium (Vsub), percentage of subtended myocardium volume versus total myocardium volume (Vratio), high-risk plaque features, minimal lumen diameter (MLD), and minimal lumen area (MLA) along with other parameters were recorded. Lesions with FFRICA ≤ 0.8 were considered to be functionally significant.

Results

One hundred eighty patients with 208 lesions were included. The lesion length (LL), diameter stenosis, area stenosis, plaque burden, Vsub, Vratio, Vratio/MLD, Vratio/MLA, and LL/MLD4 were all significantly longer or larger in the group of FFRICA ≤ 0.8 while smaller minimal lumen area, MLD, and FFRCT value were noted. The AUC of FFRCT + Vratio/MLD was significantly better than that of FFRCT alone (0.935 versus 0.873, p < 0.001). High-risk plaque features failed to show difference between functionally significant and insignificant groups. Vratio/MLD-complemented ML-based FFRCT for “gray zone” lesions with FFRCT value ranged from 0.7 to 0.8 and the combined use of these two parameters yielded the best diagnostic performance (86.5%, 180/208).

Conclusions

ML-based FFRCT simulation and Vratio/MLD both provide incremental value over CCTA-derived diameter stenosis and high-risk plaque features for predicting hemodynamically significant lesions. Vratio/MLD is more accurate than ML-based FFRCT for lesions with simulated FFRCT value from 0.7 to 0.8.

Key Points

• Machine learning–based FFR CT and subtended myocardium volume both performed well for predicting hemodynamically significant coronary stenosis.

• Subtended myocardium volume was more accurate than machine learning–based FFR CT for “gray zone” lesions with simulated FFR value from 0.7 to 0.8.

• CT-derived high-risk plaque features failed to correctly identify hemodynamically significant stenosis.

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Abbreviations

AUC:

Area under the curve

CCTA:

Coronary computed tomography angiography

CFD:

Computational fluid dynamics

DJS:

Duke Jeopardy Score

FFR:

Fractional flow reserve

ICA:

Invasive coronary angiography

LL:

Lesion length

ML:

Machine learning

MLA:

Minimal lumen area

MLD:

Minimal lumen diameter

ROC:

Receiver operating characteristic

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Funding

This study has received funding from the National Natural Science Foundation of China (Grant No.: 81671678), Shanghai Municipal Education Commission-Gaofeng Clinical Medicine Grant Support (Grant No.: 20161428), Shanghai Key Discipline of Medical Imaging (No.: 2017ZZ02005), and The National Key Research and Development Program of China (Grant Nos.: 2016YFC1300400 and 2016YFC1300402).

Author information

Author notes

  1. Mengmeng Yu and Zhigang Lu contributed equally to this work.

Authors and Affiliations

  1. Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, #600, Yishan Rd, Shanghai, 200233, China

    Mengmeng Yu & Jiayin Zhang

  2. Department of Cardiology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, #600, Yishan Rd, Shanghai, China

    Zhigang Lu & Chengxing Shen

  3. Siemens Healthineers, #278, Zhouzhugong Rd, Shanghai, China

    Jing Yan

  4. Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, China

    Yining Wang

  5. Department of Radiology, 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

    Bin Lu

Authors
  1. Mengmeng Yu
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  7. Jiayin Zhang
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Corresponding author

Correspondence to Jiayin Zhang.

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The scientific guarantor of this publication is Dr. 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.

Statistics and biometry

No complex statistical methods were necessary for this paper.

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Written informed consent was waived by hospital IRB.

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• retrospective

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• performed at one institution

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Yu, M., Lu, Z., Shen, C. et al. The best predictor of ischemic coronary stenosis: subtended myocardial volume, machine learning–based FFRCT, or high-risk plaque features?. Eur Radiol 29, 3647–3657 (2019). https://doi.org/10.1007/s00330-019-06139-2

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  • DOI: https://doi.org/10.1007/s00330-019-06139-2

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