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The effect of coronary calcification on diagnostic performance of machine learning–based CT-FFR: a Chinese multicenter study

  • Computed Tomography
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objective

To investigate the effect of coronary calcification morphology and severity on the diagnostic performance of machine learning (ML)–based coronary CT angiography (CCTA)–derived fractional flow reserve (CT-FFR) with FFR as a reference standard.

Methods

A total of 442 patients (61.2 ± 9.1 years, 70% men) with 544 vessels who underwent CCTA, ML-based CT-FFR, and invasive FFR from China multicenter CT-FFR study were enrolled. The effect of calcification arc, calcification remodeling index (CRI), and Agatston score (AS) on the diagnostic performance of CT-FFR was investigated. CT-FFR ≤ 0.80 and lumen reduction ≥ 50% determined by CCTA were identified as vessel-specific ischemia with invasive FFR as a reference standard.

Results

Compared with invasive FFR, ML-based CT-FFR yielded an overall sensitivity of 0.84, specificity of 0.94, and accuracy of 0.90 in a total of 344 calcification lesions. There was no statistical difference in diagnostic accuracy, sensitivity, or specificity of CT-FFR across different calcification arc, CRI, or AS levels. CT-FFR exhibited improved discrimination of ischemia compared with CCTA alone in lesions with mild-to-moderate calcification (AUC, 0.89 vs. 0.69, p < 0.001) and lesions with CRI ≥ 1 (AUC, 0.89 vs. 0.71, p < 0.001). The diagnostic accuracy and specificity of CT-FFR were higher than CCTA alone in patients and vessels with mid (100 to 299) or high (≥ 300) AS.

Conclusion

Coronary calcification morphology and severity did not influence diagnostic performance of CT-FFR in ischemia detection, and CT-FFR showed marked improved discrimination of ischemia compared with CCTA alone in the setting of calcification.

Key Points

• CT-FFR provides superior diagnostic performance than CCTA alone regardless of coronary calcification.

• No significant differences in the diagnostic performance of CT-FFR were observed in coronary arteries with different coronary calcification arcs and calcified remodeling indexes.

• No significant differences in the diagnostic accuracy of CT-FFR were observed in coronary arteries with different coronary calcification score levels.

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Abbreviations

AS:

Agatston score

CAD:

Coronary artery disease

CCTA:

Coronary CT angiography

CRI:

Calcification remodeling index

CT-FFR:

Coronary CT angiography–derived fractional flow reserve

FFR:

Fractional flow reserve

ICA:

Invasive coronary angiography

ML:

Machine learning

References

  1. Chao SP, Law WY, Kuo CJ et al (2010) The diagnostic accuracy of 256-row computed tomographic angiography compared with invasive coronary angiography in patients with suspected coronary artery disease. Eur Heart J 31:1916–1923

    Article  Google Scholar 

  2. Miller JM, Rochitte CE, Dewey M et al (2008) Diagnostic performance of coronary angiography by 64-row CT. N Engl J Med 359:2324–2336

    Article  CAS  Google Scholar 

  3. Meijboom WB, Van Mieghem CA, van Pelt N et al (2008) Comprehensive assessment of coronary artery stenoses: computed tomography coronary angiography versus conventional coronary angiography and correlation with fractional flow reserve in patients with stable angina. J Am Coll Cardiol 52:636–643

    Article  Google Scholar 

  4. Chen CC, Chen CC, Hsieh IC et al (2011) The effect of calcification score on the diagnostic accuracy of coronary computed tomography angiography. Int J Cardiovasc Imaging 27:37–42

    Article  Google Scholar 

  5. Vavere AL, Arbab-Zadeh A, Rochitte CE et al (2011) Coronary artery stenoses: accuracy of 64-detector row CT angiography in segments with mild, moderate, or severe calcification--a subanalysis of the CORE-64 trial. Radiology 261:100–108

    Article  Google Scholar 

  6. Kruk M, Noll D, Achenbach S et al (2014) Impact of coronary artery calcification characteristics on accuracy of CT angiography. JACC Cardiovasc Imaging 7:49–58

    Article  Google Scholar 

  7. Xaplanteris P, Fournier S, Pijls NH et al (2018) Five-year outcomes with PCI guided by fractional flow reserve. N Engl J Med 379:250–259

    Article  Google Scholar 

  8. Zimmermann FM, Ferrara A, Johnson NP et al (2015) Deferral vs. performance of percutaneous coronary intervention of functionally non-significant coronary stenosis: 15-year follow-up of the DEFER trial. Eur Heart J 36:3182–3188

    Article  Google Scholar 

  9. Zhuang B, Wang S, Zhao S, Lu M (2020) Computed tomography angiography-derived fractional flow reserve (CT-FFR) for the detection of myocardial ischemia with invasive fractional flow reserve as reference: systematic review and meta-analysis. Eur Radiol 30:712–725

    Article  Google Scholar 

  10. von Knebel Doeberitz PL, De Cecco CN, Schoepf UJ et al (2019) Coronary CT angiography-derived plaque quantification with artificial intelligence CT fractional flow reserve for the identification of lesion-specific ischemia. Eur Radiol 29:2378–2387

    Article  Google Scholar 

  11. Min JK, Leipsic J, Pencina MJ et al (2012) Diagnostic accuracy of fractional flow reserve from anatomic CT angiography. JAMA 308:1237–1245

    Article  CAS  Google Scholar 

  12. Nørgaard BL, Leipsic J, Gaur S et al (2014) Diagnostic performance of noninvasive fractional flow reserve derived from coronary computed tomography angiography in suspected coronary artery disease: the NXT trial (Analysis of Coronary Blood Flow Using CT Angiography: Next Steps). J Am Coll Cardiol 63:1145–1155

    Article  Google Scholar 

  13. Douglas PS, Pontone G, Hlatky MA et al (2015) Clinical outcomes of fractional flow reserve by computed tomographic angiography-guided diagnostic strategies vs. usual care in patients with suspected coronary artery disease: the prospective longitudinal trial of FFR(CT): outcome and resource impacts study. Eur Heart J 36:3359–3367

    Article  CAS  Google Scholar 

  14. Fairbairn TA, Nieman K, Akasaka T et al (2018) Real-world clinical utility and impact on clinical decision-making of coronary computed tomography angiography-derived fractional flow reserve: lessons from the ADVANCE registry. Eur Heart J 39:3701–3711

    Article  Google Scholar 

  15. Patel MR, Nørgaard BL, Fairbairn TA et al (2020) 1-Year impact on medical practice and clinical outcomes of FFRCT: the ADVANCE registry. JACC Cardiovasc Imaging 13:97–105

    Article  Google Scholar 

  16. Andreini D, Modolo R, Katagiri Y et al (2019) Impact of fractional flow reserve derived from coronary computed tomography angiography on heart team treatment decision-making in patients with multivessel coronary artery disease: Insights from the SYNTAX III REVOLUTION trial. Circ Cardiovasc Interv 12:e007607

    Article  Google Scholar 

  17. Coenen A, Kim YH, Kruk M et al (2018) Diagnostic accuracy of a machine-learning approach to coronary computed tomographic angiography-based fractional flow reserve: Result from the MACHINE consortium. Circ Cardiovasc Imaging 11:e007217

    Article  Google Scholar 

  18. Nørgaard BL, Gaur S, Leipsic J et al (2015) Influence of coronary calcification on the diagnostic performance of CT angiography derived FFR in coronary artery disease: a substudy of the NXT trial. JACC Cardiovasc Imaging 8:1045–1055

  19. Tesche C, Otani K, De Cecco CN et al (2020) Influence of coronary calcification on diagnostic performance of machine learning CT-FFR: results from MACHINE registry. JACC Cardiovasc Imaging 13:760–770

    Article  Google Scholar 

  20. Yu M, Li Y, Li W, Lu Z, Wei M, Zhang J (2018) Calcification remodeling index assessed by cardiac CT predicts severe coronary stenosis in lesions with moderate to severe calcification. J Cardiovasc Comput Tomogr 12:42–49

    Article  Google Scholar 

  21. Xu PP, Li JH, Zhou F et al (2020) The influence of image quality on diagnostic performance of a machine learning-based fractional flow reserve derived from coronary CT angiography. Eur Radiol 30:2525–2534

    Article  Google Scholar 

  22. SYNTAX working-group (2009) SYNTAX Score calculator. Available via http://www.syntaxscore.com. Accessed 19 Oct 2019

  23. Sekimoto T, Akutsu Y, Hamazaki Y et al (2016) Regional calcified plaque score evaluated by multidetector computed tomography for predicting the addition of rotational atherectomy during percutaneous coronary intervention. J Cardiovasc Comput Tomogr 10:221–228

    Article  Google Scholar 

  24. Foldyna B, Eslami P, Scholtz JE et al (2019) Density and morphology of coronary artery calcium for the prediction of cardiovascular events: insights from the Framingham Heart Study. Eur Radiol 29:6140–6148

    Article  Google Scholar 

  25. Agatston AS, Janowitz WR, Hildner FJ, Gasso J, Hildner F, Viamonte M Jr (1990) Quantification of coronary artery calcium using ultrafast computed tomography. J Am Coll Cardiol 15:827–832

    Article  CAS  Google Scholar 

  26. Hecht HS, Blaha MJ, Kazerooni EA et al (2018) CAC-DRS: Coronary Artery Calcification Data and Reporting System. An expert consensus document of the Society of Cardiovascular Computed Tomography (SCCT). J Cardiovasc Comput Tomogr 12:185–191

    Article  Google Scholar 

  27. Itu L, Rapaka S, Passerini T et al (2016) A machine-learning approach for computation of fractional flow reserve from coronary computed tomography. J Appl Physiol (1985) 121:42–52

    Article  Google Scholar 

  28. Tang CX, Wang YN, Zhou F et al (2019) Diagnostic performance of fractional flow reserve derived from coronary CT angiography for detection of lesion-specific ischemia: a multi-center study and meta-analysis. Eur J Radiol 116:90–97

    Article  Google Scholar 

  29. Collet C, Miyazaki Y, Ryan N et al (2018) Fractional flow reserve derived from computed tomographic angiography in patients with multivessel CAD. J Am Coll Cardiol 71:2756–2769

    Article  Google Scholar 

  30. Tang CX, Liu CY, Lu MJ et al (2020) CT FFR for ischemia-specific CAD with a new computational fluid dynamics algorithm: a Chinese multicenter study. JACC Cardiovasc Imaging 13:980–990

    Article  Google Scholar 

  31. DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837–845

    Article  CAS  Google Scholar 

  32. Motoyama S, Kondo T, Sarai M et al (2007) Multislice computed tomographic characteristics of coronary lesions in acute coronary syndromes. J Am Coll Cardiol 50:319–326

    Article  Google Scholar 

  33. Dey D, Lin A (2020) Machine-learning CT-FFR and extensive coronary calcium: overcoming the Achilles heel of coronary computed tomography angiography. JACC Cardiovasc Imaging 13:771–773

    Article  Google Scholar 

  34. Qiao HY, Tang CX, Schoepf UJ et al (2020) Impact of machine learning-based coronary computed tomography angiography fractional flow reserve on treatment decisions and clinical outcomes in patients with suspected coronary artery disease. Eur Radiol. https://doi.org/10.1007/s00330-020-06964-w

  35. Nous FMA, Budde RPJ, Lubbers MM et al (2020) Impact of machine-learning CT-derived fractional flow reserve for the diagnosis and management of coronary artery disease in the randomized CRESCENT trials. Eur Radiol 30:3692–3701

    Article  Google Scholar 

  36. Cerci R, Vavere AL, Miller JM et al (2013) Patterns of coronary arterial lesion calcification by a novel, cross-sectional CT angiographic assessment. Int J Cardiovasc Imaging 29:1619–1627

    Article  Google Scholar 

Download references

Acknowledgments

We thank our colleagues from multicenters for data support, Meng Jie Lu from Jinling Hospital for statistical advice, Chang Sheng Zhou from Jinling Hospital for technical assistance.

Funding

The work was supported by the National Key Research and Development Program of China (2017YFC0113400 for L.J.Z.) and Key Program of the National Natural Science Foundation of China (No. 81830057 for L.J.Z.).

Author information

Authors and Affiliations

  1. Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China

    Meng Di Jiang, Xiao Lei Zhang, Chun Xiang Tang, Peng Peng Xu, Chang Sheng Zhou, Fan Zhou, Meng Jie Lu, Guang Ming Lu, Qian Qian Ni & Long Jiang Zhang

  2. Department of Radiology, Guangdong General Hospital, Guangzhou, 510080, China

    Hui Liu

  3. Department of Cardiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China

    Jian Hua Li

  4. Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China

    Yi Ning Wang

  5. Institute of Diagnostic and Interventional Radiology and Department of Cardiology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, 200233, China

    Jia Yin Zhang, Meng Meng Yu & Yan Yi

  6. Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, 110001, China

    Yang Hou

  7. Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi’an, Shanxi, China

    Min Wen Zheng

  8. Department of Radiology, Jiangsu Taizhou People’s Hospital, Taizhou, 225300, China

    Bo Zhang

  9. Department of Cardiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, 210006, Jiangsu, China

    Dai Min Zhang

  10. Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, 10029, China

    Lei Xu

  11. Department of Radiology, Shaoyifu Hospital Affiliated to Medical College of Zhejiang University, Hangzhou, 310016, China

    Xiu Hua Hu

  12. Department of Radiology, the First Affiliated Hospital of Medical School, Xi’an Jiaotong University, Xi’an, 710061, China

    Jian Yang

Authors
  1. Meng Di Jiang
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  2. Xiao Lei Zhang
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Corresponding authors

Correspondence to Qian Qian Ni or Long Jiang Zhang.

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The scientific guarantor of this publication is Long Jiang Zhang.

Conflict of interest

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

Meng Jie Lu kindly provided statistical advice for this manuscript.

No complex statistical methods were necessary for this paper.

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Written informed consent was waived by the Institutional Review Board.

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Institutional Review Board approval was obtained.

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• retrospective

• diagnostic or prognostic study

• multicenter study

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Di Jiang, M., Zhang, X.L., Liu, H. et al. The effect of coronary calcification on diagnostic performance of machine learning–based CT-FFR: a Chinese multicenter study. Eur Radiol 31, 1482–1493 (2021). https://doi.org/10.1007/s00330-020-07261-2

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