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A radiomic nomogram for prediction of major adverse cardiac events in ST-segment elevation myocardial infarction

  • Cardiac
  • Published:
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Abstract

Objectives

This study was conducted to establish and validate a non-contrast T1 map–based radiomic nomogram for predicting major adverse cardiac events (MACEs) in patients with acute ST-segment elevation myocardial infarction (STEMI) undergoing percutaneous coronary intervention (PCI).

Methods

This retrospective study included 157 consecutive patients (training sets, 109 patients; test sets, 48 patients) with acute STEMI undergoing PCI. An open-source radiomics software was used to segment the myocardium on the non-contrast T1 mapping and extract features. A radiomic signature was constructed to predict MACEs using the least absolute shrinkage and selection operator method. The performance of the radiomic nomogram for predicting MACEs in both the training and test sets was evaluated by its discrimination, calibration, and clinical usefulness.

Results

The radiomic signature showed a good prognostic ability in the training sets with an AUC of 0.94 (95% CI, 0.86 to 1.00) and F1 score of 0.71, which was confirmed in the test sets with an AUC of 0.90 (95% CI, 0.74 to 1.00) and F1 score of 0.62. The nomogram consisting of the radiomic scores and cardiac troponin I showed good discrimination ability in the training and test sets with AUCs of 0.96 (95% CI, 0.91 to 1.00; F1 score, 0.71) and 0.94 (95% CI, 0.83 to 1.00; F1 score, 0.70), respectively.

Conclusions

The non-contrast T1 map–based radiomic nomogram is a useful tool for the prediction of MACEs in patients with acute STEMI undergoing PCI that can assist clinicians for optimised risk stratification of individual patients.

Key Points

• Radiomic signature improved MACE prediction in acute STEMI patients.

• T1 mapping–derived radiomic signature outperformed conventional cardiac MRI parameters in predicting MACEs in acute STEMI patients.

• The non-contrast T1 mapping–based radiomic nomogram can be used for prediction of MACEs and improvement of risk stratification in acute STEMI.

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Abbreviations

AAR:

Area at risk

AUC:

Area under the curve

CI:

Confidence interval

CMR:

Cardiac magnetic resonance

cTnI:

Cardiac troponin I

DCA:

Decision curve analysis

GBCA:

Gadolinium-based contrast agent

IQR:

Interquartile range

IS:

Infarct size

LASSO:

Least absolute shrinkage and selection operator

LGE:

Late gadolinium enhancement

LV:

Left ventricle

LVEF:

Left ventricular ejection fraction

MACE:

Major adverse cardiovascular event

MI:

Myocardial infarction

MOLLI:

Modified look-locker inversion recovery

MVO:

Microvascular obstruction

PCI:

Percutaneous coronary intervention

Rad-score:

Radiomic score

ROC:

Receiver operator characteristic

ROI:

Region of interest

STEMI:

ST-segment elevation myocardial infarction

References

  1. Rodriguez-Palomares JF, Gavara J, Ferreira-González I et al (2019) Prognostic value of initial left ventricular remodeling in patients with reperfused STEMI. JACC Cardiovasc Imaging 12:2445–2456

    Article  Google Scholar 

  2. Roger VL, Go AS, Lloyd-Jones DM et al (2011) Heart disease and stroke statistics--2011 update: a report from the American Heart Association. Circulation 123:e18–e209

    Article  Google Scholar 

  3. Izquierdo M, Ruiz-Granell R, Bonanad C et al (2013) Value of early cardiovascular magnetic resonance for the prediction of adverse arrhythmic cardiac events after a first noncomplicated ST-segment-elevation myocardial infarction. Circ Cardiovasc Imaging 6:755–761

    Article  Google Scholar 

  4. Eitel I, de Waha S, Wöhrle J et al (2014) Comprehensive prognosis assessment by CMR imaging after ST-segment elevation myocardial infarction. J Am Coll Cardiol 64:1217–1226

    Article  Google Scholar 

  5. Steg PG, James SK, Atar D et al (2012) ESC Guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation. Eur Heart J 33:2569–2619

    Article  CAS  Google Scholar 

  6. O’Gara PT, Kushner FG, Ascheim DD et al (2013) 2013 ACCF/AHA guideline for the management of ST-elevation myocardial infarction: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol 61:e78–e140

    Article  Google Scholar 

  7. Symons R, Pontone G, Schwitter J et al (2018) Long-term incremental prognostic value of cardiovascular magnetic resonance after ST-segment elevation myocardial infarction: a study of the collaborative registry on CMR in STEMI. JACC Cardiovasc Imaging 11:813–825

    Article  Google Scholar 

  8. Carrick D, Haig C, Rauhalammi S et al (2016) Prognostic significance of infarct core pathology revealed by quantitative non-contrast in comparison with contrast cardiac magnetic resonance imaging in reperfused ST-elevation myocardial infarction survivors. Eur Heart J 37:1044–1059

    Article  Google Scholar 

  9. Ugander M, Oki AJ, Hsu L-Y et al (2012) Extracellular volume imaging by magnetic resonance imaging provides insights into overt and sub-clinical myocardial pathology. Eur Heart J 33:1268–1278

    Article  CAS  Google Scholar 

  10. Carrick D, Haig C, Rauhalammi S et al (2015) Pathophysiology of LV remodeling in survivors of STEMI: inflammation, remote myocardium, and prognosis. JACC Cardiovasc Imaging 8:779–789

    Article  Google Scholar 

  11. Reinstadler SJ, Stiermaier T, Liebetrau J et al (2018) Prognostic significance of remote myocardium alterations assessed by quantitative noncontrast T1 mapping in ST-segment elevation myocardial infarction. JACC Cardiovasc Imaging 11:411–419

    Article  Google Scholar 

  12. Yip SS, Aerts HJWL (2016) Applications and limitations of radiomics. Phys Med Biol 61:R150–R166

    Article  CAS  Google Scholar 

  13. Lambin P, Leijenaar RTH, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749–762

    Article  Google Scholar 

  14. Nie P, Yang G, Wang Z et al (2019) A CT-based radiomics nomogram for differentiation of renal angiomyolipoma without visible fat from homogeneous clear cell renal cell carcinoma. Eur Radiol 30:1274–1284

    Article  Google Scholar 

  15. Ji G-W, Zhu F-P, Zhang Y-D et al (2019) A radiomics approach to predict lymph node metastasis and clinical outcome of intrahepatic cholangiocarcinoma. Eur Radiol 29:3725–3735

    Article  Google Scholar 

  16. Wang Y, Liu W, Yu Y et al (2019) CT radiomics nomogram for the preoperative prediction of lymph node metastasis in gastric cancer. Eur Radiol 30:976–986

    Article  Google Scholar 

  17. Larroza A, Materka A, López-Lereu MP, Monmeneu JV, Bodí V, Moratal D (2017) Differentiation between acute and chronic myocardial infarction by means of texture analysis of late gadolinium enhancement and cine cardiac magnetic resonance imaging. Eur J Radiol 92:78–83

  18. Baessler B, Mannil M, Oebel S, Maintz D, Alkadhi H, Manka R (2018) Subacute and chronic left ventricular myocardial scar: accuracy of texture analysis on nonenhanced cine MR images. Radiology 286:103–112

  19. Schulz-Menger J, Bluemke DA, Bremerich J et al (2013) Standardized image interpretation and post processing in cardiovascular magnetic resonance: Society for Cardiovascular Magnetic Resonance (SCMR) board of trustees task force on standardized post processing. J Cardiovasc Magn Reson 15:35

    Article  Google Scholar 

  20. Bondarenko O, Beek AM, Hofman MBM et al (2005) Standardizing the definition of hyperenhancement in the quantitative assessment of infarct size and myocardial viability using delayed contrast-enhanced CMR. J Cardiovasc Magn Reson 7:481–485

    Article  Google Scholar 

  21. Puntmann VO, Peker E, Chandrashekhar Y, Nagel E (2016) T1 Mapping in characterizing myocardial disease: a comprehensive review. Circ Res 119:277–299

    Article  CAS  Google Scholar 

  22. Szczypinski PM, Strzelecki M, Materka A, Klepaczko A (2009) MaZda--a software package for image texture analysis. Comput Methods Programs Biomed 94:66–76

    Article  Google Scholar 

  23. Strzelecki M, Szczypinski P, Materka A, Klepaczko A (2013) A software tool for automatic classification and segmentation of 2D/3D medical images. Nucl Instrum Methods Phys Res Sect A 702:137–140

    Article  CAS  Google Scholar 

  24. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol 57:289–300

    Google Scholar 

  25. Dastidar AG, Rodrigues JCL, Baritussio A, Bucciarelli-Ducci C (2016) MRI in the assessment of ischaemic heart disease. Heart Br Card Soc 102:239–252

    CAS  Google Scholar 

  26. Stone GW, Selker HP, Thiele H et al (2016) Relationship between infarct size and outcomes following primary PCI: patient-level analysis from 10 randomized trials. J Am Coll Cardiol 67:1674–1683

    Article  Google Scholar 

  27. Reinstadler SJ, Thiele H, Eitel I (2015) Risk stratification by cardiac magnetic resonance imaging after ST-elevation myocardial infarction. Curr Opin Cardiol 30:681–689

    Article  Google Scholar 

  28. Messroghli DR, Moon JC, Ferreira VM et al (2017) Clinical recommendations for cardiovascular magnetic resonance mapping of T1, T2, T2* and extracellular volume: a consensus statement by the Society for Cardiovascular Magnetic Resonance (SCMR) endorsed by the European Association for Cardiovascular Imaging (EACVI). J Cardiovasc Magn Reson 19:75

    Article  Google Scholar 

  29. Ide S, Riesenkampff E, Chiasson DA et al (2017) Histological validation of cardiovascular magnetic resonance T1 mapping markers of myocardial fibrosis in paediatric heart transplant recipients. J Cardiovasc Magn Reson 19:10

    Article  Google Scholar 

  30. Diao KY, Yang ZG, Xu HY et al (2016) Histologic validation of myocardial fibrosis measured by T1 mapping: a systematic review and meta-analysis. J Cardiovasc Magn Reson 18:92

    Article  Google Scholar 

  31. Aerts HJWL (2016) The potential of radiomic-based phenotyping in precision medicine: a review. JAMA Oncol 2:1636–1642

    Article  Google Scholar 

  32. Castellano G, Bonilha L, Li LM, Cendes F (2004) Texture analysis of medical images. Clin Radiol 59:1061–1069

    Article  CAS  Google Scholar 

  33. Kanda T, Fukusato T, Matsuda M et al (2015) Gadolinium-based contrast agent accumulates in the brain even in subjects without severe renal dysfunction: evaluation of autopsy brain specimens with inductively coupled plasma mass spectroscopy. Radiology 276:228–232

    Article  Google Scholar 

  34. Grobner T, Prischl FC (2007) Gadolinium and nephrogenic systemic fibrosis. Kidney Int 72:260–264

    Article  CAS  Google Scholar 

  35. Hammer-Hansen S, Ugander M, Hsu LY et al (2014) Distinction of salvaged and infarcted myocardium within the ischaemic area-at-risk with T2 mapping. Eur Heart J Cardiovasc Imaging 15:1048–1053

    Article  Google Scholar 

  36. Vabalas A, Gowen E, Poliakoff E, Casson AJ (2019) Machine learning algorithm validation with a limited sample size. PLoS One 14:e0224365

    Article  CAS  Google Scholar 

  37. Ogundimu EO, Altman DG, Collins GS (2016) Adequate sample size for developing prediction models is not simply related to events per variable. J Clin Epidemiol 76:175–182

    Article  Google Scholar 

  38. Teixeira T, Hafyane T, Stikov N, Greiser A, Friedrich MG, Akdeniz C (2016) Comparison of different cardiovascular magnetic resonance sequences for native myocardial T1 mapping at 3T. J Cardiovasc Magn Reson 18:65

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Acknowledgements

We thank Yan Guo for her expert opinion and helpful comments.

Funding

This study has received funding from the 345 Talent Project in Shengjing Hospital of China Medical University.

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Authors and Affiliations

  1. Department of Radiology, Shengjing Hospital of China Medical University, No. 36 Sanhao Street, Heping District, Shenyang, 110004, Liaoning, China

    Quanmei Ma, Yue Ma, Xiaonan Wang, Shanshan Li & Yang Hou

  2. Department of Cardiology, Shengjing Hospital of China Medical University, No. 36 Sanhao Street, Heping District, Shenyang, 110004, Liaoning, China

    Tongtong Yu, Weili Duan, Jiake Wu, Zongyu Wen, Yundi Jiao & Zhaoqing Sun

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  1. Quanmei Ma
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  2. Yue Ma
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  11. Yang Hou
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Corresponding author

Correspondence to Yang Hou.

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The scientific guarantor of this publication is Yang Hou.

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

Yan Guo kindly provided statistical advice for this manuscript.

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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 / observational

• performed at one institution

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Ma, Q., Ma, Y., Wang, X. et al. A radiomic nomogram for prediction of major adverse cardiac events in ST-segment elevation myocardial infarction. Eur Radiol 31, 1140–1150 (2021). https://doi.org/10.1007/s00330-020-07176-y

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  • DOI: https://doi.org/10.1007/s00330-020-07176-y

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