Skip to main content
Log in

The influence of cardiac motion on radiomics features: radiomics features of non-enhanced CMR cine images greatly vary through the cardiac cycle

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

Abstract

Objectives

The cardiac cycle might impair the reproducibility of radiomics features of cardiac magnetic resonance (CMR) cine images, yet this issue has not been addressed in the previous research. We aim to evaluate whether radiomics features of CMR cine images vary during the cardiac cycle and investigate the reproducibility of radiomics features of CMR cine images.

Methods

This retrospective study enrolled 59 healthy adults who underwent CMR examination. Two observers segmented the myocardium on a 4D stack of three consecutive mid-ventricular short-axis cine images covering the cardiac cycle. A total of 352 radiomics features were extracted. The coefficient of variation and intraclass correlation coefficient were used to assess the feature variability through the cycle and inter-observer reproducibility, respectively.

Results

Approximately 55% of radiomics features showed large variability through the cardiac cycle. The original features showed more variability than the Laplacian of Gaussian-filtered features (73.8% vs. 48%). The features of 4D stack cine images had a higher proportion of reproducible features (92.0%, 87.7%, and 76.1%) compared with the end-diastolic (77.8%, 62.2%, and 41.7%) and the end-systolic images (81.5%, 74.1%, and 58.8%) for intraclass correlation cut-off values of 30.80, > 0.85, and > 0.90, respectively.

Conclusions

Radiomics features of CMR cine images greatly vary during the cardiac cycle. The radiomics features of 4D stack of cine images are more robust compared with end-diastolic and end-systolic cine images in terms of reproducibility. The impact of the cardiac cycle on the reproducibility of the features should be considered when employing CMR cine images radiomics.

Key Points

• There is limited evidence on the impact of cardiac motion on radiomics features of CMR cine images and the reproducibility of the radiomics features of CMR cine images.

• Radiomics features of non-enhanced CMR cine images greatly vary during the cardiac cycle, and the number of “reproducible” features shows significant variations according to the cardiac phases.

• The impact of cardiac cycle on the reproducibility of the radiomics features should be considered when employing CMR cine images radiomics.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

Abbreviations

CMR:

Cardiac magnetic resonance

COV:

Coefficient of variation

DSC:

Dice similarity coefficient

GLCM:

Gray level co-occurrence matrix

GLDM:

Gray level dependence matrix

GLSZM:

Gray level size zone matrix

GRLM:

Gray level run length matrix

ICC:

Intraclass correlation coefficient

IQR:

Interquartile range

LoG:

Laplacian of Gaussian

References

  1. van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77:104–107

    Article  Google Scholar 

  2. Gillies RJ, Kinahan PE, Hricak H (2015) Radiomics: images are more than pictures, they are data. Radiology 278:563–577

    Article  Google Scholar 

  3. Oliver JA, Budzevich M, Zhang GG, Dilling TJ, Latifi K, Moros EG (2015) Variability of image features computed from conventional and respiratory-gated PET/CT images of lung cancer. Transl Oncol 8:524–534

    Article  Google Scholar 

  4. Grootjans W, Tixier F, van der Vos CS et al (2016) The impact of optimal respiratory gating and image noise on evaluation of intratumor heterogeneity on 18F-FDG pet imaging of lung cancer. J Nucl Med 57:1692–1698

    Article  Google Scholar 

  5. Yip S, McCall K, Aristophanous M, Chen AB, Aerts HJ, Berbeco R (2014) Comparison of texture features derived from static and respiratory-gated PET images in non-small cell lung cancer. PLoS One. https://doi.org/10.1371/journal.pone.0115510

  6. Traverso A, Wee L, Dekker A, Gillies R (2018) Repeatability and reproducibility of radiomic features: a systematic review. Int J Radiat Oncol Biol Phys 102:1143–1158

    Article  Google Scholar 

  7. Mackin D, Fave X, Zhang L et al (2015) Measuring computed tomography scanner variability of radiomics features. Invest Radiol 50:757–765

    Article  Google Scholar 

  8. Park JE, Kim D, Kim HS et al (2020) Quality of science and reporting of radiomics in oncologic studies: room for improvement according to radiomics quality score and TRIPOD statement. Eur Radiol 30(1):523–536. https://doi.org/10.1007/s00330-019-06360-z

    Article  Google Scholar 

  9. 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

    Article  Google Scholar 

  10. Schofield R, Ganeshan B, Fontana M et al (2019) Texture analysis of cardiovascular magnetic resonance cine images differentiates aetiologies of left ventricular hypertrophy. Clin Radiol 74:140–149

    Article  CAS  Google Scholar 

  11. 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

    Article  Google Scholar 

  12. Larroza A, López-Lereu MP, Monmeneu JV et al (2018) Texture analysis of cardiac cine magnetic resonance imaging to detect nonviable segments in patients with chronic myocardial infarction. Med Phys 45:1471–1480

    Article  CAS  Google Scholar 

  13. Amano Y, Suzuki Y, Yanagisawa F, Omori Y, Matsumoto N (2018) Relationship between extension or texture features of late gadolinium enhancement and ventricular tachyarrhythmias in hypertrophic cardiomyopathy. Biomed Res Int. https://doi.org/10.1155/2018/4092469

  14. Baeßler B, Mannil M, Maintz D, Alkadhi H, Manka R (2018) Texture analysis and machine learning of non-contrast T1-weighted MR images in patients with hypertrophic cardiomyopathy-preliminary results. Eur J Radiol 102:61–67

    Article  Google Scholar 

  15. Baessler B, Luecke C, Lurz J et al (2018) Cardiac MRI texture analysis of T1 and T2 maps in patients with infarctlike acute myocarditis. Radiology. 2289:357–365

    Article  Google Scholar 

  16. Messroghli DR, Moon JC, Ferreira VM et al (2018) 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)

  17. Lowekamp BC, Chen DT, Ibanez L, Blezek D (2013) The design of SimpleITK. Front Neuroinform 7:45

    Article  Google Scholar 

  18. Tustison NJ, Avants BB, Cook PA et al (2010) N4itk: improved n3 bias correction. IEEE Trans Med Imaging 29:1310–1320

  19. Shafiq-Ul-Hassan M, Zhang GG, Latifi K et al (2017) Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels. Med Phys 44:1050–1062

    Article  CAS  Google Scholar 

  20. Zwanenburg A, Leger S, Vallières M, Löck S (2016) Image biomarker standardisation initiative. arXiv preprint arXiv:1612.07003

  21. Duron L, Balvay D, Vande Perre S et al (2019) Gray-level discretization impacts reproducible MRI radiomics texture features. PLoS One. https://doi.org/10.1371/journal.pone.0213459

  22. Yamashita R, Perrin T, Chakraborty J et al (2020) Radiomic feature reproducibility in contrast-enhanced CT of the pancreas is affected by variabilities in scan parameters and manual segmentation. Eur Radiol 30:195–205

    Article  Google Scholar 

  23. Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26:297–302

  24. Yan J, Chu-Shern JL, Loi HY et al (2015) Impact of image reconstruction settings on texture features in 18F-FDG PET. J Nucl 56:1667–1673

    Article  CAS  Google Scholar 

  25. Du Q, Baine M, Bavitz K et al (2019) Radiomic feature stability across 4D respiratory phases and its impact on lung tumor prognosis prediction. PLoS One. https://doi.org/10.1371/journal.pone.0216480

  26. Starling MR (2002) Physiology of myocardial contraction. In: Colucci WS (ed) Atlas of heart failure. Current Medicine Group, London

    Google Scholar 

  27. Fornacon-Wood I, Mistry H, Ackermann CJ et al (2020) Reliability and prognostic value of radiomic features are highly dependent on choice of feature extraction platform [published online ahead of print, 2020 Jun 1]. Eur Radiol. 2020. https://doi.org/10.1007/s00330-020-06957-9

  28. Sullivan DC, Obuchowski NA, Kessler LG et al (2015) Metrology standards for quantitative imaging biomarkers. Radiology 277:813–825

    Article  Google Scholar 

  29. Raunig DL, McShane LM, Pennello G et al (2015) Quantitative imaging biomarkers: a review of statistical methods for technical performance assessment. Stat Methods Med Res 24:27–67

    Article  Google Scholar 

Download references

Funding

The authors state that this work has not received any funding.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Deniz Alis.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Deniz Alis.

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

Two authors (D.A. and M.Y.) have significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board. Approval from the institutional animal care committee was not required.

Ethical approval

Institutional Review Board approval was obtained from Istanbul Mehmet Akif Ersoy Thoracic and Cardiovascular Surgery Training and Research Hospital ethics committee.

Methodology

• retrospective

• cross-sectional study/experimental

• performed at one institution

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

ESM 1

(DOCX 123 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alis, D., Yergin, M., Asmakutlu, O. et al. The influence of cardiac motion on radiomics features: radiomics features of non-enhanced CMR cine images greatly vary through the cardiac cycle. Eur Radiol 31, 2706–2715 (2021). https://doi.org/10.1007/s00330-020-07370-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-020-07370-y

Keywords

Navigation