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LGE-CMR-derived texture features reflect poor prognosis in hypertrophic cardiomyopathy patients with systolic dysfunction: preliminary results

  • Cardiac
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To evaluate the prognostic value of texture features based on late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) images in hypertrophic cardiomyopathy (HCM) patients with systolic dysfunction.

Methods

67 HCM patients with systolic dysfunction (41 male and 26 female, mean age ± standard deviation, 46.20 years ± 13.38) were enrolled. All patients underwent 1.5 T CMR cine and LGE imaging. Texture features were extracted from LGE images. Cox proportional hazard analysis and Kaplan-Meier analysis were used to determine the association of texture features and traditional parameters with event free survival.

Results

Family history (hazard ratio [HR]=2.558, 95 % confidence interval [CI]=1.060–6.180), NYHA III-IV (HR=5.627, CI=1.652–19.173), left ventricular ejection fraction (HR=0.945, CI=0.902–0.991), left ventricular end-diastolic volume index (HR=1.006, CI=1.000–1.012), LGE extent (HR=1.911, CI=1.348–2.709) and three texture parameters [X0_H_skewness (HR=0.783, CI=0.691–0.889), X0_GLCM_cluster_tendency (HR=0.735, CI=0.616–0.877) and X0_GLRLM_energy (HR=1.344, CI=1.173–1.540)] were significantly associated with event free survival in univariate analysis (p<0.05). The HR of LGE extent (HR=1.548 [CI=1.046–2.293], 1.650 [CI=1.122–2.428] and 1.586 [CI=1.044–2.409] per 10 % increase, p<0.05) remained significant when adjusted by one of the three texture features.

Conclusion

Increased LGE heterogeneity (higher X0_GLRLM_energy, lower X0_H_skewness and lower X0_GLCM_cluster_tendency) was associated with adverse events in HCM patients with systolic dysfunction.

Key Points

• Textural analysis from CMR can be applied in HCM.

• Texture features derived from LGE images can capture fibrosis heterogeneity.

• CMR texture analysis provides prognostic information in HCM patients.

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Abbreviations

CMR:

Cardiac magnetic resonance

CRTD:

Cardiac resynchronization therapy defibrillator

GLCM:

Grey-level co-occurrence matrix

GLRLM:

Grey-level run-length matrix

HCM:

Hypertrophic cardiomyopathy

ICC:

Intra-/inter-class correlation coefficient

ICD:

Implantable cardioverter defibrillator

LGE:

Late gadolinium enhancement

LV:

Left ventricular

LVEF:

Left ventricular ejection fraction

NYHA:

New York Heart Association

ROC:

Receiver operating characteristic

ROI:

Region of interest

SCD:

Sudden cardiac death

SD:

Standard deviation

References

  1. Semsarian C, Ingles J, Maron MS, Maron BJ (2015) New perspectives on the prevalence of hypertrophic cardiomyopathy. J Am Coll Cardiol 65:1249–1254

    Article  Google Scholar 

  2. Maron BJ, Ommen SR, Semsarian C, Spirito P, Olivotto I, Maron MS (2014) Hypertrophic cardiomyopathy: present and future, with translation into contemporary cardiovascular medicine. J Am Coll Cardiol 64:83–99

    Article  Google Scholar 

  3. American College of Cardiology Foundation Task Force on Expert Consensus Documents, Hundley WG, Bluemke DA et al (2010) ACCF/ACR/AHA/NASCI/SCMR 2010 expert consensus document on cardiovascular magnetic resonance: a report of the American College of Cardiology Foundation Task Force on Expert Consensus Documents. J Am Coll Cardiol 55:2614–2662

  4. Choudhury L, Mahrholdt H, Wagner A et al (2002) Myocardial scarring in asymptomatic or mildly symptomatic patients with hypertrophic cardiomyopathy. J Am Coll Cardiol 40:2156–2164

    Article  Google Scholar 

  5. O'Hanlon R, Grasso A, Roughton M et al (2010) Prognostic significance of myocardial fibrosis in hypertrophic cardiomyopathy. J Am Coll Cardiol 56:867–874

    Article  Google Scholar 

  6. Rubinshtein R, Glockner JF, Ommen SR et al (2010) Characteristics and clinical significance of late gadolinium enhancement by contrast-enhanced magnetic resonance imaging in patients with hypertrophic cardiomyopathy. Circ Heart Fail 3:51–58

    Google Scholar 

  7. Green JJ, Berger JS, Kramer CM, Salerno M (2012) Prognostic value of late gadolinium enhancement in clinical outcomes for hypertrophic cardiomyopathy. JACC Cardiovasc Imaging 5:370–377

    Article  Google Scholar 

  8. Chan RH, Maron BJ, Olivotto I et al (2014) Prognostic value of quantitative contrast-enhanced cardiovascular magnetic resonance for the evaluation of sudden death risk in patients with hypertrophic cardiomyopathy. Circulation 130:484–495

  9. Kim JH, Ko ES, Lim Y et al (2017) Breast Cancer Heterogeneity: MR Imaging Texture Analysis and Survival Outcomes. Radiology 282:665–675

    Article  Google Scholar 

  10. Pickles MD, Lowry M, Gibbs P (2016) Pretreatment Prognostic Value of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Vascular, Texture, Shape, and Size Parameters Compared With Traditional Survival Indicators Obtained From Locally Advanced Breast Cancer Patients. Invest Radiol 51:177–185

    Article  CAS  Google Scholar 

  11. Kickingereder P, Burth S, Wick A et al (2016) Radiomic Profiling of Glioblastoma: Identifying an Imaging Predictor of Patient Survival with Improved Performance over Established Clinical and Radiologic Risk Models. Radiology 280:880–889

    Article  Google Scholar 

  12. Yoon SH, Park CM, Park SJ, Yoon JH, Hahn S, Goo JM (2016) Tumor Heterogeneity in Lung Cancer: Assessment with Dynamic Contrast-enhanced MR Imaging. Radiology 280(3):940–948

    Article  Google Scholar 

  13. Ng F, Ganeshan B, Kozarski R, Yoon JH, Hahn S, Goo JM (2013) Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival. Radiology 266:177–184

    Article  Google Scholar 

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

    Article  Google Scholar 

  15. Authors/Task Force members, Elliott PM, Anastasakis A et al (2014) 2014 ESC Guidelines on diagnosis and management of hypertrophic cardiomyopathy: the Task Force for the Diagnosis and Management of Hypertrophic Cardiomyopathy of the European Society of Cardiology (ESC). Eur Heart J 35:2733–2779

  16. Goto D, Kinugawa S, Hamaguchi S et al (2013) Clinical characteristics and outcomes of dilated phase of hypertrophic cardiomyopathy: report from the registry data in Japan. J Cardiol 61:65–70

    Article  Google Scholar 

  17. Lang RM, Badano LP, Mor-Avi V et al (2015) Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. Eur Heart J Cardiovasc Imaging 16:233–270

    Article  Google Scholar 

  18. 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(1):35

    Article  Google Scholar 

  19. Moravsky G, Ofek E, Rakowski H et al (2013) Myocardial fibrosis in hypertrophic cardiomyopathy: accurate reflection of histopathological findings by CMR. JACC Cardiovasc Imaging 6:587–596

    PubMed  Google Scholar 

  20. Maron M, Appelbaum E, Harrigan C et al (2008) Clinical profile and significance of delayed enhancement in hypertrophic cardiomyopathy. Circ Heart Fail 1:184–191

    Article  Google Scholar 

  21. Elliott PM, Poloniecki J, Dickie S et al (2000) Sudden death in hypertrophic cardiomyopathy: identification of high risk patients. J Am Coll Cardiol 36:2212–2218

    Article  CAS  Google Scholar 

  22. Hicks KA, Tcheng JE, Bozkurt B et al (2015) 2014 ACC/AHA Key Data Elements and Definitions for Cardiovascular Endpoint Events in Clinical Trials: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Data Standards (Writing Committee to Develop Cardiovascular Endpoints Data Standards). J Am Coll Cardiol 66:403–469

  23. Ismail TF, Jabbour A, Gulati A et al (2014) Role of late gadolinium enhancement cardiovascular magnetic resonance in the risk stratification of hypertrophic cardiomyopathy. Heart 100:1851–1858

    Article  Google Scholar 

  24. Giganti F, Antunes S, Salerno A et al (2017) Gastric cancer: texture analysis from multidetector computed tomography as a potential preoperative prognostic biomarker. Eur Radiol 27:1831–1839

    Article  Google Scholar 

  25. Thévenin FS, Drapé JL, Biau D et al (2010) Assessment of vascular invasion by bone and soft tissue tumours of the limbs: usefulness of MDCT angiography. Eur Radiol 20:1524–1531

    Article  Google Scholar 

  26. Ng F, Ganeshan B, Kozarski R, Miles KA, Goh V (2013) Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival. Radiology 266:177–184

    Article  Google Scholar 

  27. Machii M, Satoh H, Shiraki K et al (2014) Distribution of late gadolinium enhancement in end-stage hypertrophic cardiomyopathy and dilated cardiomyopathy: differential diagnosis and prediction of cardiac outcome. Magn Reson Imaging 32:118–124

    Article  CAS  Google Scholar 

  28. Authors/Task Force members, Elliott PM, Anastasakis A et al (2014) 2014 ESC Guidelines on diagnosis and management of hypertrophic cardiomyopathy: the Task Force for the Diagnosis and Management of Hypertrophic Cardiomyopathy of the European Society of Cardiology (ESC). Eur Heart J 35:2733–2779

  29. Maron BJ, Maron MS (2016) LGE Means Better Selection of HCM Patients for Primary Prevention Implantable Defibrillators. JACC Cardiovasc Imaging 9:1403–1406

    Article  Google Scholar 

  30. Galati G, Leone O, Pasquale F et al (2016) Histologic and histometric characterization of myocardial fibrosis in end-stage hypertrophic cardiomyopathy: a clinical-pathological study of 30 explanted hearts. Circ Heart Fail 9:e003090

  31. Wu KC (2017) Sudden Cardiac Death Substrate Imaged by Magnetic Resonance Imaging: From Investigational Tool to Clinical Applications. Circ Cardiovasc Imaging 10(7):e005461

    Article  Google Scholar 

  32. Harris KM, Spirito P, Maron MS et al (2006) Prevalence, clinical profile, and significance of left ventricular remodeling in the end-stage phase of hypertrophic cardiomyopathy. Circulation 114:216–225

    Article  Google Scholar 

Download references

Funding

The study was supported by the major international (regional) joint research project of National Science Foundation of China (No.81620108015), Capital Characteristic and Clinical Application Research Fund from the Beijing Municipal Commission of Science and Technology (No.Z161100000516110), National Natural Science Foundation of China (No. 81771924, 61231004, 81501616, 81301346, 81501549, 81527805 and 81671851), Science and Technology Service Network Initiative of the Chinese Academy of Sciences (No.KFJ-SW-STS-160), Key Research Program of the Chinese Academy of Sciences (No.KGZD-EW-T03), Instrument Developing Project (No.YZ201502), Strategic Priority Research Program (B) of the CAS (No.XDB02060010), Beijing Municipal Science and Technology Commission (No.Z161100002616022) and the Youth Innovation Promotion Association CAS.

Author information

Author notes

  1. Sainan Cheng and Mengjie Fang contributed equally to this work. Di Dong and Shihua Zhao contributed equally as the corresponding authors.

Authors and Affiliations

  1. Department of Cardiac MR, Fuwai Hospital, National Center for Cardiovascular Diseases of China, Chinese Academy of Medical Sciences and Peking Union Medical College, Fuwai Hospital, No. 167 Beilishi Road, Beijing, 100037, China

    Sainan Cheng, Chen Cui, Xiuyu Chen, Gang Yin & Shihua Zhao

  2. CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, No. 95 East Zhongguancun Road, Beijing, 100190, China

    Mengjie Fang, Di Dong & Jie Tian

  3. University of Chinese Academy of Sciences, Beijing, 100049, China

    Mengjie Fang, Di Dong & Jie Tian

  4. Cardiovascular Magnetic Resonance Unit, NIHR Biomedical Research Unit, Royal Brompton Hospital, Sydney Street, London, SW3 6NP, UK

    Sanjay K. Prasad

Authors
  1. Sainan Cheng
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  2. Mengjie Fang
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  3. Chen Cui
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  4. Xiuyu Chen
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  7. Di Dong
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  9. Shihua Zhao
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Corresponding authors

Correspondence to Di Dong or Shihua Zhao.

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Guarantor

The scientific guarantor of this publication is Dr. Shihua Zhao.

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

Di Dong and Mengjie Fang have significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board because of the retrospective nature.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

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Cheng, S., Fang, M., Cui, C. et al. LGE-CMR-derived texture features reflect poor prognosis in hypertrophic cardiomyopathy patients with systolic dysfunction: preliminary results. Eur Radiol 28, 4615–4624 (2018). https://doi.org/10.1007/s00330-018-5391-5

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  • DOI: https://doi.org/10.1007/s00330-018-5391-5

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