European Radiology

, Volume 28, Issue 11, pp 4615–4624 | Cite as

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

  • Sainan Cheng
  • Mengjie Fang
  • Chen Cui
  • Xiuyu Chen
  • Gang Yin
  • Sanjay K. Prasad
  • Di DongEmail author
  • Jie Tian
  • Shihua ZhaoEmail author



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.


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.


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.


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.


Hypertrophic cardiomyopathy Cardiac magnetic resonance Late gadolinium enhancement Texture features Event-free survival 

Abbreviations and acronyms


Cardiac magnetic resonance


Cardiac resynchronization therapy defibrillator


Grey-level co-occurrence matrix


Grey-level run-length matrix


Hypertrophic cardiomyopathy


Intra-/inter-class correlation coefficient


Implantable cardioverter defibrillator


Late gadolinium enhancement


Left ventricular


Left ventricular ejection fraction


New York Heart Association


Receiver operating characteristic


Region of interest


Sudden cardiac death


Standard deviation



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.

Compliance with ethical standards


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.


• retrospective

• diagnostic or prognostic study

• performed at one institution

Supplementary material

330_2018_5391_MOESM1_ESM.docx (173 kb)
ESM 1 (DOCX 172 kb)


  1. 1.
    Semsarian C, Ingles J, Maron MS, Maron BJ (2015) New perspectives on the prevalence of hypertrophic cardiomyopathy. J Am Coll Cardiol 65:1249–1254CrossRefGoogle Scholar
  2. 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 64:83–99CrossRefGoogle Scholar
  3. 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 55:2614–2662Google Scholar
  4. 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 40:2156–2164CrossRefGoogle Scholar
  5. 5.
    O'Hanlon R, Grasso A, Roughton M et al (2010) Prognostic significance of myocardial fibrosis in hypertrophic cardiomyopathy. J Am Coll Cardiol 56:867–874CrossRefGoogle Scholar
  6. 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 3:51–58Google Scholar
  7. 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 5:370–377CrossRefGoogle Scholar
  8. 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 130:484–495Google Scholar
  9. 9.
    Kim JH, Ko ES, Lim Y et al (2017) Breast Cancer Heterogeneity: MR Imaging Texture Analysis and Survival Outcomes. Radiology 282:665–675CrossRefGoogle Scholar
  10. 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 51:177–185CrossRefGoogle Scholar
  11. 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 280:880–889CrossRefGoogle Scholar
  12. 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):940–948CrossRefGoogle Scholar
  13. 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 266:177–184CrossRefGoogle Scholar
  14. 14.
    Gillies RJ, Kinahan PE, Hricak H (2015) Radiomics: images are more than pictures, they are data. Radiology 278:563–577CrossRefGoogle Scholar
  15. 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 35:2733–2779Google Scholar
  16. 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 61:65–70CrossRefGoogle Scholar
  17. 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 16:233–270CrossRefGoogle Scholar
  18. 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):35CrossRefGoogle Scholar
  19. 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 6:587–596PubMedGoogle Scholar
  20. 20.
    Maron M, Appelbaum E, Harrigan C et al (2008) Clinical profile and significance of delayed enhancement in hypertrophic cardiomyopathy. Circ Heart Fail 1:184–191CrossRefGoogle Scholar
  21. 21.
    Elliott PM, Poloniecki J, Dickie S et al (2000) Sudden death in hypertrophic cardiomyopathy: identification of high risk patients. J Am Coll Cardiol 36:2212–2218CrossRefGoogle Scholar
  22. 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 66:403–469Google Scholar
  23. 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 100:1851–1858CrossRefGoogle Scholar
  24. 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 27:1831–1839CrossRefGoogle Scholar
  25. 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 20:1524–1531CrossRefGoogle Scholar
  26. 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 266:177–184CrossRefGoogle Scholar
  27. 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 32:118–124CrossRefGoogle Scholar
  28. 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 35:2733–2779Google Scholar
  29. 29.
    Maron BJ, Maron MS (2016) LGE Means Better Selection of HCM Patients for Primary Prevention Implantable Defibrillators. JACC Cardiovasc Imaging 9:1403–1406CrossRefGoogle Scholar
  30. 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:e003090Google Scholar
  31. 31.
    Wu KC (2017) Sudden Cardiac Death Substrate Imaged by Magnetic Resonance Imaging: From Investigational Tool to Clinical Applications. Circ Cardiovasc Imaging 10(7):e005461CrossRefGoogle Scholar
  32. 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 114:216–225CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  • Sainan Cheng
    • 1
  • Mengjie Fang
    • 2
    • 3
  • Chen Cui
    • 1
  • Xiuyu Chen
    • 1
  • Gang Yin
    • 1
  • Sanjay K. Prasad
    • 4
  • Di Dong
    • 2
    • 3
    Email author
  • Jie Tian
    • 2
    • 3
  • Shihua Zhao
    • 1
    Email author
  1. 1.Department of Cardiac MR, Fuwai Hospital, National Center for Cardiovascular Diseases of ChinaChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
  2. 2.CAS Key Laboratory of Molecular Imaging, Institute of AutomationChinese Academy of SciencesBeijingChina
  3. 3.University of Chinese Academy of SciencesBeijingChina
  4. 4.Cardiovascular Magnetic Resonance Unit, NIHR Biomedical Research UnitRoyal Brompton HospitalLondonUK

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