Share：
Share this content in WeChat
X
[Chinese][PDF]73463
• Review •
Research progress of MRI radiomics in cardiac diseases
WU Xi  TANG Lingling  HU Yuntao  JIA Qing  LIU Nian  HUANG Xiaohua  SUN Jiayu 

Cite this article as: Wu X, Tang LL, Hu YT, et al. Research progress of MRI radiomics in cardiac diseases[J]. Chin J Magn Reson Imaging, 2021, 12(11): 113-116. DOI:10.12015/issn.1674-8034.2021.11.028.


[Abstract] Radiomics excavates, extracts and analyzes the textural features from standard medical images with high throughput to achieve quantitative analysis of heterogeneity of myocardium of different diseases, to improve the diagnostic accuracy of cardiac diseases, provide more accurate treatment plans and assess the prognosis of diseases. This review focuses on the research progress of MRI radiomics in cardiac diseases based on different sequences, including the sequences of cine magnetic resonance imaging (cine-MRI), native T1 mapping, T1 wighted imaging (T1WI), late gadolinium enhancement (LGE) and so on.
[Keywords] radiomics；texture analysis；magnetic resonance imaging；cardiac diseases；ischemic cardiomyopathy

WU Xi1   TANG Lingling1   HU Yuntao1   JIA Qing1   LIU Nian1   HUANG Xiaohua1*   SUN Jiayu2  

1 Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China

2 Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China

Huang XH, E-mail: 15082797553@163.com

Conflicts of interest   None.

ACKNOWLEDGMENTS This article is supported by Bureau of Science & Teclnology and Intellectual Property Nanchong City (No. 19SXHZ0429).
Received  2021-07-20
Accepted  2021-08-23
DOI: 10.12015/issn.1674-8034.2021.11.028
Cite this article as: Wu X, Tang LL, Hu YT, et al. Research progress of MRI radiomics in cardiac diseases[J]. Chin J Magn Reson Imaging, 2021, 12(11): 113-116. DOI:10.12015/issn.1674-8034.2021.11.028.

[1]
Martin-Isla C, Campello VM, Izquierdo C, et al. Image-based cardiac diagnosis with machine learning: a review[J]. Fron Cardiovasc Med, 2020, 7: 1. DOI: 10.3389/fcvm.2020.00001.
[2]
Schulz-Menger J, Bluemke DA, Bremerich J, et al. Standardized image interpretation and post-processing in cardiovascular magnetic resonance - 2020 update: society for cardiovascular magnetic resonance (SCMR): board of trustees task force on standardized post-processing[J]. J Cardiovasc Magn Reson, 2020, 22(1): 19. DOI: 10.1186/s12968-020-00610-6.
[3]
Ma QM, Ma Y, Wang XN, et al. A radiomic nomogram for prediction of major adverse cardiac events in ST-segment elevation myocardial infarction[J]. Eur Radiol, 2021, 31(2): 1140-1150. DOI: 10.1007/s00330-020-07176-y.
[4]
Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: Extracting more information from medical images using advanced feature analysis[J]. Eur J Cancer, 2012, 48(4): 441-446. DOI: 10.1016/j.ejca.2011.11.036.
[5]
Kolossvary M, De Cecco CN, Feuchtner G, et al. Advanced atherosclerosis imaging by CT: Radiomics, machine learning and deep learning[J]. J Cardiovasc Comput Tomogr, 2019, 13(5): 274-280. DOI: 10.1016/j.jcct.2019.04.007.
[6]
Hassani C, Saremi F, Varghese BA, et al. Myocardial radiomics in cardiac MRI[J]. AJR Am J Roentgenol, 2020, 214(3): 536-545. DOI: 10.2214/AJR.19.21986.
[7]
Mannil M, Eberhard M, von Spiczak J, et al. Artificial intelligence and texture analysis in cardiac imaging[J]. Curr Cardiol Reports, 2020, 22(11): 131. DOI: 10.1007/s11886-020-01402-1.
[8]
Griffié J, Shannon M, Bromley CL, et al. A Bayesian cluster analysis method for single-molecule localization microscopy data[J]. Nature protocols, 2016, 11(12): 2499-2514. DOI: 10.1038/nprot.2016.149.
[9]
Jernberg T, Hasvold P, Henriksson M, et al. Cardiovascular risk in post-myocardial infarction patients: nationwide real world data demonstrate the importance of a long-term perspective[J]. Eur Heart J, 2015, 36(19): 1163-1170. DOI: 10.1093/eurheartj/ehu505.
[10]
Baessler B, Mannil M, Oebel S, et al. Subacute and chronic left ventricular myocardial scar: accuracy of texture analysis on nonenhanced cine MR images[J]. Radiology, 2018, 286(1): 103-112. DOI: 10.1148/radiol.2017170213.
[11]
Larroza A, López-Lereu MP, Monmeneu JV, et al. Texture analysis of cardiac cine magnetic resonance imaging to detect nonviable segments in patients with chronic myocardial infarction[J]. Med Physics, 2018, 45(4): 1471-1480. DOI: 10.1002/mp.12783.
[12]
Mancio J, Pashakhanloo F, El-Rewaidy H, et al. Machine learning phenotyping of scarred myocardium from cine in hypertrophic cardiomyopathy[J]. Eur Heart J Cardiovasc Imaging, 2021. [ DOI: ]. DOI: 10.1093/ehjci/jeab056.
[13]
Jiang S, Zhang GMY, Wang YN, et al. Feasibility study of myocardial texture analysis to differentiate between hypertrophic and amyloid cardiomyopathies[J]. Radiol Pract, 2017, 32(12): 1225-1228. DOI: 10.13609/j.cnki.1000-0313.2017.12.004
[14]
Schofield R, Ganeshan B, Fontana M, et al. Texture analysis of cardiovascular magnetic resonance cine images differentiates aetiologies of left ventricular hypertrophy[J]. Clin Radiol, 2019, 74(2): 140-149. DOI: 10.1016/j.crad.2018.09.016.
[15]
Bello GA, Dawes T, Duan J, et al. Deep learning cardiac motion analysis for human survival prediction[J]. Nat Mach Intell, 2019, 1(2): 95-104. DOI: 10.1038/s42256-019-0019-2.
[16]
Zheng Q, Delingette H, Ayache N. Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flow[J]. Med Image Anal, 2019, 56: 80-95. DOI: 10.1016/j.media.2019.06.001.
[17]
Zhang N, Yang G, Gao ZF, et al. Deep learning for diagnosis of chronic myocardial infarction on nonenhanced cardiac cine MRI[J]. Radiology, 2019, 291(3): 606-617. DOI: 10.1148/radiol.2019182304.
[18]
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[J]. Eur Radiol, 2021, 31(5): 2706-2715. DOI: 10.1007/s00330-020-07370-y.
[19]
Raisi-Estabragh Z, Izquierdo C, Campello VM, et al. Cardiac magnetic resonance radiomics: basic principles and clinical perspectives[J]. Eur Heart J Cardiovasc Imaging, 2020, 21(4): 349-356. DOI: 10.1093/ehjci/jeaa028.
[20]
Wang J, Yang FY, Liu WT, et al. Radiomic analysis of native T1 mapping images discriminates between MYH7 and MYBPC3-related hypertrophic cardiomyopathy[J]. J Magn Reson Imaging, 2020, 52(6): 1714-1721. DOI: 10.1002/jmri.27209.
[21]
Neisius U, El-Rewaidy H, Nakamori S, et al. Radiomic analysis of myocardial native T1 imaging discriminates between hypertensive heart disease and hypertrophic cardiomyopathy[J]. JACC Cardiovascular Imaging, 2019, 12(10): 1946-1954. DOI: 10.1016/j.jcmg.2018.11.024.
[22]
Neisius U, El-Rewaidy H, Kucukseymen S, et al. Texture signatures of native myocardial T1 as novel imaging markers for identification of hypertrophic cardiomyopathy patients without scar[J]. J Magn Reson Imaging, 2020, 52(3): 906-919. DOI: 10.1002/jmri.27048.
[23]
Zhou H, An DA, Ni ZH, et al. Texture analysis of native T1 images as a novel method for noninvasive assessment of uremic cardiomyopathy[J]. J Magn Reson Imaging, 2021, 54(1): 290-300. DOI: 10.1002/jmri.27529.
[24]
Shao XN, Sun YJ, Xiao KT, et al. Texture analysis of magnetic resonance T1 mapping with dilated cardiomyopathy[J]. Medicine, 2018, 97(37): e12246. DOI: 10.1097/MD.0000000000012246.
[25]
Ma QM, Ma Y, Yu TT, et al. Radiomics of non-contrast-enhanced T1 mapping: diagnostic and predictive performance for myocardial injury in acute ST-segment-elevation myocardial infarction[J]. Korean J Radiol, 2021, 22(4): 535-546. DOI: 10.3348/kjr.2019.0969.
[26]
Jang J, Ngo LH, Mancio J, et al. Reproducibility of segmentation-based myocardial radiomic features with cardiac MRI[J]. Radiology, Cardiothorac Imaging, 2020, 2(3): e190216. DOI: 10.1148/ryct.2020190216.
[27]
Wu T, Sun HQ, Liu XM, et al. Myocardial magnetic resonance texture characteristcs of healthy volunteers.[J]. J Sichuan University (Med Sci Ed), 2019, 50(4): 494-499. DOI: 10.13464/j.scuxbyxb.2019.04.008.
[28]
Baessler B, Mannil M, Maintz D, et al. Texture analysis and machine learning of non-contrast T1-weighted MR images in patients with hypertrophic cardiomyopathy-Preliminary results[J]. Eur J Radiol, 2018, 102:61-67. DOI: 10.1016/j.ejrad.2018.03.013.
[29]
de Boer RA, De Keulenaer G, Bauersachs J, et al. Towards better definition, quantification and treatment of fibrosis in heart failure. A scientific roadmap by the Committee of Translational Research of the Heart Failure Association (HFA) of the European Society of Cardiology[J]. Eur J Heart Fail, 2019, 21(3): 272-285. DOI: 10.1002/ejhf.1406.
[30]
Larroza A, Materka A, Lopez-Lereu MP, et al. Differentiation between acute and chronic myocardial infarction by means of texture analysis of late gadolinium enhancement and cine cardiac magnetic resonance imaging[J]. Eur J Radiol, 2017, 92: 78-83. DOI: 10.1016/j.ejrad.2017.04.024.
[31]
Gibbs T, Villa A, Sammut E, et al. Quantitative assessment of myocardial scar heterogeneity using cardiovascular magnetic resonance texture analysis to risk stratify patients post-myocardial infarction[J]. Clin Radiol, 2018, 73(12): 1017-1059. DOI: 10.1016/j.crad.2018.08.012.
[32]
Kotu LP, Engan K, Borhani R, et al. Cardiac magnetic resonance image-based classification of the risk of arrhythmias in post-myocardial infarction patients[J]. Artif Intell Med, 2015, 64(3): 205-215. DOI: 10.1016/j.artmed.2015.06.001.
[33]
Gould J, Porter B, Claridge S, et al. Mean entropy predicts implantable cardioverter-defibrillator therapy using cardiac magnetic resonance texture analysis of scar heterogeneity[J]. Heart Rhythm, 2019, 16(8): 1242-1250. DOI: 10.1016/j.hrthm.2019.03.001.
[34]
Gould J, Porter B, Sidhu BS, et al. High mean entropy calculated from cardiac MRI texture analysis is associated with antitachycardia pacing failure[J]. Pacing Clin Electrophysiol, 2020, 43(7): 737-745. DOI: 10.1111/pace.13969.
[35]
Di Noto T, von Spiczak J, Mannil M, et al. Radiomics for distinguishing myocardial infarction from myocarditis at late gadolinium enhancement at MRI: comparison with subjective visual analysis[J]. Radiol Cardiothorac Imaging, 2019, 1(5): e180026. DOI: 10.1148/ryct.2019180026.
[36]
Cheng SN, Fang MJ, Cui C, et al. LGE-CMR-derived texture features reflect poor prognosis in hypertrophic cardiomyopathy patients with systolic dysfunction: preliminary results[J]. Eur Radiol, 2018, 28(11): 4615-4624. DOI: 10.1007/s00330-018-5391-5.
[37]
Amano Y, Suzuki Y, Yanagisawa F, et al. Relationship between extension or texture features of late gadolinium enhancement and ventricular tachyarrhythmias in hypertrophic cardiomyopathy[J]. Biomed Res Int, 2018, 2018:1-6. DOI: 10.1155/2018/4092469.
[38]
Wan K, Li WH, Sun JY, et al. Regional amyloid distribution and impact on mortality in light-chain amyloidosis: a T1 mapping cardiac magnetic resonance study[J]. Amyloid, 2019, 26(1): 45-51. DOI: 10.1080/13506129.2019.1578742.
[39]
Chen BH, An D, He J, et al. Myocardial extracellular volume fraction radiomics analysis for differentiation of reversible versus irreversible myocardial damage and prediction of left ventricular adverse remodeling after ST-elevation myocardial infarction[J]. Eur Radiol, 2021, 31(1): 504-514. DOI: 10.1007/s00330-020-07117-9.
[40]
Shi RY, Wu R, An DL, et al. Texture analysis applied in T1 maps and extracellular volume obtained using cardiac MRI in the diagnosis of hypertrophic cardiomyopathy and hypertensive heart disease compared with normal controls[J]. Clin Radiol, 2021, 76(3): 236-239. DOI: 10.1016/j.crad.2020.11.001.
[41]
Baessler B, Luecke C, Lurz J, et al. Cardiac MRI Texture analysis of T1 and T2 maps in patients with infarctlike acute myocarditis[J]. Radiology, 2018, 289(2): 357-365. DOI: 10.1148/radiol.2018180411.
[42]
Baessler B, Luecke C, Lurz J, et al. Cardiac MRI and texture analysis of myocardial T1 and T2 maps in myocarditis with acute versus chronic symptoms of heart failure[J]. Radiology, 2019, 292(3): 608-617. DOI: 10.1148/radiol.2019190101.
[43]
Mannil M, Kato K, Manka R, et al. Prognostic value of texture analysis from cardiac magnetic resonance imaging in patients with Takotsubo syndrome: a machine learning based proof-of-principle approach[J]. Sci Rep, 2020, 10(1): 20537. DOI: 10.1038/s41598-020-76432-4.

PREV Progresses of quantitative magnetic resonance imaging for myocardial tissue evaluation
NEXT Advances of functional magnetic resonance imaging in quantitative diagnosis of liver fibrosis
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn