Skip to main content
SpringerLink
Log in

Late gadolinium enhanced cardiac MR derived radiomics approach for predicting all-cause mortality in cardiac amyloidosis: a multicenter study

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

A Commentary to this article was published on 01 September 2023

Abstract

Objectives

To evaluate the prognostic value of radiomics features based on late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) images in patients with cardiac amyloidosis (CA).

Methods

This retrospective study included 120 CA patients undergoing CMR at three institutions. Radiomics features were extracted from global and three different segments (base, mid-ventricular, and apex) of left ventricular (LV) on short-axis LGE images. Primary endpoint was all-cause mortality. The predictive performance of the radiomics features and semi-quantitative and quantitative LGE parameters were compared by ROC. The AUC was used to observe whether Rad-score had an incremental value for clinical stage. The Kaplan–Meier curve was used to further stratify the risk of CA patients.

Results

During a median follow-up of 12.9 months, 30% (40/120) patients died. There was no significant difference in the predictive performance of the radiomics model in different LV sections in the validation set (AUCs of the global, basal, middle, and apical radiomics model were 0.75, 0.77, 0.76, and 0.77, respectively; all p > 0.05). The predictive performance of the Rad-score of the base-LV was better than that of the LGE total enhancement mass (AUC:0.77 vs. 0.54, p < 0.001) and LGE extent (AUC: 0.77 vs. 0.53, p = 0.004). Rad-score combined with Mayo stage had better predictive performance than Mayo stage alone (AUC: 0.86 vs. 0.81, p = 0.03). Rad-score (≥ 0.66) contributed to the risk stratification of all-cause mortality in CA.

Conclusions

Compared to quantitative LGE parameters, radiomics can better predict all-cause mortality in CA, while the combination of radiomics and Mayo stage could provide higher predictive accuracy.

Clinical relevance statement

Radiomics analysis provides incremental value and improved risk stratification for all-cause mortality in patients with cardiac amyloidosis.

Key Points

• Radiomics in LV-base was superior to LGE semi-quantitative and quantitative parameters for predicting all-cause mortality in CA.

• Rad-score combined with Mayo stage had better predictive performance than Mayo stage alone or radiomics alone.

• Rad-score ≥ 0.66 was associated with a significantly increased risk of all-cause mortality in CA patients.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Abbreviations

AL:

Immunoglobulin light chain

CA:

Cardiac amyloidosis

CMR:

Cardiac magnetic resonance

cTNT:

Cardiac troponin T

EF:

Ejection fraction

FA:

Flip angle

ICC:

Intra-class correlation coefficient

LGE:

Late gadolinium enhancement

LV:

Left ventricular

NT-proBNP:

N-terminal pro B-type natriuretic peptide

PSIR:

Phase-sensitive reconstruction

Rad-score:

Radiomics score

RV:

Right ventricular

SD:

Standard deviation

SSFP:

Steady-state free precession

TE:

Echo time

TR:

Repetition time

References

  1. Ridouani F, Damy T, Tacher V et al (2018) Myocardial native T2 measurement to differentiate light-chain and transthyretin cardiac amyloidosis and assess prognosis. J Cardiovasc Magn Reson 20:58

    Article  PubMed  PubMed Central  Google Scholar 

  2. Falk RH, Alexander KM, Liao R, Dorbala S (2016) AL (Light-Chain) Cardiac amyloidosis: a review of diagnosis and therapy. J Am Coll Cardiol 68:1323–1341

    Article  PubMed  Google Scholar 

  3. Rubin J, Maurer MS (2020) Cardiac amyloidosis: overlooked, underappreciated, and treatable. Annu Rev Med 71:203–219

    Article  CAS  PubMed  Google Scholar 

  4. Gertz MA, Dispenzieri A, Sher T (2015) Pathophysiology and treatment of cardiac amyloidosis. Nat Rev Cardiol 12:91–102

    Article  CAS  PubMed  Google Scholar 

  5. Arenja N, Andre F, Riffel JH et al (2019) Prognostic value of novel imaging parameters derived from standard cardiovascular magnetic resonance in high risk patients with systemic light chain amyloidosis. J Cardiovasc Magn Reson 21:53

    Article  PubMed  PubMed Central  Google Scholar 

  6. Banypersad SM, Fontana M, Maestrini V et al (2015) T1 mapping and survival in systemic light-chain amyloidosis. Eur Heart J 36:244–251

    Article  PubMed  Google Scholar 

  7. Fontana M, Corovic A, Scully P, Moon JC (2019) Myocardial amyloidosis: the exemplar interstitial disease. JACC Cardiovasc Imaging 12:2345–2356

    Article  PubMed  Google Scholar 

  8. Dorbala S, Cuddy S, Falk RH (2020) How to image cardiac amyloidosis: a practical approach. JACC Cardiovasc Imaging 13:1368–1383

    Article  PubMed  Google Scholar 

  9. Kumar S, Dispenzieri A, Lacy MQ et al (2012) Revised prognostic staging system for light chain amyloidosis incorporating cardiac biomarkers and serum free light chain measurements. J Clin Oncol 30:989–995

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Pregenzer-Wenzler A, Abraham J, Barrell K, Kovacsovics T, Nativi-Nicolau J (2020) Utility of biomarkers in cardiac amyloidosis. JACC Heart Fail 8:701–711

    Article  PubMed  Google Scholar 

  11. Fontana M, Chung R, Hawkins PN, Moon JC (2015) Cardiovascular magnetic resonance for amyloidosis. Heart Fail Rev 20:133–144

    Article  CAS  PubMed  Google Scholar 

  12. Fontana M, Pica S, Reant P et al (2015) Prognostic value of late gadolinium enhancement cardiovascular magnetic resonance in cardiac amyloidosis. Circulation 132:1570–1579

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Wan K, Li W, Sun J et al (2019) Regional amyloid distribution and impact on mortality in light-chain amyloidosis: a T1 mapping cardiac magnetic resonance study. Amyloid 26:45–51

    Article  CAS  PubMed  Google Scholar 

  14. Raina S, Lensing SY, Nairooz RS et al (2016) Prognostic value of late gadolinium enhancement CMR in systemic amyloidosis. JACC Cardiovasc Imaging 9:1267–1277

    Article  PubMed  Google Scholar 

  15. Raisi-Estabragh Z, Izquierdo C, Campello VM et al (2020) Cardiac magnetic resonance radiomics: basic principles and clinical perspectives. Eur Heart J Cardiovasc Imaging 21:349–356

    Article  PubMed  PubMed Central  Google Scholar 

  16. Avanzo M, Stancanello J, El Naqa I (2017) Beyond imaging: the promise of radiomics. Phys Med 38:122–139

    Article  PubMed  Google Scholar 

  17. Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577

    Article  PubMed  Google Scholar 

  18. Cheng S, Fang M, Cui C et al (2018) LGE-CMR-derived texture features reflect poor prognosis in hypertrophic cardiomyopathy patients with systolic dysfunction: preliminary results. Eur Radiol 28:4615–4624

    Article  PubMed  Google Scholar 

  19. Ma Q, Ma Y, Wang X et al (2021) A radiomic nomogram for prediction of major adverse cardiac events in ST-segment elevation myocardial infarction. Eur Radiol 31:1140–1150

    Article  PubMed  Google Scholar 

  20. Kotu LP, Engan K, Borhani R et al (2015) Cardiac magnetic resonance image-based classification of the risk of arrhythmias in post-myocardial infarction patients. Artif Intell Med 64:205–215

    Article  PubMed  Google Scholar 

  21. Ma Q, Ma Y, Yu T, Sun Z, Hou Y (2021) Radiomics of non-contrast-enhanced T1 mapping: diagnostic and predictive performance for myocardial injury in acute ST-segment-elevation myocardial infarction. Korean J Radiol 22:535–546

    Article  PubMed  Google Scholar 

  22. Dorbala S, Ando Y, Bokhari S et al (2020) ASNC/AHA/ASE/EANM/HFSA/ISA/SCMR/SNMMI expert consensus recommendations for multimodality imaging in cardiac amyloidosis: Part 2 of 2-Diagnostic criteria and appropriate utilization. J Nucl Cardiol 27:659–673

    Article  PubMed  Google Scholar 

  23. Garcia-Pavia P, Rapezzi C, Adler Y et al (2021) Diagnosis and treatment of cardiac amyloidosis: a position statement of the ESC Working Group on Myocardial and Pericardial Diseases. Eur Heart J 42:1554–1568

    Article  PubMed  PubMed Central  Google Scholar 

  24. Dungu JN, Valencia O, Pinney JH et al (2014) CMR-based differentiation of AL and ATTR cardiac amyloidosis. JACC Cardiovasc Imaging 7:133–142

    Article  PubMed  Google Scholar 

  25. Wan K, Sun J, Han Y et al (2018) Increased prognostic value of query amyloid late enhancement score in light-chain cardiac amyloidosis. Circ J 82:739–746

    Article  CAS  PubMed  Google Scholar 

  26. Williams LK, Forero JF, Popovic ZB et al (2017) Patterns of CMR measured longitudinal strain and its association with late gadolinium enhancement in patients with cardiac amyloidosis and its mimics. J Cardiovasc Magn Reson 19:61

    Article  PubMed  PubMed Central  Google Scholar 

  27. Boynton SJ, Geske JB, Dispenzieri A et al (2016) LGE provides incremental prognostic information over serum biomarkers in AL cardiac amyloidosis. JACC Cardiovasc Imaging 9:680–686

    Article  PubMed  Google Scholar 

  28. Kotecha T, Martinez-Naharro A, Treibel TA et al (2018) Myocardial edema and prognosis in amyloidosis. J Am Coll Cardiol 71:2919–2931

    Article  PubMed  Google Scholar 

  29. Jeung MY, Germain P, Croisille P, El ghannudi S, Roy C, Gangi A (2012) Myocardial tagging with MR imaging: overview of normal and pathologic findings. Radiographics 32:1381–1398

    Article  PubMed  Google Scholar 

  30. Oda S, Utsunomiya D, Nakaura T et al (2017) Identification and assessment of cardiac amyloidosis by myocardial strain analysis of cardiac magnetic resonance imaging. Circ J 81:1014–1021

    Article  CAS  PubMed  Google Scholar 

  31. 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 2018:4092469

    Article  PubMed  PubMed Central  Google Scholar 

  32. Zhou XY, Tang CX, Guo YK et al (2022) Diagnosis of cardiac amyloidosis using a radiomics approach applied to late gadolinium-enhanced cardiac magnetic resonance images: a retrospective, multicohort, diagnostic study. Front Cardiovasc Med 9:818957

Download references

Acknowledgements

We thank our colleagues from multicenters for data support.

Funding

This study has received funding from the National Natural Science Foundation of China (8217933 for C.X.T.).

Author information

Author notes

  1. Drs. Zhou and Tang had equal contribution to this work.

Authors and Affiliations

  1. Department of Nuclear Medicine, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China

    Xi Yang Zhou, Jun Hao Li & Gui Fen Yang

  2. Department of Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China

    Xi Yang Zhou, Chun Xiang Tang, Jun Hao Li, Guang Ming Lu & Long Jiang Zhang

  3. Department of Radiology, West China Second University Hospital, Sichuan University, 20# South Renmin Road, Chengdu, 610041, Sichuan, China

    Ying Kun Guo

  4. National Clinical Research Center of Kidney Disease, Jinling Hospital, Nanjing University School of Medicine, Nanjing, 210002, Jiangsu, China

    Wen Cui Chen, Jin Zhou Guo, Gui Sheng Ren & Xiang Hua Huang

  5. Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 1, Shuaifuyuan, Dongcheng District, Beijing, 100730, China

    Xiao Li & Yi Ning Wang

Authors
  1. Xi Yang Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chun Xiang Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ying Kun Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wen Cui Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jin Zhou Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Gui Sheng Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xiao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jun Hao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Guang Ming Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Xiang Hua Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Yi Ning Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Long Jiang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Gui Fen Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Long Jiang Zhang or Gui Fen Yang.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Long Jiang Zhang, Department of Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, Jiangsu, China.

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

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the institutional review board.

Ethical approval

Institutional review board approval was obtained.

Study subjects or cohorts overlap

Our previous research included 200 patients with AL amyloidosis, including 139 internal data and 61 external data to assess the potential of a radiomics approach of LGE-CMR in the diagnosis of CA, and found that radiomics approach is a useful and complementary tool for the detection of CA. In this multicenter study, we included 120 patients with cardiac involvement among these 200 patients with AL amyloidosis to evaluate the prognostic value of radiomics.

Some study subjects or cohorts have been previously reported in a study. (Zhou XY, Tang CX, Guo YK et al (2022) Diagnosis of cardiac amyloidosis using a radiomics approach applied to late gadolinium-enhanced cardiac magnetic resonance images: a retrospective, multicohort, diagnostic study. Frontiers in Cardiovascular Medicine 9:818957).

Methodology

• Retrospective

• Prognostic study

• Multicenter study

Additional information

Publisher's note

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

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 191 kb)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, X.Y., Tang, C.X., Guo, Y.K. et al. Late gadolinium enhanced cardiac MR derived radiomics approach for predicting all-cause mortality in cardiac amyloidosis: a multicenter study. Eur Radiol 34, 402–410 (2024). https://doi.org/10.1007/s00330-023-09999-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-023-09999-x

Keywords

Search

Navigation