Skip to main content

Advertisement

SpringerLink
Log in

Combined brain and spinal FDG PET allows differentiation between ALS and ALS mimics

  • Original Article
  • Published:
European Journal of Nuclear Medicine and Molecular Imaging Aims and scope Submit manuscript

Abstract

Purpose

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder with on average a 1-year delay between symptom onset and diagnosis. Studies have demonstrated the value of [18F]-FDG PET as a sensitive diagnostic biomarker, but the discriminatory potential to differentiate ALS from patients with symptoms mimicking ALS has not been investigated. We investigated the combination of brain and spine [18F]-FDG PET-CT for differential diagnosis between ALS and ALS mimics in a real-life clinical diagnostic setting.

Methods

Patients with a suspected diagnosis of ALS (n = 98; 64.8 ± 11 years; 61 M) underwent brain and spine [18F]-FDG PET-CT scans. In 62 patients, ALS diagnosis was confirmed (67.8 ± 10 years; 35 M) after longitudinal follow-up (average 18.1 ± 8.4 months). In 23 patients, another disease was diagnosed (ALS mimics, 60.9 ± 12.9 years; 17 M) and 13 had a variant motor neuron disease, primary lateral sclerosis (PLS; n = 4; 53.6 ± 2.5 years; 2 M) and progressive muscular atrophy (PMA; n = 9; 58.4 ± 7.3 years; 7 M). Spine metabolism was determined after manual and automated segmentation. VOI- and voxel-based comparisons were performed. Moreover, a support vector machine (SVM) approach was applied to investigate the discriminative power of regional brain metabolism, spine metabolism and the combination of both.

Results

Brain metabolism was very similar between ALS mimics and ALS, whereas cervical and thoracic spine metabolism was significantly different (in standardised uptake values; cervical: ALS 2.1 ± 0.5, ALS mimics 1.9 ± 0.4; thoracic: ALS 1.8 ± 0.3, ALS mimics 1.5 ± 0.3). As both brain and spine metabolisms were very similar between ALS mimics and PLS/PMA, groups were pooled for accuracy analyses. Mean discrimination accuracy was 65.4%, 80.0% and 81.5%, using only brain metabolism, using spine metabolism and using both, respectively.

Conclusion

The combination of brain and spine FDG PET-CT with SVM classification is useful as discriminative biomarker between ALS and ALS mimics in a real-life clinical setting.

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

References

  1. Salameh JS, Brown RH Jr, Berry JD. Amyotrophic lateral sclerosis: review. Semin Neurol. 2015;35(4):469–76.

    PubMed  Google Scholar 

  2. Paganoni S, Macklin EA, Lee A, Murphy A, Chang J, Zipf A, et al. Diagnostic timelines and delays in diagnosing amyotrophic lateral sclerosis (ALS). Amyotroph Lateral Scler Frontotemporal Degener. 2014;15(5–6):453–6.

    PubMed  PubMed Central  Google Scholar 

  3. Pagani M, Oberg J, De Carli F, Calvo A, Moglia C, Canosa A, et al. Metabolic spatial connectivity in amyotrophic lateral sclerosis as revealed by independent component analysis. Hum Brain Mapp. 2016;37(3):942–53.

    PubMed  Google Scholar 

  4. Van Laere K, Vanhee A, Verschueren J, De Coster L, Driesen A, Dupont P, et al. Value of 18fluorodeoxyglucose-positron-emission tomography in amyotrophic lateral sclerosis: a prospective study. JAMA Neurol. 2014;71(5):553–61.

    PubMed  Google Scholar 

  5. Van Weehaeghe D, Ceccarini J, Delva A, Robberecht W, Van Damme P, Van Laere K. Prospective validation of 18F-FDG brain PET discriminant analysis methods in the diagnosis of amyotrophic lateral sclerosis. J Nucl Med. 2016;57(8):1238–43.

    PubMed  Google Scholar 

  6. Wheeler-Kingshott CA, Stroman PW, Schwab JM, Bacon M, Bosma R, Brooks J, et al. The current state-of-the-art of spinal cord imaging: applications. Neuroimage. 2014;84:1082–93.

    CAS  PubMed  Google Scholar 

  7. Marini C, Cistaro A, Campi C, Calvo A, Caponnetto C, Nobili FM, et al. A PET/CT approach to spinal cord metabolism in amyotrophic lateral sclerosis. Eur J Nucl Med Mol Imaging. 2016;43(11):2061–71.

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Branco LM, De Albuquerque M, De Andrade HM, Bergo FP, Nucci A, Franca MC Jr. Spinal cord atrophy correlates with disease duration and severity in amyotrophic lateral sclerosis. Amyotroph Lateral Scler Frontotemporal Degener. 2014;15(1–2):93–7.

    PubMed  Google Scholar 

  9. El Mendili MM, Cohen-Adad J, Pelegrini-Issac M, Rossignol S, Morizot-Koutlidis R, Marchand-Pauvert V, et al. Multi-parametric spinal cord MRI as potential progression marker in amyotrophic lateral sclerosis. PLoS One. 2014;9(4):e95516.

    PubMed  PubMed Central  Google Scholar 

  10. Grolez G, Kyheng M, Lopes R, Moreau C, Timmerman K, Auger F, et al. MRI of the cervical spinal cord predicts respiratory dysfunction in ALS. Sci Rep. 2018;8(1):1828.

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Costa J, Swash M, de Carvalho M. Awaji criteria for the diagnosis of amyotrophic lateral sclerosis: a systematic review. Arch Neurol. 2012;69(11):1410–6.

    PubMed  Google Scholar 

  12. Schrooten M, Smetcoren C, Robberecht W, Van Damme P. Benefit of the Awaji diagnostic algorithm for amyotrophic lateral sclerosis: a prospective study. Ann Neurol. 2011;70(1):79–83.

    PubMed  Google Scholar 

  13. Balendra R, Jones A, Jivraj N, Knights C, Ellis CM, Burman R, et al. Estimating clinical stage of amyotrophic lateral sclerosis from the ALS Functional Rating Scale. Amyotroph Lateral Scler Frontotemporal Degener. 2014;15(3–4):279–84.

    PubMed  Google Scholar 

  14. van Weehaeghe D, Ceccarini J, Willekens SM, de Vocht J, van Damme P, van Laere K. Is there a glucose metabolic signature of spreading TDP-43 pathology in amyotrophic lateral sclerosis? Q J Nucl Med Mol Imaging. 2017.

  15. Hearst MA. Support vector machines. IEEE Intell Syst App. 1998;13(4):18–21.

    Google Scholar 

  16. van den Hoff J, Lougovski A, Schramm G, Maus J, Oehme L, Petr J, et al. Correction of scan time dependence of standard uptake values in oncological PET. EJNMMI Res. 2014;4(1):18.

    PubMed  PubMed Central  Google Scholar 

  17. Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. Lect Notes Comput Sc. 2015;9351:234–41.

    Google Scholar 

  18. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res. 2014;15:1929–58.

    Google Scholar 

  19. De Leener B, Levy S, Dupont SM, Fonov VS, Stikov N, Collins DL, et al. SCT: spinal cord toolbox, an open-source software for processing spinal cord MRI data. Neuroimage. 2017;145:24–43.

    PubMed  Google Scholar 

  20. De Leener B, Mangeat G, Dupont S, Martin AR, Callot V, Stikov N, et al. Topologically preserving straightening of spinal cord MRI. J Magn Reson Imaging. 2017;46(4):1209–19.

    PubMed  Google Scholar 

  21. Tustison NJ, Cook PA, Klein A, Song G, Das SR, Duda JT, et al. Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements. Neuroimage. 2014;99:166–79.

    PubMed  Google Scholar 

  22. Avants BB, Epstein CL, Grossman M, Gee JC. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med Image Anal. 2008;12(1):26–41.

    CAS  PubMed  Google Scholar 

  23. Lai TH, Liu RS, Yang BH, Wang PS, Lin KP, Lee YC, et al. Cerebral involvement in spinal and bulbar muscular atrophy (Kennedy’s disease): a pilot study of PET. J Neurol Sci. 2013;335(1–2):139–44.

    PubMed  Google Scholar 

  24. Pedersen BK. Physical activity and muscle-brain crosstalk. Nat Rev Endocrinol. 2019;15(7):383–92.

    PubMed  Google Scholar 

  25. Clark BC, Mahato NK, Nakazawa M, Law TD, Thomas JS. The power of the mind: the cortex as a critical determinant of muscle strength/weakness. J Neurophysiol. 2014;112(12):3219–26.

    PubMed  PubMed Central  Google Scholar 

  26. Shimada H, Ishii K, Ishiwata K, Oda K, Suzukawa M, Makizako H, et al. Gait adaptability and brain activity during unaccustomed treadmill walking in healthy elderly females. Gait Posture. 2013;38(2):203–8.

    PubMed  Google Scholar 

  27. Tashiro M, Itoh M, Fujimoto T, Fujiwara T, Ota H, Kubota K, et al. 18F-FDG PET mapping of regional brain activity in runners. J Sports Med Phys Fitness. 2001;41(1):11–7.

    CAS  PubMed  Google Scholar 

  28. Watson N, Ji X, Yasuhara T, Date I, Kaneko Y, Tajiri N, et al. No pain, no gain: lack of exercise obstructs neurogenesis. Cell Transplant. 2015;24(4):591–7.

    PubMed  Google Scholar 

  29. Marini C, Morbelli S, Cistaro A, Campi C, Caponnetto C, Bauckneht M, et al. Interplay between spinal cord and cerebral cortex metabolism in amyotrophic lateral sclerosis. Brain. 2018;141(8):2272–9.

    PubMed  PubMed Central  Google Scholar 

  30. Cistaro A, Valentini MC, Chio A, Nobili F, Calvo A, Moglia C, et al. Brain hypermetabolism in amyotrophic lateral sclerosis: a FDG PET study in ALS of spinal and bulbar onset. Eur J Nucl Med Mol Imaging. 2012;39(2):251–9.

    CAS  PubMed  Google Scholar 

  31. Brettschneider J, Del Tredici K, Toledo JB, Robinson JL, Irwin DJ, Grossman M, et al. Stages of pTDP-43 pathology in amyotrophic lateral sclerosis. Ann Neurol. 2013;74(1):20–38.

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Feneberg E, Oeckl P, Steinacker P, Verde F, Barro C, Van Damme P, et al. Multicenter evaluation of neurofilaments in early symptom onset amyotrophic lateral sclerosis. Neurology. 2018;90(1):E22–30.

    CAS  PubMed  Google Scholar 

  33. van der Burgh HK, Westeneng HJ, Meier JM, van Es MA, Veldink JH, Hendrikse J, et al. Cross-sectional and longitudinal assessment of the upper cervical spinal cord in motor neuron disease. Neuroimage Clin. 2019;24:101984.

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors want to thank the nuclear medicine team for their contributions to the scanning and data handling, the PET radiopharmacy team and the medical physics team of UZ Leuven for their skilled contributions.

Author information

Author notes

  1. Donatienne Van Weehaeghe, Martijn Devrome, Michel Koole and Koen Van Laere contributed equally to this work.

Authors and Affiliations

  1. Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium

    Donatienne Van Weehaeghe, Martijn Devrome, Georg Schramm, Kristof Baete, Michel Koole & Koen Van Laere

  2. Division of Nuclear Medicine, UZ Leuven, Herestraat 49, 3000, Leuven, Belgium

    Donatienne Van Weehaeghe, Wies Deckers, Kristof Baete & Koen Van Laere

  3. Department of Neurology, University Hospital Leuven, Leuven, Belgium

    Joke De Vocht & Philip Van Damme

  4. Laboratory of Neurobiology, Center for Brain & Disease Research, VIB and KU Leuven, Leuven, Belgium

    Philip Van Damme

Authors
  1. Donatienne Van Weehaeghe
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Martijn Devrome
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Georg Schramm
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Joke De Vocht
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wies Deckers
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kristof Baete
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Philip Van Damme
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Michel Koole
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Koen Van Laere
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Donatienne Van Weehaeghe.

Ethics declarations

Conflict of interest

PVD, KVL and MK are senior clinical investigators of the research foundation of Flandres (FWO). PVD is supported by the E. von Behring Chair for Neuromuscular and Neurodegenerative Disorders, the ALS Liga België and the KU Leuven funds ‘Een Hart voor ALS’, ‘Laeversfonds voor ALS Onderzoek’ and ‘the Valéry Perrier Race against ALS fund’. MD and DVW and PhD fellows from FWO. JDV is funded by the Klinische onderzoeks- en opleidingsraad (KOOR) of the University Hospitals Leuven. No other conflicts of interest were present.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

This retrospective study was approved by the local University Hospital Ethics Committee; because of its retrospective nature, the need for written consent was waived.

Additional information

Publisher’s note

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

This article is part of the Topical Collection on Neurology

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Van Weehaeghe, D., Devrome, M., Schramm, G. et al. Combined brain and spinal FDG PET allows differentiation between ALS and ALS mimics. Eur J Nucl Med Mol Imaging 47, 2681–2690 (2020). https://doi.org/10.1007/s00259-020-04786-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00259-020-04786-y

Keywords

Search

Navigation