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Automated assessment of FDG-PET for differential diagnosis in patients with neurodegenerative disorders

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

Abstract

Purpose

To review literature until November 2015 and reach a consensus on whether automatic semi-quantification of brain FDG-PET is useful in the clinical setting for neurodegenerative disorders.

Methods

A literature search was conducted in Medline, Embase, and Google Scholar. Papers were selected with a lower limit of 30 patients (no limits with autopsy confirmation). Consensus recommendations were developed through a Delphi procedure, based on the expertise of panelists, who were also informed about the availability and quality of evidence, assessed by an independent methodology team.

Results

Critical outcomes were available in nine among the 17 papers initially selected. Only three papers performed a direct comparison between visual and automated assessment and quantified the incremental value provided by the latter. Sensitivity between visual and automatic analysis is similar but automatic assessment generally improves specificity and marginally accuracy. Also, automated assessment increases diagnostic confidence. As expected, performance of visual analysis is reported to depend on the expertise of readers.

Conclusions

Tools for semi-quantitative evaluation are recommended to assist the nuclear medicine physician in reporting brain FDG-PET pattern in neurodegenerative conditions. However, heterogeneity, complexity, and drawbacks of these tools should be known by users to avoid misinterpretation. Head-to-head comparisons and an effort to harmonize procedures are encouraged.

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References

  1. Nobili F, Arbizu J, Bouwman F, Drzezga A, Filippi M, Nestor P, et al. EAN-EANM recommendations for the use of brain 18F-Fluorodeoxyglucose positron emission tomography (FDG-PET) in neurodegenerative cognitive impairment and dementia: Delphi consensus. Eur J Neurol. (EJoN-18-0310; Submitted, March 13, 2018).

  2. Varrone A, Asenbaum S, Vander Borght T, Booij J, Nobili F, Någren K, et al. EANM procedure guidelines for PET brain imaging using [18F]FDG, version 2. Eur J Nucl Med Mol Imaging. 2009;36:2103–10.

    Article  PubMed  Google Scholar 

  3. Friston K, Holmes A, Worsley K, Poline J, Frith C, Frackowiak R. Statistical parametric maps in functional imaging: a general linear approach. Hum Brain Mapp. 1994;2:189–210.

    Article  Google Scholar 

  4. Signorini M, Paulesu E, Friston K, Perani D, Colleluori A, Lucignani G, et al. Rapid assessment of regional cerebral metabolic abnormalities in single subjects with quantitative and nonquantitative [18F]FDG PET: a clinical validation of statistical parametric mapping. NeuroImage. 1999;9:63–80.

    Article  CAS  PubMed  Google Scholar 

  5. Perani D, Della Rosa PA, Cerami C, Gallivanone F, Fallanca F, Vanoli EG, et al. Validation of an optimized SPM procedure for FDG-PET in dementia diagnosis in a clinical setting. NeuroImage Clin. 2014;6:445–54.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Della Rosa PA, Cerami C, Gallivanone F, Prestia A, Caroli A, Castiglioni I, et al. A standardized [18F]-FDG-PET template for spatial normalization in statistical parametric mapping of dementia. Neuroinformatics. 2014;12:575–93.

    Article  PubMed  Google Scholar 

  7. Minoshima S, Frey KA, Koeppe RA, Foster NL, Kuhl DE. A diagnostic approach in Alzheimer’s disease using three-dimensional stereotactic surface projections of fluorine-18-FDG PET. J Nucl Med. 1995;36:1238–48.

    CAS  PubMed  Google Scholar 

  8. Herholz K, Salmon E, Perani D, Baron JC, Holthoff V, Frölich L, et al. Discrimination between Alzheimer dementia and controls by automated analysis of multicenter FDG PET. NeuroImage. 2002;17:302–16.

    Article  CAS  PubMed  Google Scholar 

  9. Caroli A, Prestia A, Chen K, Ayutyanont N, Landau SM, Madison CM, et al. Summary metrics to assess Alzheimer disease-related hypometabolic pattern with 18F-FDG PET: head-to-head comparison. J Nucl Med. 2012;53:592–600.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Chen K, Ayutyanont N, Langbaum JBS, Fleisher AS, Reschke C, Lee W, et al. Characterizing Alzheimer’s disease using a hypometabolic convergence index. NeuroImage. 2011;56:52–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Landau SM, Harvey D, Madison CM, Koeppe RA, Reiman EM, Foster NL, et al. Associations between cognitive, functional, and FDG-PET measures of decline in AD and MCI. Neurobiol Aging. 2011;32:1207–18.

    Article  PubMed  Google Scholar 

  12. Pagani M, De Carli F, Morbelli S, Öberg J, Chincarini A, Frisoni GB, et al. Volume of interest-based [18F]fluorodeoxyglucose PET discriminates MCI converting to Alzheimer’s disease from healthy controls. A European Alzheimer’s disease consortium (EADC) study. NeuroImage Clin. 2015;7:34–42.

    Article  CAS  PubMed  Google Scholar 

  13. Boccardi M, Festari C, Altomare D, Gandolfo F, Orini S, Nobili F, et al. Assessing accuracy diagnostic FDG-PET studies to define clinical use for dementia diagnosis. EJNMMI.

  14. Leone MA, Brainin M, Boon P, Pugliatti M, Keindl M, Bassetti CL. Guidance for the preparation of neurological management guidelines by EFNS scientific task forces—revised recommendations 2012. Eur J Neurol. 2013;20:410–9.

    Article  CAS  PubMed  Google Scholar 

  15. Patterson JC, Lilien DL, Takalkar A, Kelley RE, Minagar A. Potential value of quantitative analysis of cerebral PET in early cognitive decline. Am J Alzheimers Dis Other Demen. 2009;23:586–92.

    Article  Google Scholar 

  16. Von Borczyskowski D, Wilke F, Martin B, Brenner W, Clausen M, Mester J, et al. Evaluation of a new expert system for fully automated detection of the Alzheimer’s dementia pattern in FDG PET. J Nucl Med. 2006;27:739–43.

    Google Scholar 

  17. Lehman VT, Carter RE, Claassen DO, Murphy RC, Lowe V, Petersen RC, et al. Visual assessment versus quantitative three-dimensional stereotactic surface projection fluorodeoxyglucose positron emission tomography for detection of mild cognitive impairment and Alzheimer disease. Clin Nucl Med. 2012;37:721–6.

    Article  PubMed  Google Scholar 

  18. Burdette JH, Minoshima S, Vander Borght T, Tran DD, Kuhl DE. Alzheimer disease: improved visual interpretation of PET images by using three-dimensional stereotaxic surface projections. Radiology. 1996;198:837–43.

    Article  CAS  PubMed  Google Scholar 

  19. Ishii K, Kono AK, Sasaki H, Miyamoto N, Fukuda T, Sakamoto S, et al. Fully automatic diagnostic system for early- and late-onset mild Alzheimer’s disease using FDG PET and 3D-SSP. Eur J Nucl Med Mol Imaging. 2006;33:575–83.

    Article  PubMed  Google Scholar 

  20. Kono AK, Ishii K, Sofue K, Miyamoto N, Sakamoto S, Mori E. Fully automatic differential diagnosis system for dementia with Lewy bodies and Alzheimer’s disease using FDG-PET and 3D-SSP. Eur J Nucl Med Mol Imaging. 2007;34:1490–7.

    Article  PubMed  Google Scholar 

  21. Morbelli S, Brugnolo A, Bossert I, Buschiazzo A, Frisoni GB, Galluzzi S, et al. Visual versus semi-quantitative analysis of 18F-FDG-PET in amnestic MCI: an European Alzheimer’s Disease Consortium (EADC) project. J Alzheimers Dis. 2015;44:815–26.

    Article  CAS  PubMed  Google Scholar 

  22. Ng S, Villemagne VL, Berlangieri S, Lee S-T, Cherk M, Gong SJ, et al. Visual assessment versus quantitative assessment of 11C-PIB PET and 18F-FDG PET for detection of Alzheimer’s disease. J Nucl Med. 2007;48:547–52.

    Article  CAS  PubMed  Google Scholar 

  23. Rabinovici GD, Rosen HJ, Alkalay A, Kornak J, Furst AJ, Agarwal N, et al. Amyloid vs. FDG-PET in the differential diagnosis of AD and FTLD. Neurology. 2011;77:2034–42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Foster NL, Heidebrink JL, Clark CM, Jagust WJ, Arnold SE, Barbas NR, et al. FDG-PET improves accuracy in distinguishing frontotemporal dementia and Alzheimer’s disease. Brain. 2007;130:2616–35.

    Article  PubMed  Google Scholar 

  25. Frisoni GB, Bocchetta M, Chételat G, Rabinovici GD, De Leon MJ, Kaye J, et al. Imaging markers for Alzheimer disease: which vs. how. Neurology. 2013. p. 487–500.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Yakushev I, Hammers A, Fellgiebel A, Schmidtmann I, Scheurich A, Buchholz HG, et al. SPM-based count normalization provides excellent discrimination of mild Alzheimer’s disease and amnestic mild cognitive impairment from healthy aging. NeuroImage. 2009;44:43–50.

    Article  PubMed  Google Scholar 

  27. Moher D, Liberati A, Tetzlaff J. ADPG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. J Clin Epidemiol. 2009;62:1006–12.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The procedure for assessing scientific evidence and defining consensual recommendations was funded by the European Association of Nuclear Medicine (EANM) and by the European Academy of Neurology (EAN). We thank the Guidelines working group of EAN, particularly Simona Arcuti and Maurizio Leone, for methodological advice.

Author information

Authors and Affiliations

  1. Department of Neuroscience (DINOGMI), University of Genoa and Polyclinic San Martino Hospital, Genoa, Italy

    Flavio Nobili

  2. LANE – Laboratory of Alzheimer’s Neuroimaging & Epidemiology, IRCCS S. Giovanni di Dio, Fatebenefratelli, Brescia, Italy

    Cristina Festari, Daniele Altomare & Marina Boccardi

  3. Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy

    Cristina Festari & Daniele Altomare

  4. Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy

    Federica Agosta

  5. Alzheimer Operative Unit, IRCCS S. Giovanni di Dio, Fatebenefratelli, Brescia, Italy

    Stefania Orini

  6. Division of Nuclear Medicine, University Hospitals Leuven, Leuven, Belgium

    Koen Van Laere

  7. Department of Imaging and Pathology, KU Leuven, Leuven, Belgium

    Koen Van Laere

  8. Department of Nuclear Medicine, Clinica Universidad de Navarra, University of Navarra, Pamplona, Spain

    Javier Arbizu

  9. Department of Neurology & Alzheimer Center, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, The Netherlands

    Femke Bouwman

  10. Department of Nuclear Medicine, University Hospital of Cologne, University of Cologne and German Center for Neurodegenerative Diseases (DZNE), Cologne, Germany

    Alexander Drzezga

  11. German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany

    Peter Nestor

  12. Queensland Brain Institute, University of Queensland and Mater Hospital, Brisbane, Australia

    Peter Nestor

  13. Division of Psychiatry & Essex Partnership University, University College London, London, UK

    Zuzana Walker

  14. LANVIE (Laboratoire de Neuroimagerie du Vieillissement), Department of Psychiatry, University of Geneva, Chemin du Petit-Bel-Air, 2, 1225, Chene-Bourg, Geneva, Switzerland

    Marina Boccardi

Authors
  1. Flavio Nobili
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  2. Cristina Festari
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  7. Javier Arbizu
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  8. Femke Bouwman
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  9. Alexander Drzezga
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Consortia

For the EANM-EAN Task Force for the Prescription of FDG-PET for Dementing Neurodegenerative Disorders

Corresponding authors

Correspondence to Flavio Nobili or Marina Boccardi.

Ethics declarations

Conflict of interest

Flavio Nobili: received personal fees and non-financial support from GE Healthcare, non-financial support from Eli-Lilly and grants from Chiesi Farmaceutici.

Cristina Festari: declares that she has no conflict of interest.

Daniele Altomare: was the recipient of the grant allocated by the European Academy of Neurology (EAN) for data extraction and evidence assessment for the present project.

Federica Agosta: is Section Editor of NeuroImage: Clinical; has received speaker fees from Biogen Idec, Novartis, and Excellence in Medical Education; and receives or has received research supports from the Italian Ministry of Health, AriSLA (Fondazione Italiana di Ricerca per la SLA), and the European Research Council. She received personal fees from Elsevier INC.

Stefania Orini: declares that she has no conflict of interest.

Koen Van Laere: received grant support through KU Leuven from GE Healthcare, Merck, Janssen Pharmaceuticals, UCB, Novartis, Pfizer and Abide Therapeutics.

Javier Arbizu: received grants from Eli Lilly & Co, Piramal, and GE Healthcare.

Femke Bouwman: declares that she has no conflict of interest.

Peter Nestor: declares that he has no conflict of interest.

Alexander Drzezga: received grants and non-financial support from Eli-Lilly & Co, Siemens and GE Healthcare; he also received non-financial support from Piramal.

Zuzana Walker: received from GE Healthcare grants and tracers, personal fees for consultancy and speaker’s fee.

Marina Boccardi has received funds from the European Association of Nuclear Medicine (EANM) to perform the evidence assessment and the global coordination of the present project. Moreover, she has received research grants from Piramal and served as a paid member of advisory boards for Eli Lilly.

Ethical approval

This article does not contain any new studies with human participants or animals performed by any of the authors. The human studies discussed herein came exclusively from previously published research articles.

Informed consent

Not applicable, this is a review article. Informed consent statement is declared in each of the revised paper.

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Nobili, F., Festari, C., Altomare, D. et al. Automated assessment of FDG-PET for differential diagnosis in patients with neurodegenerative disorders. Eur J Nucl Med Mol Imaging 45, 1557–1566 (2018). https://doi.org/10.1007/s00259-018-4030-3

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  • DOI: https://doi.org/10.1007/s00259-018-4030-3

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