Automated assessment of FDG-PET for differential diagnosis in patients with neurodegenerative disorders
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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.
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.
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.
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.
KeywordsBrain FDG-PET Visual reading Semi-quantitative assessment Dementia Neurodegenerative diseases
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.
Compliance with ethical standards
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.
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.
Not applicable, this is a review article. Informed consent statement is declared in each of the revised paper.
- 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).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.CrossRefPubMedGoogle 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.CrossRefPubMedGoogle 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.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.CrossRefPubMedGoogle Scholar