FDG PET in the differential diagnosis of degenerative parkinsonian disorders: usefulness of voxel-based analysis in clinical practice.
Degenerative Parkinsonian disorders
Differential diagnosis
Metabolism maps
Positron emission tomography
SPM
Voxel-based analysis
Journal
Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
ISSN: 1590-3478
Titre abrégé: Neurol Sci
Pays: Italy
ID NLM: 100959175
Informations de publication
Date de publication:
Sep 2022
Sep 2022
Historique:
received:
16
02
2022
accepted:
23
05
2022
pubmed:
14
6
2022
medline:
20
8
2022
entrez:
13
6
2022
Statut:
ppublish
Résumé
The early differential diagnosis among neurodegenerative parkinsonian disorders becomes essential to set up the correct clinical-therapeutic approach. The increased utilization of [ Eighty-three subjects with a clinically confirmed diagnosis of degenerative parkinsonian disorders who underwent FDG brain PET/CT were selected. A voxel-based analysis was set up using statistical parametric mapping (SPM) on MATLAB to produce maps of brain hypometabolism and relative hypermetabolism. Four nuclear physicians (two expert and two not expert), blinded to the patients' symptoms, other physicians' evaluations, and final clinical diagnosis, independently evaluated all data by visual assessment and by adopting metabolic maps. In not-expert evaluators, the support of both hypometabolism and hypermetabolism maps results in a significant increase in diagnostic accuracy as well as clinical confidence. In expert evaluators, the increase in accuracy and in diagnostic confidence is mainly supported by hypometabolism maps alone. In this study, we demonstrated the additional value of combining voxel-based analyses with qualitative assessment of brain PET images. Moreover, maps of relative hypermetabolism can also make their contribution in clinical practice, particularly for less experienced evaluators.
Sections du résumé
BACKGROUND
BACKGROUND
The early differential diagnosis among neurodegenerative parkinsonian disorders becomes essential to set up the correct clinical-therapeutic approach. The increased utilization of [
METHOD
METHODS
Eighty-three subjects with a clinically confirmed diagnosis of degenerative parkinsonian disorders who underwent FDG brain PET/CT were selected. A voxel-based analysis was set up using statistical parametric mapping (SPM) on MATLAB to produce maps of brain hypometabolism and relative hypermetabolism. Four nuclear physicians (two expert and two not expert), blinded to the patients' symptoms, other physicians' evaluations, and final clinical diagnosis, independently evaluated all data by visual assessment and by adopting metabolic maps.
RESULTS
RESULTS
In not-expert evaluators, the support of both hypometabolism and hypermetabolism maps results in a significant increase in diagnostic accuracy as well as clinical confidence. In expert evaluators, the increase in accuracy and in diagnostic confidence is mainly supported by hypometabolism maps alone.
CONCLUSIONS
CONCLUSIONS
In this study, we demonstrated the additional value of combining voxel-based analyses with qualitative assessment of brain PET images. Moreover, maps of relative hypermetabolism can also make their contribution in clinical practice, particularly for less experienced evaluators.
Identifiants
pubmed: 35697965
doi: 10.1007/s10072-022-06166-w
pii: 10.1007/s10072-022-06166-w
pmc: PMC9385817
doi:
Substances chimiques
Fluorodeoxyglucose F18
0Z5B2CJX4D
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
5333-5341Informations de copyright
© 2022. The Author(s).
Références
Caminiti SP, Alongi P, Majno L, Volontè MA, Cerami C, Gianolli L, Comi G, Perani D (2017) Evaluation of an optimized [
doi: 10.1111/ene.13269
pubmed: 28244178
Postuma RB, Berg D, Stern M, Poewe W, Olanow CW, Oertel W, Obeso J, Marek K, Litvan I, Lang AE, Halliday G, Goetz CG, Gasser T, Dubois B, Chan P, Bloem BR, Adler CH, Deuschl G (2015) MDS clinical diagnostic criteria for Parkinson’s disease. Mov Disord 30(12):1591–1601. https://doi.org/10.1002/mds.26424
doi: 10.1002/mds.26424
pubmed: 26474316
Reich SG, Savitt JM (2019) Parkinson’s disease. Med Clin North Am 103(2):337–350. https://doi.org/10.1016/j.mcna.2018.10.014
doi: 10.1016/j.mcna.2018.10.014
pubmed: 30704685
Berti V, Pupi A, Mosconi L (2011) PET/CT in diagnosis of movement disorders. Ann N Y Acad Sci 1228:93–108. https://doi.org/10.1111/j.1749-6632.2011.06025.x
doi: 10.1111/j.1749-6632.2011.06025.x
pubmed: 21718327
pmcid: 3692301
Teune LK, Renken RJ, Mudali D, De Jong BM, Dierckx RA, Roerdink JBTM, Leenders KL (2013) Validation of parkinsonian disease-related metabolic brain patterns. Mov Disord 28(4):547–551. https://doi.org/10.1002/mds.25361
doi: 10.1002/mds.25361
pubmed: 23483593
Walker Z, Gandolfo F, Orini S, Garibotto V, Agosta F, Arbizu J, Bouwman F, Drzezga A, Nestor P, Boccardi M, Altomare D, Festari C, Nobili F (2018) Clinical utility of FDG PET in Parkinson’s disease and atypical parkinsonism associated with dementia. Eur J Nucl Med Mol Imaging 45(9):1534–1545. https://doi.org/10.1007/s00259-018-4031-2
doi: 10.1007/s00259-018-4031-2
pubmed: 29779045
pmcid: 6061481
Eidelberg D, Takikawa S, Moeller JR, Dhawan V, Redington K, Chaly T, Robeson W, Dahl JR, Margouleff D, Fazzini E, Przedborski S, Fahn S (1993) Striatal hypometabolism distinguishes striatonigral degeneration from Parkinson’s disease. Ann Neurol 33(5):518–527. https://doi.org/10.1002/ana.410330517
doi: 10.1002/ana.410330517
pubmed: 8498828
Eckert T, Barnes A, Dhawan V, Frucht S, Gordon MF, Feigin AS, Eidelberg D (2005) FDG PET in the differential diagnosis of parkinsonian disorders. Neuroimage 26(3):912–921. https://doi.org/10.1016/j.neuroimage.2005.03.012
doi: 10.1016/j.neuroimage.2005.03.012
pubmed: 15955501
Borghammer P, Chakravarty M, Jonsdottir KY, Sato N, Matsuda H, Ito K, Arahata Y, Kato T, Gjedde A (2010) Cortical hypometabolism and hypoperfusion in Parkinson’s disease is extensive: probably even at early disease stages. Brain Struct Funct 214(4):303–17. https://doi.org/10.1007/s00429-010-0246-0
doi: 10.1007/s00429-010-0246-0
pubmed: 20361208
Berti V, Pupi A, Mosconi L (2011) PET-CT in diagnosis of dementia.pdf. Ann N Y Acad Sci 1228:81–92. https://doi.org/10.1111/j.1749-6632.2011.06015.x.PET/CT
doi: 10.1111/j.1749-6632.2011.06015.x.PET/CT
pubmed: 21718326
pmcid: 3692287
Yong SW, Yoon JK, An YS, Lee PH (2007) A comparison of cerebral glucose metabolism in Parkinson’s disease, Parkinson’s disease dementia and dementia with Lewy bodies. Eur J Neurol 14(12):1357–1362. https://doi.org/10.1111/j.1468-1331.2007.01977.x
doi: 10.1111/j.1468-1331.2007.01977.x
pubmed: 17941855
Berti V, Pupi A, Mosconi L (2011) PET / CT in diagnosis of movement disorders Edited by Foxit Reader. Ann N Y Acad Sci 1228(C):93–108. https://doi.org/10.1111/j.1749-6632.2011.06025.x.PET/CT
doi: 10.1111/j.1749-6632.2011.06025.x.PET/CT
pubmed: 21718327
pmcid: 3692301
Pardini M, Huey ED, Spina S, Kreisl WC, Morbelli S, Wassermann EM, Nobili F, Ghetti B, Grafman J (2019) FDG-PET patterns associated with underlying pathology in corticobasal syndrome. Neurology 92(10):e1121–e1135. https://doi.org/10.1212/WNL.0000000000007038
doi: 10.1212/WNL.0000000000007038
pubmed: 30700592
pmcid: 6442013
Klaffke S, Kuhn AA, Plotkin M, Amthauer H, Harnack D, Felix R, Kupsch A (2006) Dopamine transporters, D2 receptors, and glucose metabolism in corticobasal degeneration. Mov Disord 21(10):1724–7. https://doi.org/10.1002/mds.21004
doi: 10.1002/mds.21004
pubmed: 16773621
Juh R, Pae CU, Kim TS, Lee CU, Choe B, Suh T (2005) Cerebral glucose metabolism in corticobasal degeneration comparison with progressive supranuclear palsy using statistical mapping analysis. Neurosci Lett 383(1–2):22–7. https://doi.org/10.1016/j.neulet.2005.03.057
doi: 10.1016/j.neulet.2005.03.057
pubmed: 15936506
McKeith IG, Dickson DW, Lowe J et al (2005) Diagnosis and management of dementia with Lewy bodies: third report of the DLB Consortium. Neurology 65(1863–1872):16
Gilman S, Wenning GK, Low PA et al (2008) Second consensus statement on the diagnosis of multiple system atrophy. Neurology 71(670–676):17
Armstrong MJ, Litvan I, Lang AE et al (2013) Criteria for the diagnosis of corticobasal degeneration. Neurology 80:496–503
doi: 10.1212/WNL.0b013e31827f0fd1
Tripathi M, Dhawan V, Peng S et al (2013) Differential diagnosis of parkinsonian syndromes using F-18 fluorodeoxyglucose positron emission tomography. Neuroradiology 55:483–492
doi: 10.1007/s00234-012-1132-7
Tang CC, Poston KL, Eckert T, Feigin A, Frucht S, Gudesblatt M, Dhawan V, Lesser M, Vonsattel JP, Fahn S, Eidelberg D (2010) Differential diagnosis of parkinsonism: a metabolic imaging study using pattern analysis. Lancet Neurol 9(2):149–158. https://doi.org/10.1016/S1474-4422(10)70002-8
doi: 10.1016/S1474-4422(10)70002-8
pubmed: 20061183
pmcid: 4617666
Guedj E et al (2022) EANM procedure guidelines for brain PET imaging using [18F]FDG, version 3. Eur J Nucl Med Mol Imaging. 49(2):632–651
doi: 10.1007/s00259-021-05603-w
Perani D, Della Rosa PA, Cerami C et al (2014) Validation of an optimized SPM procedure for FDG-PET in dementia diagnosis in a clinical setting. Neuroimage Clin 6:445–454
doi: 10.1016/j.nicl.2014.10.009
Meyer PT, Frings L, Rücker G, Hellwig S (2017)
doi: 10.2967/jnumed.116.186403
pubmed: 28912150
Lu CF, Soong BW, Wu HM, Teng S, Wang PS, Wu YT (2013) Disrupted cerebellar connectivity reduces whole-brain network efficiency in multiple system atrophy. Mov Disord 28(3):362–369. https://doi.org/10.1002/mds.25314
doi: 10.1002/mds.25314
pubmed: 23325625
Lee MJ, Kim TH, Mun CW, Shin HK, Son J, Lee JH (2018) Spatial correlation and segregation of multimodal MRI abnormalities in multiple system atrophy. J Neurol 265(7):1540–1547. https://doi.org/10.1007/s00415-018-8874-z
doi: 10.1007/s00415-018-8874-z
pubmed: 29696500
Armstrong MJ (2018) Progressive supranuclear palsy: an update. Curr Neurol Neurosci Rep 18(3):1–9. https://doi.org/10.1007/s11910-018-0819-5
doi: 10.1007/s11910-018-0819-5
Cerami C, Dodich A, Iannaccone S, Magnani G, Marcone A, Guglielmo P, Vanoli G, Cappa SF, Perani D (2020) Individual brain metabolic signatures in corticobasal syndrome. J Alzheimers Dis 76(2):517–528. https://doi.org/10.3233/JAD-200153
doi: 10.3233/JAD-200153
pubmed: 32538847
Watanabe H, Hara K, Ito M, Katsuno M, Sobue G (2018) New diagnostic criteria for Parkinson’s disease: MDS-PD criteria. Brain Nerve 70(2):139–146. https://doi.org/10.11477/mf.1416200966 (Japanese)
doi: 10.11477/mf.1416200966
pubmed: 29433115
Alexander SK, Rittman T, Xuereb JH, Bak TH, Hodges JR, Rowe JB (2014) Validation of the new consensus criteria for the diagnosis of corticobasal degeneration. J Neurol Neurosurg Psychiatry 85(8):925–9. https://doi.org/10.1136/jnnp-2013-307035
doi: 10.1136/jnnp-2013-307035
pubmed: 24521567
Gilman S, Wenning GK, Low PA, Brooks DJ, Mathias CJ, Trojanowski JQ, Wood NW, Colosimo C, Dürr A, Fowler CJ, Kaufmann H, Klockgether T, Lees A, Poewe W, Quinn N, Revesz T, Robertson D, Sandroni P, Seppi K, Vidailhet M (2008) Second consensus statement on the diagnosis of multiple system atrophy. Neurology 71(9):670–6. https://doi.org/10.1212/01.wnl.0000324625.00404.15
doi: 10.1212/01.wnl.0000324625.00404.15
pubmed: 18725592
pmcid: 2676993
Höglinger GU, Respondek G, Stamelou M, Kurz C, Josephs KA, Lang AE, Mollenhauer B, Müller U, Nilsson C, Whitwell JL, Arzberger T, Englund E, Gelpi E, Giese A, Irwin DJ, Meissner WG, Pantelyat A, Rajput A, van Swieten JC, Troakes C, Antonini A, Bhatia KP, Bordelon Y, Compta Y, Corvol JC, Colosimo C, Dickson DW, Dodel R, Ferguson L, Grossman M, Kassubek J, Krismer F, Levin J, Lorenzl S, Morris HR, Nestor P, Oertel WH, Poewe W, Rabinovici G, Rowe JB, Schellenberg GD, Seppi K, van Eimeren T, Wenning GK, Boxer AL, Golbe LI, Litvan I (2017) Movement Disorder Society-endorsed PSP Study Group Clinical diagnosis of progressive supranuclear palsy: the movement disorder society criteria. Mov Disord 32(6):853–864. https://doi.org/10.1002/mds.26987
doi: 10.1002/mds.26987
pubmed: 28467028
pmcid: 5516529
Della Rosa PA, Cerami C, Gallivanone F, Prestia A, Caroli A, Castiglioni I, Gilardi MC, Frisoni G, Friston K, Ashburner J, Perani D, EADC-PET Consortium (2014) A standardized [18F]-FDG-PET template for spatial normalization in statistical parametric mapping of dementia. Neuroinformatics 12(4):575–93. https://doi.org/10.1007/s12021-014-9235-4
doi: 10.1007/s12021-014-9235-4
pubmed: 24952892
Caminiti SP, Sala A, Presotto L, Chincarini A, Sestini S, Perani D; Alzheimer’s Disease Neuroimaging Initiative (ADNI), for the Associazione Italiana Medicina Nucleare (AIMN) datasets, The AIMN Neurology Study-Group collaborators:, Schillaci O, Berti V, Calcagni ML, Cistaro A, Morbelli S, Nobili F, Pappatà S, Volterrani D, Gobbo CL (2021) Validation of FDG-PET datasets of normal controls for the extraction of SPM-based brain metabolism maps. Eur J Nucl Med Mol Imaging. 48(8):2486-2499 https://doi.org/10.1007/s00259-020-05175-1