FDG-PET improves accuracy in distinguishing frontotemporal dementia and Alzheimer's disease
Abstract
Distinguishing Alzheimer's disease (AD) and frontotemporal dementia (FTD) currently relies on a clinical history and examination, but positron emission tomography with [(18)F] fluorodeoxyglucose (FDG-PET) shows different patterns of hypometabolism in these disorders that might aid differential diagnosis. Six dementia experts with variable FDG-PET experience made independent, forced choice, diagnostic decisions in 45 patients with pathologically confirmed AD (n = 31) or FTD (n = 14) using five separate methods: (1) review of clinical summaries, (2) a diagnostic checklist alone, (3) summary and checklist, (4) transaxial FDG-PET scans and (5) FDG-PET stereotactic surface projection (SSP) metabolic and statistical maps. In addition, we evaluated the effect of the sequential review of a clinical summary followed by SSP. Visual interpretation of SSP images was superior to clinical assessment and had the best inter-rater reliability (mean kappa = 0.78) and diagnostic accuracy (89.6%). It also had the highest specificity (97.6%) and sensitivity (86%), and positive likelihood ratio for FTD (36.5). The addition of FDG-PET to clinical summaries increased diagnostic accuracy and confidence for both AD and FTD. It was particularly helpful when raters were uncertain in their clinical diagnosis. Visual interpretation of FDG-PET after brief training is more reliable and accurate in distinguishing FTD from AD than clinical methods alone. FDG-PET adds important information that appropriately increases diagnostic confidence, even among experienced dementia specialists.
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FDG-PET has significant diagnostic accuracy for differential diagnosis between AD and FTD with high specificity (>95%) in multiple studies.
“FDG-PET has significant diagnostic accuracy for the differential diagnosis between AD and FTD with high specificity (>95%) in multiple studies [129–131].”
From The use of neuroimaging techniques in the early and differential diagnosis of dementia (2023) · cites this paper as [130]
FDG-PET imaging shows hypometabolism in Alzheimer's disease brains, mainly in the temporoparietal cortex including the precuneus and posterior cingulate cortex.
“[18F] fluorodeoxyglucose (FDG)-PET imaging showed hypometabolism in AD brains, mainly in the temporoparietal cortex including the precuneus and posterior cingulate cortex (Reiman et al., 1996; Foster et al., 2007).”
From APOE genotype-dependent differences in human astrocytic energy metabolism (2025) · cites this paper as Foster et al., 2007
Cites
The Measurement of Observer Agreement for Categorical Data
Landis, J. R., & Koch, G. G. (1977) · Biometrics · doi:10.2307/2529310 · Cited by 81,148 · see citing works
The Amyloid Hypothesis of Alzheimer’s Disease: Progress and Problems on the Road to Therapeutics
Hardy, J., & Selkoe, D. J. (2002) · Science · doi:10.1126/science.1072994 · Cited by 13,864 · see citing works
The Natural History of Alzheimer's Disease: Description of Study Cohort and Accuracy of Diagnosis
Becker, J. T. (1994) · Archives of Neurology · doi:10.1001/archneur.1994.00540180063015 · Cited by 576 · see citing works
Neuroimaging and Early Diagnosis of Alzheimer Disease: A Look to the Future
Petrella, J. R., Coleman, R. E., & Doraiswamy, P. M. (2003) · Radiology · doi:10.1148/radiol.2262011600 · Cited by 427 · see citing works
Minoshima, S., Frey, K. A., Foster, N. L., & Kuhl, D. E. (1995) · Journal of Computer Assisted Tomography · doi:10.1097/00004728-199507000-00006 · Cited by 252 · cited 2× · see citing works
“We normalized surface pixel values to the pons, which is relatively preserved in AD (Minoshima et al., 1995a).”
MethodSelf-citationa, S. (1999) · Journal of Psychiatric Research · doi:10.1016/s0022-3956(98)90048-6 · Cited by 148 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Dementia prevention, intervention, and care
Livingston, G., Sommerlad, A., Orgeta, V., Costafreda, S. G., Huntley, J., Ames, D., Ballard, C., Banerjee, S., Burns, A., Cohen-Mansfield, J., et al. (2017) · The Lancet · doi:10.1016/s0140-6736(17)31363-6 · Cited by 6,072 · see citing works
Advancing research diagnostic criteria for Alzheimer's disease: the IWG-2 criteria
Dubois, B., Feldman, H. H., Jacova, C., Hampel, H., Molinuevo, J. L., Blennow, K., DeKosky, S. T., Gauthier, S., Selkoe, D., Bateman, R., et al. (2014) · The Lancet Neurology · doi:10.1016/s1474-4422(14)70090-0 · Cited by 3,321 · see citing works
Zhou, J., Greicius, M. D., Gennatas, E. D., Growdon, M. E., Jang, J. Y., Rabinovici, G. D., Kramer, J. H., Weiner, M., Miller, B. L., & Seeley, W. W. (2010) · Brain · doi:10.1093/brain/awq075 · Cited by 1,016 · see citing works
Brain Imaging in Alzheimer Disease
Johnson, K. A., Fox, N. C., Sperling, R. A., & Klunk, W. E. (2012) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a006213 · Cited by 875 · see citing works
Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer's Disease
Liu, S., Liu, S., Cai, W., Che, H., Pujol, S., Kikinis, R., Feng, D., Fulham, M. J., & ADNI (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2372011 · Cited by 637 · see citing works
Johnson, K. A., Minoshima, S., Bohnen, N. I., Donohoe, K. J., Foster, N. L., Herscovitch, P., Karlawish, J. H., Rowe, C. C., Carrillo, M. C., Hartley, D. M., et al. (2013) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2013.01.002 · Cited by 603 · see citing works
Self-citationChételat, G., Arbizu, J., Barthel, H., Garibotto, V., Law, I., Morbelli, S., van de Giessen, E., Agosta, F., Barkhof, F., Brooks, D. J., et al. (2020) · The Lancet Neurology · doi:10.1016/s1474-4422(20)30314-8 · Cited by 526 · see citing works
Self-citationDucharme, S., Dols, A., Laforce, R., Devenney, E., Kumfor, F., van den Stock, J., Dallaire-Théroux, C., Seelaar, H., Gossink, F., Vijverberg, E., et al. (2020) · Brain · doi:10.1093/brain/awaa018 · Cited by 320 · see citing works
Cruchaga, C., Chakraverty, S., Mayo, K., Vallania, F. L. M., Mitra, R. D., Faber, K., Williamson, J., Bird, T., Diaz-Arrastia, R., Foroud, T. M., et al. (2012) · PLoS ONE · doi:10.1371/journal.pone.0031039 · Cited by 320 · see citing works
Shaw, L. M., Arias, J., Blennow, K., Galasko, D., Molinuevo, J. L., Salloway, S., Schindler, S., Carrillo, M. C., Hendrix, J. A., Ross, A., et al. (2018) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2018.07.220 · Cited by 266 · see citing works
Brain PET in Suspected Dementia: Patterns of Altered FDG Metabolism
Brown, R. K. J., Bohnen, N. I., Wong, K. K., Minoshima, S., & Frey, K. A. (2014) · RadioGraphics · doi:10.1148/rg.343135065 · Cited by 256 · see citing works
Self-citationAghourian, M., Legault-Denis, C., Soucy, J.-P., Rosa-Neto, P., Gauthier, S., Kostikov, A., Gravel, P., & Bédard, M.-A. (2017) · Molecular Psychiatry · doi:10.1038/mp.2017.183 · Cited by 220 · see citing works
Brain fluorodeoxyglucose (FDG) PET in dementia
Kato, T., Inui, Y., Nakamura, A., & Ito, K. (2016) · Ageing Research Reviews · doi:10.1016/j.arr.2016.02.003 · Cited by 236 · see citing works
Biomarkers for the Early Detection and Progression of Alzheimer's Disease
Counts, S. E., Ikonomovic, M. D., Mercado, N., Vega, I. E., & Mufson, E. J. (2017) · Neurotherapeutics · doi:10.1007/s13311-016-0481-z · Cited by 224 · see citing works
The use of neuroimaging techniques in the early and differential diagnosis of dementia
Chouliaras, L., & O’Brien, J. T. (2023) · Molecular Psychiatry · doi:10.1038/s41380-023-02215-8 · Cited by 186 · see citing works
SPECT and PET imaging in Alzheimer’s disease
Valotassiou, V., Malamitsi, J., Papatriantafyllou, J., Dardiotis, E., Tsougos, I., Psimadas, D., Alexiou, S., Hadjigeorgiou, G., & Georgoulias, P. (2018) · Annals of Nuclear Medicine · doi:10.1007/s12149-018-1292-6 · Cited by 169 · see citing works
AI-based differential diagnosis of dementia etiologies on multimodal data
Xue, C., Kowshik, S. S., Lteif, D., Puducheri, S., Jasodanand, V. H., Zhou, O. T., Walia, A. S., Guney, O. B., Zhang, J. D., Poésy, S., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-03118-z · Cited by 182 · see citing works
<scp>EFNS</scp> task force: the use of neuroimaging in the diagnosis of dementia
Filippi, M., Agosta, F., Barkhof, F., Dubois, B., Fox, N. C., Frisoni, G. B., Jack, C. R., Johannsen, P., Miller, B. L., Nestor, P. J., et al. (2012) · European Journal of Neurology · doi:10.1111/j.1468-1331.2012.03859.x · Cited by 141 · see citing works
Frontotemporal Lobar Degeneration: Epidemiology, Pathology, Diagnosis and Management
Seltman, R. E., & Matthews, B. R. (2012) · CNS Drugs · doi:10.2165/11640070-000000000-00000 · Cited by 132 · see citing works
Tartaglia, M. C., Rosen, H. J., & Miller, B. L. (2011) · Neurotherapeutics · doi:10.1007/s13311-010-0012-2 · Cited by 124 · see citing works
Antemortem differential diagnosis of dementia pathology using structural MRI: Differential-STAND
Vemuri, P., Simon, G., Kantarci, K., Whitwell, J. L., Senjem, M. L., Przybelski, S. A., Gunter, J. L., Josephs, K. A., Knopman, D. S., Boeve, B. F., et al. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.12.073 · Cited by 108 · see citing works
An Efficient Classifier for Alzheimer’s Disease Genes Identification
Xu, L., Liang, G., Liao, C., Chen, G.-D., & Chang, C.-C. (2018) · Molecules · doi:10.3390/molecules23123140 · Cited by 107 · see citing works
Ferreira, L. K., & Busatto, G. F. (2011) · Clinics · doi:10.1590/s1807-59322011001300003 · Cited by 102 · see citing works
Amyloid PET in clinical practice: Its place in the multidimensional space of Alzheimer's disease
Vandenberghe, R., Adamczuk, K., Dupont, P., Laere, K. V., & Chételat, G. (2013) · NeuroImage: Clinical · doi:10.1016/j.nicl.2013.03.014 · Cited by 99 · see citing works
“…to a comparative study of 11C-PIB and FDG-PET (Ng et al., 2007), but this may improve when statisticalmaps are used based on the contrast between an individual's scan and a normal database rather than visual reads of native images (Foster et al., 2007; Herholz et al., 2002; Mosconi et al., 2008).”
BackgroundVijverberg, E. G. B., Wattjes, M. P., Dols, A., Krudop, W. A., Möller, C., Peters, A., Kerssens, C. J., Gossink, F., Prins, N. D., Stek, M. L., et al. (2016) · Journal of Alzheimer’s Disease · doi:10.3233/jad-160285 · Cited by 92 · see citing works
Initiative, T. A. D. N., Zu, C., Jie, B., Liu, M., Chen, S., Shen, D., & Zhang, D. (2016) · Brain Imaging and Behavior · doi:10.1007/s11682-015-9480-7 · Cited by 95 · see citing works
Biomarkers in Neurodegenerative Diseases
Jeromin, A., & Bowser, R. (2017) · Advances in Neurobiology · doi:10.1007/978-3-319-57193-5_20 · Cited by 92 · see citing works
The dynamics of biomarkers across the clinical spectrum of Alzheimer's disease
Hadjichrysanthou, C., Evans, S., Bajaj, S., Siakallis, L. C., McRae-McKee, K., de Wolf, F., Anderson, R. M., & Initiative, T. A. D. N. (2020) · Alzheimer's Research & Therapy · doi:10.1186/s13195-020-00636-z · Cited by 86 · see citing works
“…considered 18 F-fluorodeoxyglucose (FDG) positron emission tomography (PET) which is used to visualise changes in the cerebral glucose metabolism [44], and standardised uptake value ratio (SUVr) for Pittsburgh compound B (PiB)-PET [45] and florbetapir (F18-AV-45) PET [46], which both allow in…”
Womack, K. B., Diaz-Arrastia, R., Aizenstein, H. J., Arnold, S. E., Barbas, N. R., Boeve, B. F., Clark, C. M., DeCarli, C. S., Jagust, W. J., Leverenz, J. B., et al. (2011) · Archives of Neurology · doi:10.1001/archneurol.2010.295 · Cited by 85 · see citing works
Self-citationPanegyres, P. K., Rogers, J. M., McCarthy, M., Campbell, A., & Wu, J. S. (2009) · BMC Neurology · doi:10.1186/1471-2377-9-41 · Cited by 81 · see citing works
Moguilner, S., García, A. M., Perl, Y. S., Tagliazucchi, E., Piguet, O., Kumfor, F., Reyes, P., Matallana, D., Sedeño, L., & Ibáñez, A. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2020.117522 · Cited by 63 · see citing works
Fink, H. A., Linskens, E. J., Silverman, P. C., McCarten, J. R., Hemmy, L. S., Ouellette, J. M., Greer, N. L., Wilt, T. J., & Butler, M. (2020) · Annals of Internal Medicine · doi:10.7326/m19-3888 · Cited by 57 · see citing works
FTD spectrum: Neuroimaging across the FTD spectrum
Whitwell, J. L. (2019) · Progress in Molecular Biology and Translational Science · doi:10.1016/bs.pmbts.2019.05.009 · Cited by 53 · see citing works
Yoo, I. D., Park, M. W., Cha, H. W., Yoon, S., Boonpraman, N., Yi, S. S., & Moon, J.-S. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21217862 · Cited by 51 · see citing works
Yamane, T., Ikari, Y., Nishio, T., Ishii, K., Ishii, K., Kato, T., Ito, K., Silverman, D. H. S., Senda, M., Asada, T., et al. (2014) · American Journal of Neuroradiology · doi:10.3174/ajnr.a3665 · Cited by 48 · see citing works
Diagnostic biomarkers in Alzheimer’s disease
Andersen, E., Casteigne, B., Chapman, W. D., Creed, A., Foster, F., Lapins, A., Shatz, R., & Sawyer, R. P. (2021) · Biomarkers in Neuropsychiatry · doi:10.1016/j.bionps.2021.100041 · Cited by 42 · see citing works
Biomarkers in frontotemporal lobar degenerations—Progress and challenges
Hu, W. T., Trojanowski, J. Q., & Shaw, L. M. (2011) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2011.04.012 · Cited by 42 · see citing works
Gossye, H., Van Broeckhoven, C., & Engelborghs, S. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00757 · Cited by 34 · see citing works
Clinical impact of a second FDG-PET in atypical/unclear dementia syndromes
Bergeron, D., Beauregard, J.-M., Guimond, J., Fortin, M.-P., Houde, M., Poulin, S., Verret, L., Bouchard, R. W., & Laforce, R. (2015) · Journal of Alzheimer's Disease · doi:10.3233/jad-150302 · Cited by 17 · see citing works
APOE genotype-dependent differences in human astrocytic energy metabolism
Budny, V., Bodenmann, C., Zürcher, K. J., Krüger, M., de Leeuw, S. M., Weber, R. Z., Rust, R., Ravotto, L., Ruminot, I., Barros, L. F., et al. (2025) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2025.1603657 · Cited by 6 · see citing works
- Authors
- Foster, N. L., Heidebrink, J. L., Clark, C. M., Jagust, W. J., Arnold, S. E., Barbas, N. R., DeCarli, C. S., Scott Turner, R., Koeppe, R. A., Higdon, R., & Minoshima, S.
- Affiliations
- University of Utah (US)
- University of Washington (US)
- ORCID
- Venue
- Brain
- Publisher
- Oxford University Press (OUP)
- Published
- 2007-10-01
- Volume
- 130
- Issue
- 10
- Pages
- 2616-2635
- ISSN
- 0006-8950 (ISSN-L)
- 1460-2156
- OpenAlex
- W2026553142
- Cited by
- 608
- References
- 6
- Citations by year
- 2026: 16 · 2025: 22 · 2024: 19 · 2023: 18 · 2022: 19 · 2021: 31 · 2020: 39 · 2019: 29 · 2018: 33 · 2017: 30 · 2016: 31 · 2015: 38 · 2014: 36 · 2013: 42 · 2012: 34
- FWCI
- 25.84
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Brain (publishedVersion)
- PubMed (publishedVersion)
- Funders
- University of Pennsylvania
- National Alzheimer's Coordinating Center
- National Institutes of Health (AG 10 124, AG10129)
- National Institute on Aging (P30AG010129, AG 08671, U01AG016976, P30AG010124, AG-10124, AG-10129, AG-16976, P50AG008671)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Keywords
- frontotemporal dementia, Alzheimer's disease, FDG-PET, differential diagnosis, brain hypometabolism, diagnostic accuracy, inter-rater reliability
- MeSH terms
- Adult, Aged, Alzheimer Disease, Brain, Dementia, Diagnosis, Differential, Female, Humans, Image Processing, Computer-Assisted, Male, Middle Aged, Sensitivity and Specificity, Observer Variation, Disease Progression, Radiopharmaceuticals, Fluorodeoxyglucose F18, Positron-Emission Tomography
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