The DOI matches its Crossref record; 219 of the 220 references it cites were found in a registry or an index.
Quantification of amyloid PET for future clinical use: a state-of-the-art review
Open accessAbstract
Amyloid-β (Aβ) pathology is one of the earliest detectable brain changes in Alzheimer’s disease (AD) pathogenesis. The overall load and spatial distribution of brain Aβ can be determined in vivo using positron emission tomography (PET), for which three fluorine-18 labelled radiotracers have been approved for clinical use. In clinical practice, trained readers will categorise scans as either Aβ positive or negative, based on visual inspection. Diagnostic decisions are often based on these reads and patient selection for clinical trials is increasingly guided by amyloid status. However, tracer deposition in the grey matter as a function of amyloid load is an inherently continuous process, which is not sufficiently appreciated through binary cut-offs alone. State-of-the-art methods for amyloid PET quantification can generate tracer-independent measures of Aβ burden. Recent research has shown the ability of these quantitative measures to highlight pathological changes at the earliest stages of the AD continuum and generate more sensitive thresholds, as well as improving diagnostic confidence around established binary cut-offs. With the recent FDA approval of aducanumab and more candidate drugs on the horizon, early identification of amyloid burden using quantitative measures is critical for enrolling appropriate subjects to help establish the optimal window for therapeutic intervention and secondary prevention. In addition, quantitative amyloid measurements are used for treatment response monitoring in clinical trials. In clinical settings, large multi-centre studies have shown that amyloid PET results change both diagnosis and patient management and that quantification can accurately predict rates of cognitive decline. Whether these changes in management reflect an improvement in clinical outcomes is yet to be determined and further validation work is required to establish the utility of quantification for supporting treatment endpoint decisions. In this state-of-the-art review, several tools and measures available for amyloid PET quantification are summarised and discussed. Use of these methods is growing both clinically and in the research domain. Concurrently, there is a duty of care to the wider dementia community to increase visibility and understanding of these methods.
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“Moreover, the range from 12 Centiloids to 30 Centiloids has been proposed to reflect the ‘gray zone’ of Aβ deposition24.”
From Plasma p-tau231 and p-tau217 as state markers of amyloid-β pathology in preclinical Alzheimer’s disease (2022) · cites this paper as [24]
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Items 201–219 of 219
Verfaillie, S. C. J., Golla, S. S. V., Timmers, T., Tuncel, H., van der Weijden, C. W. J., Schober, P. R., Schuit, R. C., van der Flier, W. M., Windhorst, A. D., Lammertsma, A. A., et al. (2020) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x20915403 · Cited by 11
Curry, S., Patel, N., Fakhry-Darian, D., Khan, S. R., Perry, R. J., Malhotra, P., Nijran, K. S., & Win, Z. (2019) · British Journal of Radiology · doi:10.1259/bjr.20181025 · Cited by 10
Centiloid scale analysis for 18F-THK5351 PET imaging in Alzheimer’s disease
Yamao, T., Miwa, K., Wagatsuma, K., Shigemoto, Y., Sato, N., Akamatsu, G., Ito, H., & Matsuda, H. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.02.017 · Cited by 10
Centiloid method evaluation for amyloid PET of subcortical vascular dementia
Yun, H. J., Moon, S. H., Kim, H. J., Lockhart, S. N., Choe, Y. S., Lee, K. H., Na, D. L., Lee, J. M., & Seo, S. W. (2017) · Scientific Reports · doi:10.1038/s41598-017-16236-1 · Cited by 9
Chételat, G., & Murray, M. E. (2017) · Neurology · doi:10.1212/wnl.0000000000004678 · Cited by 7
AMYLOID AND TAU BIOMARKERS IN CSF
Blennow, K., & Zetterberg, H. (2015) · The Journal of Prevention of Alzheimer s Disease · doi:10.14283/jpad.2015.41 · Cited by 7
Converging evidence for a “gray‐zone” of amyloid burden and its relevance
Bullich, S., Salvadó, G., Alves, I. L., Marquié, M., Stephens, A. W., Gispert, J. D., Molinuevo, J. L., Buckley, C. J., Boada, M., & Barkhof, F. (2020) · Alzheimer s & Dementia · doi:10.1002/alz.044786 · Cited by 7
Self-citationTeipel, S. J., Dyrba, M., Vergallo, A., Lista, S., Habert, M. O., Potier, M., Lamari, F., Dubois, B., Hampel, H. J., & Grothe, M. J. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.748198 · Cited by 7
Evaluating robustness of the Centiloid scale against variations in amyloid PET image resolution
Shekari, M., Salvadó, G., Battle, M. R., Collij, L. E., Heeman, F., Alves, I. L., Palombit, A., Buckley, C. J., Farrar, G., Bullich, S., et al. (2021) · Alzheimer s & Dementia · doi:10.1002/alz.055726 · Cited by 7
Self-citationHattori, N., Sherwin, P. F., & Farrar, G. (2020) · Journal of Alzheimer s Disease Reports · doi:10.3233/adr-190150 · Cited by 6
Self-citationIDEAS and New IDEAS: Amyloid PET in diverse populations
Windon, C., Dilworth‐Anderson, P., Carrillo, M. C., Apgar, C., Gatsonis, C. A., Gareen, I. F., Gutman, R., Hanna, L., Hill, C. V., Hillner, B. E., et al. (2021) · Alzheimer s & Dementia · doi:10.1002/alz.051946 · Cited by 6
Overview of dominantly inherited AD and top‐line DIAN‐TU results of solanezumab and gantenerumab
Bateman, R. J., Aschenbrenner, A. J., Benzinger, T. L. S., Clifford, D. B., Coalier, K. A., Cruchaga, C., Fagan, A. M., Farlow, M. R., Goate, A., Gordon, B. A., et al. (2020) · Alzheimer s & Dementia · doi:10.1002/alz.041129 · Cited by 5
IC‐P‐034: COMPARISON OF [18F] FLUTEMETAMOL AND [11C] PIB PET IMAGES
Ataka, S., Takeda, A., Mino, T., Yamakawa, Y., Yamamoto, K., Tsutada, T., Kawabe, J., Wada, Y., Shiomi, S., Watanabe, Y., et al. (2014) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2014.05.038 · Cited by 4
Shekari, M., Niñerola‐Baizán, A., Salvadó, G., Battle, M. R., Buckley, C. J., Farrar, G., Operto, G., Falcón, C., Arenaza‐Urquijo, E. M., Perissinotti, A., et al. (2020) · Alzheimer s & Dementia · doi:10.1002/alz.045294 · Cited by 4
Self-citationPatel, N., Fakhry-Darian, D., Nijran, K. S., Khan, S. R., & Win, Z. (2018) · Journal of Nuclear Medicine · Cited by 4
Leuzy, A., Heurling, K., Santi, S. D., Bullich, S., Hansson, O., & Lilja, J. (2020) · PubMed Central · Cited by 3
Self-citationAmyloid PET in the diagnostic workup of neurodegenerative disease
Alongi, P., Chiaravalloti, A., Berti, V., Vellani, C., Trifirò, G., Puccini, G., Carli, G., Chincarini, A., Morbelli, S. D., Perani, D., et al. (2021) · Clinical and Translational Imaging · doi:10.1007/s40336-021-00428-x · Cited by 3
Franceschi, A. M., Petrover, D. R., Giliberto, L., Clouston, S., & Gordon, M. L. (2022) · World Journal of Nuclear Medicine · doi:10.1055/s-0042-1757290 · Cited by 3
Buckley, C. J., Foley, C., Battle, M. R., Grecchi, E., Farrar, G., Gispert, J. D., Alves, I. L., Barkhof, F., Lammertsma, A. A., Bullich, S., et al. (2019) · Cited by 2
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Milà-Alomà, M., Ashton, N. J., Shekari, M., Salvadó, G., Ortiz-Romero, P., Montoliu-Gaya, L., Benedet, A. L., Karikari, T. K., Lantero-Rodriguez, J., Vanmechelen, E., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01925-w · Cited by 353
Self-citationBiomarker-based staging of Alzheimer disease: rationale and clinical applications
Therriault, J., Schindler, S. E., Salvadó, G., Pascoal, T. A., Benedet, A. L., Ashton, N. J., Karikari, T. K., Apostolova, L. G., Murray, M. E., Verberk, I. M. W., et al. (2024) · Nature Reviews Neurology · doi:10.1038/s41582-024-00942-2 · Cited by 225
Self-citationAlzheimer's disease drug development pipeline: 2025
Cummings, J. L., Zhou, Y., Lee, G., Zhong, K., Fonseca, J., Leisgang‐Osse, A. M., & Cheng, F. (2025) · Alzheimer's & Dementia: Translational Research & Clinical Interventions · doi:10.1002/trc2.70098 · Cited by 190
Alzheimer’s Disease: Exploring the Landscape of Cognitive Decline
Tenchov, R., Sasso, J. M., & Zhou, Q. A. (2024) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.4c00339 · Cited by 170
Rabinovici, G. D., Knopman, D. S., Arbizu, J., Benzinger, T. L. S., Donohoe, K. J., Hansson, O., Herscovitch, P., Kuo, P. H., Lingler, J. H., Minoshima, S., et al. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.14338 · Cited by 163
Metadata
- Authors
- Pemberton, H. G., Collij, L. E., Heeman, F., Bollack, A., Shekari, M., Salvadó, G., Alves, I. L., Garcia, D. V., Battle, M., Buckley, C., Stephens, A. W., Bullich, S., Garibotto, V., Barkhof, F., Gispert, J. D., Farrar, G., & on behalf of the AMYPAD consortium
- Affiliations
- UCL Queen Square Institute of Neurology (GB)
- Amsterdam University Medical Centers (NL)
- University College London (GB)
- Universitat Pompeu Fabra (ES)
- Lund University (SE)
- University of Geneva (CH)
- Corresponding author
- Pemberton, Hugh G.
- ORCID
- Venue
- European Journal of Nuclear Medicine and Molecular Imaging
- Published
- 2022-04-07
- Volume
- 49
- Issue
- 10
- Pages
- 3508-3528
- ISSN
- 1619-7070 (ISSN-L)
- 1619-7089
- PMC
- PMC9308604
- OpenAlex
- W4225623397
- Cited by
- 226
- References
- 220
- Citations by year
- 2026: 61 · 2025: 71 · 2024: 64 · 2023: 25 · 2022: 5
- FWCI
- 7.10
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- European Journal of Nuclear Medicine and Molecular Imaging (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion, other-oa)
- Archive ouverte UNIGE (University of Geneva) (publishedVersion, other-oa)
- Pure Amsterdam UMC (submittedVersion, cc-by)
- UCL Discovery (University College London) (submittedVersion)
- Lund University Publications (Lund University) (submittedVersion)
- Repositori digital de la UPF (Universitat Pompeu Fabra) (submittedVersion, cc-by)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- Innovative Medicines Initiative (115952)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Keywords
- amyloid PET quantification, amyloid-beta, Alzheimer's disease, fluorine-18 radiotracer, aducanumab, treatment response monitoring, cognitive decline prediction, patient selection for clinical trials, amyloid imaging
- MeSH terms
- Alzheimer Disease, Amyloid, Amyloidosis, Brain, Humans, Amyloid beta-Peptides, Positron-Emission Tomography, Cognitive Dysfunction
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