CoChat
Sign Up
Cited by 226Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatmentsMedical Imaging Techniques and Applications

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 access
Pemberton, Hugh G., Collij, Lyduine E., Heeman, Fiona, Bollack, Ariane, Shekari, Mahnaz, Salvadó, Gemma, Alves, Isadora Lopes, Garcia, David Vallez, Battle, Mark, Buckley, Christopher, Stephens, Andrew W., Bullich, Santiago, Garibotto, Valentina, Barkhof, Frederik, Gispert, Juan Domingo, Farrar, Gill, on behalf of the AMYPAD consortium·2022·European Journal of Nuclear Medicine and Molecular Imaging
DOI 10.1007/s00259-022-05784-yPMC PMC9308604
Open this paper in CoChat — free accountOpen PDF

Abstract

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1007/s00259-022-05784-y

Cites

219 of 219 references verified0 citation errors · 0 not checkable

Statistics

Figures

Fig. 1 Illustrative PET images derived from the five most commonly used amyloid tracers on different patients. The left column shows Aß negative subjects (all ~0 Centiloid) and right column shows Aß positive subjects (all ~50 Centiloid, for further details, see “Centiloid scaling” section). Colour schemes used for regulatory approved tracers are in line with each of their FDA label prescribing information: [18F]flutemetamol (https://www.accessdata.fda.gov/drugsatfda_docs/label/2016/203137s005lbl.pdf), [18F]florbetaben (https://www.accessdata.fda.gov/drugsatfda_docs/label/2014/204677s000lbl.pdf), [18F]florbetapir (https://www.accessdata.fda.gov/drugsatfda_docs/label/2012/202008s000lbl.pdf)
Fig. 1 Illustrative PET images derived from the five most commonly used amyloid tracers on different patients. The left column shows Aß negative subjects (all ~0 Centiloid) and right column shows Aß positive subjects (all ~50 Centiloid, for further details, see “Centiloid scaling” section). Colour schemes used for regulatory approved tracers are in line with each of their FDA label prescribing information: [18F]flutemetamol (https://www.accessdata.fda.gov/drugsatfda_docs/label/2016/203137s005lbl.pdf), [18F]florbetaben (https://www.accessdata.fda.gov/drugsatfda_docs/label/2014/204677s000lbl.pdf), [18F]florbetapir (https://www.accessdata.fda.gov/drugsatfda_docs/label/2012/202008s000lbl.pdf)CC BY · PubMed Central
Fig. 2 Example of the most common reference and target regions used when generating SUVr
Fig. 2 Example of the most common reference and target regions used when generating SUVrCC BY · PubMed Central
Fig. 3 Bar graph showing the increasing use of CLs in academic publications. The numbers were obtained through a PubMed search for “Centiloid” in all fields on 7th September 2021
Fig. 3 Bar graph showing the increasing use of CLs in academic publications. The numbers were obtained through a PubMed search for “Centiloid” in all fields on 7th September 2021CC BY · PubMed Central
Fig. 4 Summary of the various CL thresholds established in the literature and in use for clinical current clinical trial inclusion
Fig. 4 Summary of the various CL thresholds established in the literature and in use for clinical current clinical trial inclusionCC BY · PubMed Central
Fig. 5 Example of quantitative metrics computed on two subjects from the AIBL dataset scanned with [18F]flutemetamol. Low amyloid uptake (left image) and high amyloid uptake (right image), including demographics. It was not possible to compute AMYQ due to the proprietary nature of the software. Abbreviations: mini-mental state examination (MMSE), standardised uptake value ratio (SUVr); amyloid-β (Aβ)
Fig. 5 Example of quantitative metrics computed on two subjects from the AIBL dataset scanned with [18F]flutemetamol. Low amyloid uptake (left image) and high amyloid uptake (right image), including demographics. It was not possible to compute AMYQ due to the proprietary nature of the software. Abbreviations: mini-mental state examination (MMSE), standardised uptake value ratio (SUVr); amyloid-β (Aβ)CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 220 references

Cited by

Sorted by citations

Items 101–200 of 226

Related

See all

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
Publisher
Springer Science and Business Media LLC
Published
2022-04-07
Volume
49
Issue
10
Pages
3508-3528
ISSN
  • 1619-7070 (ISSN-L)
  • 1619-7089
DOI
10.1007/s00259-022-05784-y
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
Licence
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review