Evidence map›Paper›PMID 35389071›Full record

ReviewEuropean journal of nuclear medicine and molecular imaging2022

Quantification of amyloid PET for future clinical use: a state-of-the-art review.

Hugh G Pemberton, Lyduine E Collij, Fiona Heeman, Ariane Bollack, Mahnaz Shekari, Gemma Salvadó, Isadora Lopes Alves, David Vallez Garcia, Mark Battle, Christopher Buckley and 7 more

Open access · hybridAbstract readReview
In one paragraph

Review in European journal of nuclear medicine and molecular imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 137 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
137citing papers in PubMed, 4 pooled it
25.5field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

137 citing papers in PubMed, 4 syntheses or guidelines pooled it, 196 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Article
  6. Review
  7. Article
  8. Article
  9. Investigating AD and non-AD [European journal of nuclear medicine and molecular imaging · 2026
    Article
  10. Article
  11. Article
  12. Beyond CT: Attenuation correction for stand-alone brain PET.Zeitschrift fur medizinische Physik · 2026
    Review
  13. Article
  14. Article
  15. Preclinical evaluation of [EJNMMI research · 2026
    Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Radiomics-enhancedEuropean radiology experimental · 2026
    Article

77 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors at 8 institutions in 5 countries.

Hugh G PembertonGE Healthcare, Amersham, UK. hugh.pemberton@ge.com.ORCID 0000-0001-8419-6423
Lyduine E CollijDepartment of Radiology and Nuclear Medicine, Amsterdam Neurocience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
Fiona HeemanDepartment of Radiology and Nuclear Medicine, Amsterdam Neurocience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.ORCID 0000-0001-7769-8329
Ariane BollackCentre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK.
Mahnaz ShekariBarcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.
Gemma SalvadóBarcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.ORCID 0000-0002-5210-9230
Isadora Lopes AlvesDepartment of Radiology and Nuclear Medicine, Amsterdam Neurocience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
David Vallez GarciaDepartment of Radiology and Nuclear Medicine, Amsterdam Neurocience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.ORCID 0000-0003-3308-3167
Mark BattleGE Healthcare, Amersham, UK.ORCID 0000-0002-6190-6389
Christopher BuckleyGE Healthcare, Amersham, UK.
Andrew W StephensLife Molecular Imaging GmbH, Berlin, Germany.ORCID 0000-0001-5052-6586
Santiago BullichLife Molecular Imaging GmbH, Berlin, Germany.ORCID 0000-0001-5669-269X
Valentina GaribottoDivision of Nuclear Medicine and Molecular Imaging, University Hospitals of Geneva, Geneva, Switzerland.
Frederik BarkhofCentre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK.ORCID 0000-0003-3543-3706
Juan Domingo GispertBarcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.
Gill FarrarGE Healthcare, Amersham, UK.
AMYPAD consortium
Amsterdam University Medical Centers · NLAmersham Hospital · GBLund University · SEUniversitat Pompeu Fabra · ESNational Hospital for Neurology and Neurosurgery · GBQueen Mary University of London · GBUniversity College London · GBUniversity of Geneva · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Alzheimer DiseaseAmyloidosisCognitive DysfunctionAmyloidAmyloid beta-PeptidesBrainHumansPositron-Emission TomographyAmyloidAmyloid beta-PeptidesAlzheimer’sAmyloidBrainCentiloidDementiaPETQuantificationSUVr

Identifiers

PMID35389071
PMCPMC9308604
OpenAlexW4225623397

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.