Evidence map›Paper›PMID 38276892›Full record

ArticleAlzheimer's & dementia : the journal of the Alzheimer's Association2024

Harmonizing florbetapir and PiB PET measurements of cortical Aβ plaque burden using multiple regions-of-interest and machine learning techniques: An alternative to the Centiloid approach.

Kewei Chen, Valentina Ghisays, Ji Luo, Yinghua Chen, Wendy Lee, Teresa Wu, Eric M Reiman, Yi Su

Abstract read
In one paragraph

Article in Alzheimer's & dementia : the journal of the Alzheimer's Association, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
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

6 citing papers in PubMed.

  1. Article
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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

8 authors.

Kewei ChenBanner Alzheimer's Institute, Phoenix, Arizona, USA.ORCID 0000-0001-8497-3069
Valentina GhisaysBanner Alzheimer's Institute, Phoenix, Arizona, USA.ORCID 0000-0002-8434-9407
Ji LuoBanner Alzheimer's Institute, Phoenix, Arizona, USA.ORCID 0000-0001-5504-3129
Yinghua ChenBanner Alzheimer's Institute, Phoenix, Arizona, USA.ORCID 0000-0001-8810-0978
Wendy LeeBanner Alzheimer's Institute, Phoenix, Arizona, USA.
Teresa WuASU-Mayo Center for Innovative Imaging, Arizona State University, Tempe, Arizona, USA.ORCID 0000-0002-0529-7048
Eric M ReimanBanner Alzheimer's Institute, Phoenix, Arizona, USA.ORCID 0000-0002-0705-3696
Yi SuBanner Alzheimer's Institute, Phoenix, Arizona, USA.ORCID 0000-0002-1946-8063

Funding

Smartphone-Based "Burst" Cognitive AssessmentsP01AG003991 · NIA · WASHINGTON UNIVERSITY · PI Tammie Lee Smith Benzinger · 1985 to 2026
$69.5M
The natural history of AB accumulation in preclinical ADP01AG026276 · NIA · WASHINGTON UNIVERSITY · PI SCHINDLER, SUZANNE ELIZABETH · 2005 to 2025
$49.5M
Washington University Institute of Clinical and Translational SciencesUL1TR000448 · NCATS · WASHINGTON UNIVERSITY · PI EVANOFF, BRADLEY A · 2012 to 2016
$41.4M
Research Education ComponentP30AG066444 · NIA · WASHINGTON UNIVERSITY · PI DAVID M. HOLTZMAN · 2020 to 2026
$28.7M
APOE in the Predisposition to, Protection from and Prevention of Alzheimer's DiseaseR01AG069453 · NIA · BANNER HEALTH · PI ASHTON, NICHOLAS, LANGBAUM, JESSICA BROOKE · 2020 to 2025
$27.4M
Research Education ComponentP30AG072980 · NIA · BANNER HEALTH · PI HEATHER Allyson BIMONTE-NELSON · 2021 to 2026
$24.9M
THE XNAT IMAGING INFORMATICS PLATFORMR01EB009352 · NIBIB · WASHINGTON UNIVERSITY · PI Daniel Scott Marcus · 2009 to 2026
$9.3M
DRIVING PERFORMANCE IN PRECLINICAL ALZHEIMER'S DISEASER01AG043434 · NIA · WASHINGTON UNIVERSITY · PI ROE, CATHERINE M · 2012 to 2016
$2.4M
Tracer harmonization for amyloid and tau PET imaging using statistical and deep learning techniquesRF1AG073424 · NIA · BANNER HEALTH · PI SU, YI · 2022 to 2022
$2.3M
NCATS NIH HHS UL1 TR000448NIA NIH HHS P01 AG003991NIA NIH HHS P01 AG026276NIA NIH HHS P30 AG066444NIA NIH HHS P30 AG072980NIA NIH HHS R01 AG043434NIA NIH HHS R01 AG069453NIA NIH HHS RF1 AG073424NIBIB NIH HHS R01 EB009352
6 · The paper itself

Abstract

introductionMachine learning (ML) can optimize amyloid (Aβ) comparability among positron emission tomography (PET) radiotracers. Using multi-regional florbetapir (FBP) measures and ML, we report better Pittsburgh compound-B (PiB)/FBP harmonization of mean-cortical Aβ (mcAβ) than Centiloid.

methodsPiB-FBP pairs from 92 subjects in www.oasis-brains.org and 46 in www.gaain.org/centiloid-project were used as the training/testing sets. FreeSurfer-extracted FBP multi-regional Aβ and actual PiB mcAβ in the training set were used to train ML models generating synthetic PiB mcAβ. The correlation coefficient (R) between the synthetic/actual PiB mcAβ in the testing set was assessed.

resultsIn the testing set, the synthetic/actual PiB mcAβ correlation R = 0.985 (R DISCUSSION: ML improved mcAβ comparability. Additional studies are needed for the generalizability to other amyloid tracers, and to tau PET. Highlights Centiloid is a calibration of the amyloid scale, not harmonization. Centiloid unifies the amyloid scale without improving inter-tracer association (R

Indexed as

Alzheimer DiseasePositron-Emission TomographyAmyloidAmyloid beta-PeptidesAmyloidogenic ProteinsAniline CompoundsBrainEthylene GlycolsHumansPlaque, AmyloidAmyloidAmyloid beta-PeptidesAmyloidogenic ProteinsAniline CompoundsEthylene Glycolsflorbetapiramyloid PET harmonizationartificial neural networkCentiloidflorbetapirmachine learningPiB

Identifiers

PMID38276892
PMCPMC10984485

What OpenQuestion holds

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Registered trials

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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.