DOI: 10.1002/alz70856_106683

B I OMARK E R S

POSTER PRESENTATION

NEUROIMAGING

Creating amulti-centre Amyloid PET dataset for DLB patients:
Design andMethodology

Ariane Bollack1,2 Beatrice Orso3 Mahnaz Shekari4 Cecilia Boccalini5

Valentina Garibotto6 Giovanni B Frisoni7 Lisa Quenon8

Bernard J Hanseeuw9,10 Val J Lowe11 Lyduine E. Collij12,13

Christopher Buckley2 Alan J Thomas14 John T O’Brien15 Gill Farrar2

1UCL Centre forMedical Image Computing,

London, United Kingdom

2GEHealthCare, Chalfont St Giles,

Buckinghamshire, United Kingdom

3University of Genoa, Genoa, Genoa, Italy

4Barcelonaβeta Brain Research Center
(BBRC), PasqualMaragall Foundation,

Barcelona, Spain

5University of Geneva, Geneva, Switzerland,

Switzerland

6Faculty ofMedicine, University of Geneva,

Geneva, Geneva, Switzerland

7GenevaMemory Center, Geneva University

Hospitals and University of Geneva, Geneva,

Switzerland

8Institute of Neuroscience, UCLouvain,

Brussels, Belgium

9Gordon Center forMedical Imaging,

Massachusetts General Hospital, Harvard

Medical School, Boston, MA, USA

10Institute of Neuroscience, Université

Catholique de Louvain, Brussels, Belgium

11Mayo Clinic, Rochester, MN, USA

12AmsterdamUMC location VUmc,

Amsterdam, Amsterdam, Netherlands

13Clinical Memory Research Unit, Department

of Clinical SciencesMalmö, Faculty of

Medicine, Lund University, Lund, Sweden

14Newcastle University, Newcastle upon Tyne,

Newcastle, United Kingdom

15University of Cambridge, Cambridge,

Cambridgeshire, United Kingdom

Correspondence

Abstract

Background: Dementia with Lewy Bodies (DLB) is often misdiagnosed or conflated

with Alzheimer’s Disease (AD) due to overlapping clinical presentations and

neuropathological features. The presence of AD-like features correlates with

accelerated cognitive decline and a worse overall prognosis. Compared to AD, studies

suggest that amyloid uptake in DLB may spare the occipital lobe. This project aims to

investigate whether amyloid PET could be leveraged to differentiate between AD and

DLB patients, with the goal of improving diagnostic accuracy and supporting patient

stratification and safety in anti-amyloid trials.

Method: This study involves collaboration across six centres (University of Geneva,

BioFINDER, AMPLE, AMYPAD PNHS, MCSA, GEHC), each contributing amyloid PET

imaging data from patients diagnosed with DLB based on clinical or neuropathological

assessments. Moreover, each center provided an additional subset of AD and healthy

controls, then matched to DLB samples based on age/sex/APOE genotype/education

for comparison. A sub analysis will be performed on patients with evidence of both

DLB and AD markers. Given the relatively small sample size, key aspect of the

methodological approach is to reduce technical sources of variability. The processing

workflow includes rigorous quality control, formatting, normalisation to MNI space,

harmonisation (differential smoothing), andMRI-based segmentation.

Result: The dataset currently includes amyloid PET scans (acquired using [11C]PiB,

[18F]Flutemetamol or [18F]Florbetapir) and MRI scans from ∼130 DLB patients,

along with ∼150 AD patients and ∼150 controls. In addition, 99 patients positive

for the CSF α-Synuclein Seeding Amplification Assay were included. Patient data

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided

the original work is properly cited.

© 2025 The Alzheimer’s Association. Alzheimer’s & Dementia published byWiley Periodicals LLC on behalf of Alzheimer’s Association.

Alzheimer’s Dement. 2025;21(Suppl. 2):e106683. wileyonlinelibrary.com/journal/alz 1 of 2

https://doi.org/10.1002/alz70856_106683

http://creativecommons.org/licenses/by/4.0/
https://wileyonlinelibrary.com/journal/alz
https://doi.org/10.1002/alz70856_106683


2 of 2 BIOMARKERS

Ariane Bollack, UCL Centre forMedical Image

Computing, London, United Kingdom.

Email: ariane.bollack@gehealthcare.com
were anonymized, and a harmonised database was created, including demographic

characteristics, clinical history, and results from neuropsychological assessments.

Conclusion: The creation of a standardized, multi-centre amyloid PET dataset for

DLB will enable the study of amyloid PET patterns at the global, regional and voxel

level, in comparison with uptake in AD patients and controls. This dataset will

serve as a valuable resource for investigating the neuropathological underpinnings

of DLB, improving diagnostic accuracy, and potentially guiding patient stratification

in anti-amyloid trials. Efforts to integrate [18F]Florbetaben and additional data are

ongoing.

mailto:ariane.bollack@gehealthcare.com

	Abstract