Evidence map›Paper›PMID 42297931›Full record

ArticleEuropean journal of nuclear medicine and molecular imaging2026

Baseline amyloid deposition phenotypes inform the spatial distribution of early amyloid clearance with lecanemab in Alzheimer's disease.

Xiaofeng Dou, Daoyan Hu, Jing Wang, Yan Zhong, Congcong Yu, Hongwei Zhan, Yanxing Chen, Rui Zhou, Xiaohui Zhang, Baorong Zhang and 5 more

Abstract read
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In one paragraph

Article in European journal of nuclear medicine and molecular imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

15 authors.

Xiaofeng Dou *Department of Nuclear Medicine and PET-CT Center, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Daoyan Hu *Department of Nuclear Medicine and PET-CT Center, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Jing Wang *Department of Nuclear Medicine and PET-CT Center, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Yan Zhong *Department of Nuclear Medicine and PET-CT Center, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Congcong YuDepartment of Nuclear Medicine and PET-CT Center, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Hongwei ZhanDepartment of Nuclear Medicine and PET-CT Center, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Yanxing ChenDepartment of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Rui ZhouDepartment of Nuclear Medicine and PET-CT Center, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Xiaohui ZhangDepartment of Nuclear Medicine and PET-CT Center, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Baorong ZhangDepartment of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Jiong ZhouDepartment of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Mei TianHuman Phenome Institute & Huashan Hospital, Fudan University, Shanghai, China. tianmei@fudan.edu.cn.
Hong ZhangDepartment of Nuclear Medicine and PET-CT Center, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China. hzhang21@zju.edu.cn.ORCID 0000-0002-4084-5150
Yaping YanDepartment of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China. yanyaping@zju.edu.cn.
Chentao JinDepartment of Nuclear Medicine and PET-CT Center, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China. chentao_jin@zju.edu.cn.

Funding

Key Technologies Research and Development Program 2021YFA1101700, 2022YFE0118000National Natural Science Foundation of China 82030049, 32027802National Natural Science Foundation of China 82394433, 82361148130
6 · The paper itself

Abstract

objectiveLecanemab effectively reduces brain amyloid-β (Aβ) burden in early Alzheimer's disease (AD). However, Aβ deposition is spatially heterogeneous.The aim of this study is to test whether baseline β-amyloid (Aβ) deposition phenotype influences lecanemab-related Aβ clearance on Aβ-PET.

methodsAn Aβ Subtype and Stage Inference (SuStaIn) model was trained to identify Aβ progression phenotypes using cross-sectional Aβ-PET SUVRs from seven brain regions in 303 individuals (249 Aβ+/tau + PET-confirmed Alzheimer's disease and 54 Aβ-/tau- controls). This trained model was then applied to 39 lecanemab-treated patients with paired baseline and 6‑month Aβ-PET. Global and regional Aβ burden was quantified in Centiloids (CL) and SUVR, respectively. Subtype differences in ΔCL and ΔSUVR were tested. By defining baseline topology and clearance topology using posterior-to-anterior dominance indices (Topo_occ-fron = SUVR of occipital lobe - SUVR of frontal lobe), topology-clearance coupling was assessed using correlation and regression analysis. Additionally, spatial topological correspondence was quantified by modeling ROI-level clearance as a function of ROI-level baseline SUVR within a mixed-effects model with a subject-level random intercept.

resultsSuStaIn identified two dominant phenotypes consistent with an occipital-onset pattern (subtype 1) and a precuneus-frontal-onset pattern (subtype 2). In the lecanemab cohort, CL decreased by a mean ΔCL of - 27.77 ± 24.51. ΔCL did not differ between subtype 1 (n = 16) and subtype 2 (n = 17), whereas baseline CL strongly predicted ΔCL (r = -0.68, P < 0.001). ROI-wise ΔSUVR differences were not significant after FDR correction, while the ΔSUVR was significantly correlated with baseline SUVR (r = -0.64, P < 0.0001). In subtype-assigned patients (n = 33), a posterior-dominant baseline topology predicted greater posterior clearance. The changed Topo_occ-fron is correlated with baseline values (r = 0.514, P < 0.01) and remained significant after adjusting for baseline CL (β=-0.273, P < 0.01). ROI-level clearance magnitude increased with baseline regional SUVR (mixed-effects β = 0.264, p < 0.001), quantifying high spatial correspondence between deposition and clearance.

conclusionWhile baseline Aβ phenotype did not influence the magnitude of early global amyloid removal, it predicted the spatial pattern of regional clearance. Quantitative topological correspondence between baseline deposition and early clearance provides in vivo evidence of target engagement and supports topology-informed PET monitoring beyond global CL.

Indexed as

Alzheimer DiseaseAmyloid beta-PeptidesAgedBrainFemaleHumansMalePhenotypePositron-Emission TomographyAmyloid beta-PeptidesAlzheimer’s diseaseAmyloid PETLecanemabSuStaInTopology

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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.