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Record W7117245699 · doi:10.1002/alz70856_100048

Lecanemab in the treatment of early Alzheimer's disease: An observational study based on 7.0T ultra‐high field MRI

2025· article· en· W7117245699 on OpenAlexaboutno aff
Haokun Liu, Weiwei Zhang, Keying Fang, Bin Jiao, Lu Shen

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldNeuroscience
TopicCerebrospinal fluid and hydrocephalus
Canadian institutionsnot available
Fundersnot available
KeywordsObservational studyField (mathematics)Magnetic resonance imagingMedical imagingClinical trial

Abstract

fetched live from OpenAlex

BACKGROUND: Previous research has demonstrated the effectiveness of lecanemab in treating early Alzheimer's disease (AD). However, its validation in the Hunan region of China remains limited. Traditional evaluation methods lack precision, particularly in brain imaging technologies. To address this gap, this study utilizes 7.0-tesla ultra-high-field magnetic resonance imaging (MRI) and its sensitive susceptibility-weighted imaging (SWI) sequence, which effectively detects microbleeds. Additionally, diffusion tensor imaging (DTI) analysis, in combination with the along the perivascular spaces (ALPS) index, is employed to evaluate glymphatic function. This study aims to assess the efficacy and safety of lecanemab in AD treatment, focusing on its impact on brain microstructure, glymphatic function, and amyloid-β (Aβ) clearance. METHOD: Beginning on June 28, 2024, 27 patients diagnosed with AD were enrolled in the study and commenced treatment with lecanemab. Comprehensive baseline data collection, clinical screening, and cognitive assessments were completed. Each patient underwent SWI and DTI imaging using a 7.0T Siemens Terra MRI scanner equipped with a 32-channel head coil and parallel transmission (pTX) system, both pre- and post-treatment. RESULT: Of the 27 participants (mean age 61.67±7.30 years; 9 male), two patients (7.4%; 1 male) experienced flu-like symptoms following their initial lecanemab injection. No new cortical or subcortical microhemorrhages were detected via 7.0T MRI SWI sequences during treatment. Additionally, there were no instances of ARIA-E or ARIA-H among participants. Cognitive function, evaluated through the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA), showed no significant decline during the observation period (p >0.05). Among the 27 patients, 9 (mean age 63.56±8.90 years; 4 male) completed observation for their seventh medication dose. Paired t-tests revealed a statistically significant increase in left DTI-ALPS index values post-treatment (t = -2.781, p = 0.024), suggesting improvements in glymphatic function. CONCLUSION: 1. Lecanemab treatment in AD patients from Hunan, China, is generally safe, though its long-term efficacy requires further investigation. 2. The use of 7.0T MRI, particularly its SWI sequence, enhances monitoring of microbleeds and improves safety surveillance. Furthermore, DTI-ALPS, which reflects the glymphatic function, presents as a potential imaging marker for assessing treatment efficacy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.082
GPT teacher head0.333
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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