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Tau-PET的视觉分类可检测出具有不同长期预后的4种亚型。

Visual Classification of Tau-PET Detects 4 Subtypes With Different Long-Term Outcomes.

作者信息

Boccalini Cecilia, Mathoux Gregory, Hristovska Ines, Ribaldi Federica, Peretti Debora Elisa, Arnone Annachiara, Scheffler Max, Frisoni Giovanni Battista, Hansson Oskar, Vogel Jacob W, Garibotto Valentina

机构信息

Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Geneva University Neurocenter and Faculty of Medicine, University of Geneva, Switzerland.

Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals, Switzerland.

出版信息

Neurology. 2025 Oct;105(7):e213950. doi: 10.1212/WNL.0000000000213950. Epub 2025 Sep 12.

Abstract

BACKGROUND AND OBJECTIVES

Tau accumulation pattern shows substantial variability in Alzheimer disease (AD), and 4 distinct spatiotemporal trajectories were distinguished using a data-driven approach called the Subtype and Stage Inference (SuStaIn). A visual method to validate and identify these subtypes is a requirement for their clinical translation. Our study aimed to provide a standardized topographic method for identifying tau patterns visually using tau-PET in a clinical setting.

METHODS

Participants in this prospective study were included from the memory clinic of Geneva University Hospital. Inclusion criteria required participants to have undergone at least 1 F-Flortaucipir tau-PET scan and a Mini-Mental State Examination (MMSE) within a 1-year time frame. All scans were classified into different tau subtypes (limbic [S1], medial temporal lobe-sparing [S2], posterior [S3], and lateral temporal [S4]) using both visual rating and SuStain algorithm. A subgroup underwent amyloid-PET and clinical follow-up. Cohen's κ tested the agreement between raters and between visual and automated subtypes. Chi-squared and Kruskal-Wallis tests assessed differences in clinical and biomarker features between subtypes, whereas differences in cognitive trajectories were tested using linear mixed-effects models, controlling for age, sex, and clinical and tau stages.

RESULTS

A total of 245 tau-PET scans of individuals ranging from cognitively unimpaired to mild dementia (mean age: 68.25 years, 52% women) were included and classified into different tau pattern subtypes. A substantial agreement between raters was found in visually interpreting tau subtypes (κ > 0.65, < 0.001) and a fair agreement between visual and automated subtypes (κ = 0.39, < 0.001), with the automated approach more likely to classify a scan as tau negative and lower agreement between methods in more severe cases and AD clinical variants. Regarding the visual classification, individuals with S2 subtype were younger than S1 and S3, had lower MMSE and verbal fluency scores than S4 and S1, showed higher global tau burden than other subtypes, and a steeper cognitive decline.

DISCUSSION

Visual classification reliably identified 4 tau patterns that differ in global tau load, clinical features, and long-term outcomes, suggesting its clinical usefulness for the detection of higher-risk AD variants. A clinically implementable classification of subtypes with faster decline is paramount for personalized diagnosis, accurate prognosis, and treatment.

摘要

背景与目的

在阿尔茨海默病(AD)中,tau蛋白的积聚模式存在显著差异,通过一种名为亚型与阶段推断(SuStaIn)的数据驱动方法区分出了4种不同的时空轨迹。对于这些亚型的临床转化而言,需要一种可视化方法来验证和识别它们。我们的研究旨在提供一种标准化的地形图方法,以便在临床环境中通过tau蛋白正电子发射断层扫描(PET)直观地识别tau蛋白模式。

方法

这项前瞻性研究的参与者来自日内瓦大学医院记忆门诊。纳入标准要求参与者在1年时间内至少接受过1次18F-氟代托品tau蛋白PET扫描和简易精神状态检查(MMSE)。所有扫描结果均通过视觉评分和SuStaIn算法分为不同的tau蛋白亚型(边缘型[S1]、内侧颞叶保留型[S

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ff8/12439490/e8d5609ef158/WNL-2025-200283f1.jpg

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