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

Boccalini, Cecilia ; Mathoux, Gregory ; Hristovska, Ines LU ; Ribaldi, Federica ; Peretti, Debora Elisa ; Arnone, Annachiara ; Scheffler, Max ; Frisoni, Giovanni Battista ; Hansson, Oskar LU orcid and Vogel, Jacob W. LU , et al. (2025) In Neurology 105(7).
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 18F-Flortaucipir tau-PET scan and a Mini-Mental State Examination... (More)

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 18F-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, p < 0.001) and a fair agreement between visual and automated subtypes (κ = 0.39, p < 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.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Neurology
volume
105
issue
7
article number
e213950
publisher
Lippincott Williams & Wilkins
external identifiers
  • scopus:105015895429
  • pmid:40939126
ISSN
1526-632X
DOI
10.1212/WNL.0000000000213950
language
English
LU publication?
yes
id
5ba75bc4-a3b9-4901-a74d-d3d0c9a0fa52
date added to LUP
2025-10-10 10:20:17
date last changed
2025-10-14 09:02:14
@article{5ba75bc4-a3b9-4901-a74d-d3d0c9a0fa52,
  abstract     = {{<p>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 18F-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 &gt; 0.65, p &lt; 0.001) and a fair agreement between visual and automated subtypes (κ = 0.39, p &lt; 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.</p>}},
  author       = {{Boccalini, Cecilia and Mathoux, Gregory and Hristovska, Ines and Ribaldi, Federica and Peretti, Debora Elisa and Arnone, Annachiara and Scheffler, Max and Frisoni, Giovanni Battista and Hansson, Oskar and Vogel, Jacob W. and Garibotto, Valentina}},
  issn         = {{1526-632X}},
  language     = {{eng}},
  number       = {{7}},
  publisher    = {{Lippincott Williams & Wilkins}},
  series       = {{Neurology}},
  title        = {{Visual Classification of Tau-PET Detects 4 Subtypes With Different Long-Term Outcomes}},
  url          = {{http://dx.doi.org/10.1212/WNL.0000000000213950}},
  doi          = {{10.1212/WNL.0000000000213950}},
  volume       = {{105}},
  year         = {{2025}},
}