ArticleAlzheimer's & dementia (New York, N. Y.)
Predicting regional tau accumulation with machine learning-based tau-PET and advanced radiomics.
Article in Alzheimer's & dementia (New York, N. Y.). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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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.
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Who cites it
4 citing papers in PubMed.
- CSF Proteomics and Machine Learning Reveal Distinct Stages Across the Alzheimer's Disease Continuum.medRxiv : the preprint server for health sciences · 2025Article
- Cerebrospinal fluid proteomics for predictive assessment of Alzheimer's Disease risk.medRxiv : the preprint server for health sciences · 2025Article
- Integrating genetic and immune profiles for personalized immunotherapy in Alzheimer's disease.Frontiers in medicine · 2025Review
- Predicting regional tau accumulation with machine learning-based tau-PET and advanced radiomics.Alzheimer's & dementia (New York, N. Y.)Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionAlzheimer's disease is partially characterized by the progressive accumulation of aggregated tau-containing neurofibrillary tangles. Although the association between accumulated tau, neurodegeneration, and cognitive decline is critical for disease understanding and clinical trial design, we still lack robust tools to predict individualized trajectories of tau accumulation. Our objective was to assess whether brain imaging biomarkers of flortaucipir-positron emission tomography (PET), in combination with clinical and genomic measures, could predict future pathological tau accumulation.
methodsWe quantified the disease profile of participants (
resultsIn binary classification for predicting stable/slow- versus fast-progressors, the area-under-the-receiver-operating-characteristic curve was 0.86 in the AD-signature region and 0.83, 0.82, 0.84, and 0.83 in frontal, occipital, parietal, and temporal regions, respectively. The trained models successfully predicted annualized-rate-of-change of flortaucipir-PET regional flortaucipir SUVr in AD-signature and lobar regions (Pearson-correlation [ DISCUSSION: Taken together, our results propose a robust approach to predict future tau accumulation that may improve the ability to enroll, stratify, and gauge efficacy in clinical trial participants. Highlights: Machine learning predicts the future rate of tau accumulation in Alzheimer's disease.Tau prediction in lobar/global regions benefits from diverse multimodal features.This prognostic index can serve as a sensitive tool for patient stratification.
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Registered trials
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 Socratic graph.