Purpose: Several [¹⁸F]Flortaucipir cutoffs have been proposed for tau PET positivity (T⁺) in Alzheimer's disease (AD), but none were data-driven. The aim of this study was to establish and validate unsupervised T⁺cutoffs by applying Gaussian mixture models (GMM).

Methods: Amyloid negative (A^-) cognitively normal (CN) and amyloid positive (A⁺) AD-related dementia (ADRD) subjects from ADNI (n=269) were included. ADNI (n=475) and Geneva Memory Clinic (GMC) cohorts (n=98) were used for validation. GMM-based cutoffs were extracted for the temporal meta-ROI, and validated against previously published cutoffs and visual rating.

Results: GMM-based cutoffs classified less subjects as T⁺, mainly in the A^-CN (<3.4% vs >28.5%) and A⁺CN (<14.5% vs >42.9%) groups and showed higher agreement with visual rating (ICC=0.91 vs ICC<0.62) than published cutoffs.

Conclusion: We provided reliable data-driven [¹⁸F]Flortaucipir cutoffs for in vivo T⁺detection in AD. These cutoffs might be useful to select participants in clinical and research studies.