Classification of tau status with machine learning models in amyloid-positive cohorts.

INTRODUCTION: Although tau positron emission tomography (PET) imaging is effective for staging tau pathology, it is limited clinically by cost and availability. Machine learning models based on magnetic resonance imaging (MRI)- and amyloid PET-derived features may serve as useful screening tools for tau pathology. METHODS: Multiple machine learning models were developed to classify tau positivity in the Braak III/IV region using structural MRI, amyloid PET, and demographic features. Alzheimer's Disease Neuroimaging Initiative (ADNI) (n = 410) data were used for model training. Open Access Series of Imaging Studies (OASIS-3; n = 143) and the Standardized Centralized Alzheimer's Disease Neuroimaging (SCAN; n = 154) data were used for external validation. RESULTS: Logistic regression achieved the best performance with areas under the curve (AUCs) of 0.92 for both internal and external validation. Combined external validation yielded accuracy/sensitivity/specificity of 85%/83%/85%. Subjects with mild cognitive impairment and predicted tau positivity progressed to AD at a significantly faster pace (p < 10-6). DISCUSSION: Our model demonstrates the feasibility of classifying tau burden in amyloid-positive cohorts with MRI- and amyloid PET-derived features and may serve as a surrogate biomarker.