Visual classification of tau‐PET detects four subtypes with different long‐term outcomes

Abstract Background Substantial variability in tau accumulation patterns in Alzheimer’s disease (AD) population has now become accepted. Subtype and Stage Inference (SuStaIn) has distinguished four distinct spatiotemporal trajectories of tau pathology: limbic (S1), medial temporal lobe‐sparing (S2), posterior (S3), and lateral temporal (S4). A visual method to validate and identify them is a requirement for their clinical translation. Our study aims to provide evidence for multiple tau accumulation patterns in a clinical setting by developing and testing a novel topographic visual method for tau‐PET based on SuStaIn subtypes. Method We included 245 participants from the Geneva Memory Clinic, who underwent 18 F‐Flortaucipir‐PET. All scans were classified into different subtypes by visual ratings and the automated SuStain algorithm. Cohen's kappa (k) tested the agreement between raters and between visual and automated subtypes. Chi‐squared and Kruskal‐Wallis tests were used to test differences in clinical features, tau, and amyloid (Aβ) loads between subtypes. Differences in cognitive trajectories were tested using linear mixed‐effects models, controlling for age, sex, and tau stages. Result A substantial agreement between raters was found in visually interpreting tau pattern subtypes (k>0.65, p<0.001) and a fair agreement between visual and automated subtypes (k=0.39, p<0.001), with the automated approach detecting a higher rate of negative scans. According to the visual classification, individuals with S2 subtype were younger than S1 and S3 (p<0.001), had worse MMSE and verbal fluency scores (p<0.05) than S4 and S1, showed higher global tau than other subtypes (p<0.05), and a steeper cognitive decline. Conclusion Our results show that visual classification can reliably identify four tau patterns, differing in global tau loads, clinical characteristics, and long‐term outcomes, suggesting its clinical usefulness for the detection of higher‐risk AD variants. A clinically implementable classification in subtypes with faster decline is paramount for personalized diagnosis and therapy.