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INTRODUCTION: Assessing the potential sources of bias and variability of the Centiloid (CL) scale is fundamental for its appropriate clinical application. METHODS: We included 533 participants from AMYloid imaging to Prevent Alzheimer's Disease (AMYPAD DPMS) and Alzheimer's Disease Neuroimaging Initiative (ADNI) cohorts. Thirty‐two CL pipelines were created using different combinations of reference region (RR), RR and target types, and quantification spaces. Generalized estimating equations stratified by amyloid positivity were used to assess the impact of the quantification pipeline, radiotracer, age, brain atrophy, and harmonization status on CL. RESULTS: RR selection and RR type impact CL the most, particularly in amyloid‐negative individuals. The standard CL pipeline with the whole cerebellum as RR is robust against brain atrophy and differences in image resolution, with 95% confidence intervals below ± 3.95 CL for amyloid beta positivity cutoffs (CL < 24). DISCUSSION: The standard CL pipeline is recommended for most scenarios. Confidence intervals should be considered when operationalizing CL cutoffs in clinical and research settings. Highlights: We developed a framework for evaluating Centiloid (CL) variability to different factors.Reference region selection and delineation had the highest impact on CL values.Whole cerebellum (WCB) and whole cerebellum plus brainstem (WCB+BSTM) as reference regions yielded consistent results across tracers.The standard CL pipeline is robust against atrophy and image resolution variation.Estimated within‐ and between‐pipeline variability (95% confidence interval) in absolute CL units. [ABSTRACT FROM AUTHOR]

Background: The Centiloid scale is a well‐established standardized metric for estimating amyloid load in clinical and research settings. In this study, we evaluated the stability of Centiloid to pipeline design options, tracer, and image harmonization. Method: A total of 670 participants of the AMYPAD DPMS and PNHS multicenter studies with either [18F]flutemetamol or [18F]florbetaben PET scans and T1‐weighted MRI were included (Table1). PET images were harmonized to achieve an 8 mm Full‐Width‐at‐Half‐Maximum(FWHM) effective image resolution. Using SPM12, 32 Centiloid pipelines were created, calibrated and validated, based on combinations of four reference regions (Whole Cerebellum[WCB], Cerebellar Gray[CG], Whole Cerebellum+Brainstem[WCB+BSTM], Pons), two target VOI types (standard GAAIN vs subject‐based), two reference region types (standard GAAIN vs subject‐based) and two analysis spaces (native vs MNI). Generalized Estimating Equations (GEE) were used to evaluate the impact of the different factors on Centiloid. First, a base model including only the pipeline design factors was defined. Then, harmonization status (original vs harmonized PET images), tracer, and grey matter volume, as a measure of cerebral atrophy, were added to the base GEE model. Analyses were performed in the two cohorts separately and changes >5 Centiloid were considered relevant. Result: Quantification space, target VOI, and reference region type had a small impact on Centiloid. Selection of reference region had the largest impact on Centiloid as using the Pons resulted in ∆Centiloid∼‐12.00 compared with using the standard reference region, WCB (Table2). Harmonization status affected Centiloid only for some reference regions: CG and Pons were sensitive to harmonization status (∆Centiloid∼ 5.45 and ‐5.70 respectively). In contrast, Centiloid was minimally affected by harmonization when using WCB and WCB+BSTM (Table3). Tracer and atrophy had a minimal effect on Centiloid for both DPMS and PNHS cohorts. Conclusion: Centiloid quantification is robust against differences in analysis space, reference region type, target VOI, tracer and the presence of atrophy. However, choice of reference region can significantly influence Centiloid, with the Pons having the largest impact on Centiloid relative to other reference regions. Centiloid is stable against harmonization status and image resolution heterogeneities while using WCB as reference region. [ABSTRACT FROM AUTHOR]

Deposition of amyloid and tau pathology can be quantified in vivo using positron emission tomography (PET). Accurate longitudinal measurements of accumulation from these images are critical for characterizing the start and spread of the disease. However, these measurements are challenging; precision and accuracy can be affected substantially by various sources of errors and variability. This review, supported by a systematic search of the literature, summarizes the current design and methodologies of longitudinal PET studies. Intrinsic, biological causes of variability of the Alzheimer's disease (AD) protein load over time are then detailed. Technical factors contributing to longitudinal PET measurement uncertainty are highlighted, followed by suggestions for mitigating these factors, including possible techniques that leverage shared information between serial scans. Controlling for intrinsic variability and reducing measurement uncertainty in longitudinal PET pipelines will provide more accurate and precise markers of disease evolution, improve clinical trial design, and aid therapy response monitoring. [ABSTRACT FROM AUTHOR]