Validation of amyloid PET positivity thresholds in centiloids: a multisite PET study approach

Abstract

Background: Inconsistent positivity thresholds, image analysis pipelines, and quantitative outcomes are key challenges of multisite studies using more than one β-amyloid (Aβ) radiotracer in positron emission tomography (PET). Variability related to these factors contributes to disagreement and lack of replicability in research and clinical trials. To address these problems and promote Aβ PET harmonization, we used [¹⁸F]florbetaben (FBB) and [¹⁸F]florbetapir (FBP) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to derive (1) standardized Centiloid (CL) transformations and (2) internally consistent positivity thresholds based on separate young control samples.

Methods: We analyzed Aβ PET data using a native-space, automated image processing pipeline that is used for PET quantification in many large, multisite AD studies and trials and made available to the research community. With this pipeline, we derived SUVR-to-CL transformations using the Global Alzheimer's Association Interactive Network data; we used reference regions for cross-sectional (whole cerebellum) and longitudinal (subcortical white matter, brain stem, whole cerebellum) analyses. Finally, we developed a FBB positivity threshold using an independent young control sample (N=62) with methods parallel to our existing FBP positivity threshold and validated the FBB threshold using a data-driven approach in ADNI participants (N=295).

Results: The FBB threshold based on the young sample (1.08; 18 CL) was consistent with that of the data-driven approach (1.10; 21 CL), and the existing FBP threshold converted to CL with the derived transformation (1.11; 20 CL). The following equations can be used to convert whole cerebellum- (cross-sectional) and composite- (longitudinal) normalized FBB and FBP data quantified with the native-space pipeline to CL units: [¹⁸F]FBB: CL_{whole cerebellum} = 157.15 × SUVR_FBB - 151.87; threshold=1.08, 18 CL [¹⁸F]FBP: CL_{whole cerebellum} = 188.22 × SUVR_FBP - 189.16; threshold=1.11, 20 CL [¹⁸F]FBB: CL_composite = 244.20 × SUVR_FBB - 170.80 [¹⁸F]FBP: CL_composite = 300.66 × SUVR_FBP - 208.84 CONCLUSIONS: FBB and FBP positivity thresholds derived from independent young control samples and quantified using an automated, native-space approach result in similar CL values. These findings are applicable to thousands of available and anticipated outcomes analyzed using this pipeline and shared with the scientific community. This work demonstrates the feasibility of harmonized PET acquisition and analysis in multisite PET studies and internal consistency of positivity thresholds in standardized units.

Keywords: Alzheimer’s disease; Amyloid imaging; Beta-amyloid; Centiloid; Florbetaben; Florbetapir; Standardization.

Fig. 1

a CL outcomes derived from UP’s level-1 analysis of the GAAIN 34 YC-0 and 45 AD-100 scans vs. published CL values. The equation and R² indicate that the standard CL pipeline was appropriately replicated. b FBP SUVRs derived from UP’s implementation of the ADNI FS v7.1 pipeline vs. SUVRs provided by UCB; FBP scans were provided to UP by UCB. The equation and R² indicate appropriate local implementation of the ADNI FS v7.1 pipeline

Fig. 2

Linear regressions of FBB (left) and FBP (right) whole cerebellum-normalized SUVRs derived from ADNI FS v7.1 pipeline against PiB SUVRs derived from the standard CL pipeline. A linear conversion from these regressions was used to create “Calculated” PiB SUVRs from FBB and FBP SUVRs

Fig. 3

All baseline ADNI FBP scans (N=1292) were analyzed with both the previous and updated ANDI pipelines. The best-fit linear regression line (black) was used to confirm that the previously validated FBP threshold (cortical summary/whole cerebellum SUVR=1.11) is unchanged with the FS v7.1 pipeline

Fig. 4

Gaussian mixture modeling distributions for ADNI baseline FBB scans analyzed with the updated ADNI pipeline