Authors: Gaia Rizzo, Alex Whittington, Jacob Hesterman, Roger N Gunn
Published: 2020-12-07
DOI: 10.1002/alz.043823
Source: Full article
AbstractBackgroundThe global amyloid‐β (Aβ) burden in Alzheimer’s disease (AD) is routinely quantified from static amyloid PET scans using SUVR. We have previously used AmyloidIQ to analyse over 3000 18F‐Florbetapir, 18F‐Florbetaben and 18F‐Flutemetamol scans. These analyses demonstrated that the outcome measure amyloid load (AβL) derived from AmyloidIQ is more powerful than SUVR in cross‐sectional and longitudinal analyses (Whittington et al. 2018 JNM) as well as having a better agreement with visual reads (Whittington et al. HAI 2019). In this work, we extend the AmyloidIQ methodology to [18F]NAV4694 images downloaded from the Global Alzheimer's Association Interactive Network (GAAIN) website (http://www.gaain.org).MethodAβL is automatically calculated by the AmyloidIQ algorithm from an individual Aβ‐PET scan using voxel‐wise regression with two canonical images that represent non‐displaceable and specific binding signals. The canonical images were derived from 18F‐Florbetapir ADNI data (Whittington et al. 2018 JNM). A composite SUVR was also calculated for each scan. 52 [18F]NAV4694 scans (35 healthy controls, HC, 10 mild cognitive impaired, MCI, and 7 AD) were obtained from GAAIN. The effect sizes (Hedges’ g) between HC and AD patients, and between HC and AD/MCI of AβL and SUVR were compared in [18F]NAV4694 cross‐sectional data.ResultThe AmyloidIQ algorithm was successfully extended to [18F]NAV4694 data with accurate characterization of the data obtained with the previously derived canonical images, now demonstrating its applicability to four different Aβ tracers (Figure 1). In this small sample dataset the cross‐sectional analysis showed that, AβL had similar performance to SUVR (effect size between non‐HC patients and HC was 2.29 for AβL and 2.32 SUVR, and between HC and AD patients was 4.41 for AβL and 4.90 for SUVR). In previous works, AβL has shown better agreement with visual reads than SUVR (Figure 2) and more power in cross‐sectional analysis (Figure 3) with an average increase in effect size of 44% and 21% compared to SUVR for [18F]Florbetapir and [18F]Florbetaben respectively.ConclusionAmyloidIQ is a powerful algorithm that has been successfully applied to four different Aβ tracers and that can be used for stratification. A larger sample is required to determine whether AmyloidIQ is more powerful than SUVR for [18F]NAV4694.