Estimation of amyloid distribution by 18Fflutemetamol PET predicts the neuropathological phase of amyloid β-protein deposition

The deposition of the amyloid β-protein (Aβ) in senile plaques is one of the histopathological hallmarks of Alzheimer’s disease (AD). Aβ-plaques arise first in neocortical areas and, then, expand into further brain regions in a process described by 5 phases. Since it is possible to identify amyloid pathology with radioactive-labeled tracers by positron emission tomography (PET) the question arises whether it is possible to distinguish the neuropathological Aβ-phases with amyloid PET imaging. To address this question we reassessed 97 cases of the end-of-life study cohort of the phase 3 18Fflutemetamol trial (ClinicalTrials.gov identifiers NCT01165554, and NCT02090855) by combining the standardized uptake value ratios (SUVRs) with pons as reference region for cortical and caudate nucleus-related 18Fflutemetamol-retention. We tested them for their prediction of the neuropathological pattern found at autopsy. By defining threshold levels for cortical and caudate nucleus SUVRs we could distinguish different levels of 18Fflutemetamol uptake termed PET-Aβ phase estimates. When comparing these PET-Aβ phase estimates with the neuropathological Aβ-phases we found that PET-Aβ phase estimate 0 corresponded with Aβ-phases 0-2, 1 with Aβ-phase 3, 2 with Aβ-phase 4, and 3 with Aβ-phase 5. Classification using the PET-Aβ phase estimates predicted the correct Aβ-phase in 72.16% of the cases studied here. Bootstrap analysis was used to confirm the robustness of the estimates around this association. When allowing a range of ± 1 phase for a given Aβ-phase correct classification was given in 96.91% of the cases. In doing so, we provide a novel method to convert SUVR-levels into PET-Aβ phase estimates that can be easily translated into neuropathological phases of Aβ-deposition. This method allows direct conclusions about the pathological distribution of amyloid plaques (Aβ-phases) in vivo. Accordingly, this method may be ideally suited to detect early preclinical AD-patients, to follow them with disease progression, and to provide a more precise prognosis for them based on the knowledge about the underlying pathological phase of the disease.