Therriault, J; Benedet, A; Pascoal, TA; Savard, M; Ashton, N; Chamoun, M; Tissot, C; ... Rosa-Neto, P; + view all Therriault, J; Benedet, A; Pascoal, TA; Savard, M; Ashton, N; Chamoun, M; Tissot, C; Lussier, F; Kang, MSP; Bergin, G; Wang, T; Fernandez-Arias, J; Massarweh, G; Tissot, C; Zetterberg, H; Blennow, K; Saha-Chaudhuri, P; Soucy, J-P; Gauthier, S; Rosa-Neto, P; - view fewer (2020) Determining Amyloid-β positivity using [18F]AZD4694 PET imaging. The Journal of Nuclear Medicine 10.2967/jnumed.120.245209. (In press).

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Abstract

Amyloid-β deposition into plaques is a pathological hallmark of Alzheimer’s disease (AD) appearing years before the onset of symptoms. Although cerebral amyloid-β deposition occurs on a continuum, dichotomization into positive and negative groups has advantages for diagnosis, clinical management and population enrichment for clinical trials. 18F-AZD4694 (also known as 18F-NAV4694) is an amyloid-β imaging ligand with high affinity for amyloid-β plaques. Despite being employed in multiple academic centers, no studies have assessed a quantitative cut-off for amyloid-β positivity using 18F-AZD4694 PET. Methods: We assessed 176 individuals [young adults (n = 22), cognitively unimpaired elderly (n = 89), and cognitively impaired (n = 65)] who underwent amyloid-β PET with 18F-AZD4694, lumbar puncture, structural MRI, and genotyping for APOEε4. 18F-AZD4694 values were normalized using the cerebellar grey matter as a reference region. We compared five methods for deriving a quantitative threshold for 18F-AZD4694 PET positivity: comparison with young controls SUVRs values, Receiver Operating Characteristic (ROC) curves based on clinical classification of CU elderly vs AD dementia, ROC curves based on visual Aβ+/Aβ- classification, Gaussian Mixture Modeling and comparison with cerebrospinal fluid measures of amyloid-β, specifically the Aβ42/Aβ40 ratio. Results: We observed good convergence between four methods ROC curves based on visual classification (optimal cut point: 1.55 SUVR), ROC curves based on clinical classification (optimal cut point: 1.56 SUVR) Gaussian Mixture Modeling (optimal cut point: 1.55 SUVR) and comparison with CSF measures of amyloid-β (optimal cut point: 1.51 SUVR). Means and 2 standard deviations from young controls resulted in a lower threshold (1.33 SUVR) that did not agree with the other methods and labeled the majority of elderly individuals as Aβ+. Conclusion: Good convergence was obtained between a number of methods for determining an optimal cut-off for 18F-AZD4694 PET positivity. Despite conceptual and analytical idiosyncrasies linked with dichotomization of continuous variables, an 18F-AZD4694 threshold of 1.55 SUVR had reliable discriminative accuracy. While clinical use of amyloid-PET currently is made by visual inspection of scans, quantitative thresholds may be helpful to arbitrate disagreement among raters or in borderline cases.

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