RT Journal Article SR Electronic T1 Determining Amyloid-β Positivity Using 18F-AZD4694 PET Imaging JF Journal of Nuclear Medicine JO J Nucl Med FD Society of Nuclear Medicine SP 247 OP 252 DO 10.2967/jnumed.120.245209 VO 62 IS 2 A1 Joseph Therriault A1 Andrea L. Benedet A1 Tharick A. Pascoal A1 Melissa Savard A1 Nicholas J. Ashton A1 Mira Chamoun A1 Cecile Tissot A1 Firoza Lussier A1 Min Su Kang A1 Gleb Bezgin A1 Tina Wang A1 Jaime Fernandes-Arias A1 Gassan Massarweh A1 Paolo Vitali A1 Henrik Zetterberg A1 Kaj Blennow A1 Paramita Saha-Chaudhuri A1 Jean-Paul Soucy A1 Serge Gauthier A1 Pedro Rosa-Neto YR 2021 UL http://jnm.snmjournals.org/content/62/2/247.abstract AB Amyloid-β deposition into plaques is a pathologic hallmark of Alzheimer disease 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 used in multiple academic centers, no studies have assessed a quantitative cutoff 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 gray matter as a reference region. We compared 5 methods for deriving a quantitative threshold for 18F-AZD4694 PET positivity: comparison with young-control SUV ratios (SUVRs), receiver-operating-characteristic (ROC) curves based on clinical classification of cognitively unimpaired elderly versus Alzheimer disease dementia, ROC curves based on visual Aβ-positive/Aβ-negative classification, gaussian mixture modeling, and comparison with cerebrospinal fluid measures of amyloid-β, specifically the Aβ42/Aβ40 ratio. Results: We observed good convergence among the 4 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 cerebrospinal fluid measures of amyloid-β (optimal cut point, 1.51 SUVR). Means and 2 SDs from young controls resulted in a lower threshold (1.33 SUVR) that did not agree with the other methods and labeled most elderly individuals as Aβ-positive. Conclusion: Good convergence was obtained among several methods for determining an optimal cutoff for 18F-AZD4694 PET positivity. Despite conceptual and analytic idiosyncrasies linked with dichotomization of continuous variables, an 18F-AZD4694 threshold of 1.55 SUVR had reliable discriminative accuracy. Although clinical use of amyloid PET is currently by visual inspection of scans, quantitative thresholds may be helpful to arbitrate disagreement among raters or in borderline cases.