Joseph Therriault1,2,3, Andrea L Benedet1,2,3, Tharick A Pascoal1,2,3, Melissa Savard1, Nicholas J Ashton4,5, Mira Chamoun1,2, Cecile Tissot1,2,3, Firoza Lussier1,2,3, Min Su Kang1,2,3, Gleb Bezgin1,2,3, Tina Wang1,2,3, Jaime Fernandes-Arias1,2,3, Gassan Massarweh3,6, Paolo Vitali2, Henrik Zetterberg4,5, Kaj Blennow4,5, Paramita Saha-Chaudhuri7, Jean-Paul Soucy2,3, Serge Gauthier1,2, Pedro Rosa-Neto8,2,3. 1. Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada. 2. Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada. 3. Montreal Neurological Institute, Montreal, Quebec, Canada. 4. Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, Mölndal, Sweden. 5. Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden. 6. Department of Radiochemistry, McGill University, Montreal, Quebec, Canada; and. 7. Department of Epidemiology and Biostatistics, McGill University, Montreal, Quebec, Canada. 8. Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada pedro.rosa@mcgill.ca.
Abstract
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.
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.
Authors: Eduardo R Zimmer; Pedro Rosa-Neto; Tharick A Pascoal; João Pedro Ferrari-Souza; Pâmela C L Ferreira; Bruna Bellaver; Cécile Tissot; Yi-Ting Wang; Douglas T Leffa; Wagner S Brum; Andréa L Benedet; Nicholas J Ashton; Marco Antônio De Bastiani; Andréia Rocha; Joseph Therriault; Firoza Z Lussier; Mira Chamoun; Stijn Servaes; Gleb Bezgin; Min Su Kang; Jenna Stevenson; Nesrine Rahmouni; Vanessa Pallen; Nina Margherita Poltronetti; William E Klunk; Dana L Tudorascu; Ann D Cohen; Victor L Villemagne; Serge Gauthier; Kaj Blennow; Henrik Zetterberg; Diogo O Souza; Thomas K Karikari Journal: Mol Psychiatry Date: 2022-08-10 Impact factor: 13.437
Authors: Tharick A Pascoal; Andrea L Benedet; Dana L Tudorascu; Joseph Therriault; Sulantha Mathotaarachchi; Melissa Savard; Firoza Z Lussier; Cécile Tissot; Mira Chamoun; Min Su Kang; Jenna Stevenson; Gassan Massarweh; Marie-Christine Guiot; Jean-Paul Soucy; Serge Gauthier; Pedro Rosa-Neto Journal: Brain Date: 2021-12-16 Impact factor: 15.255
Authors: A Leuzy; N J Ashton; N Mattsson-Carlgren; A Dodich; M Boccardi; J Corre; A Drzezga; A Nordberg; R Ossenkoppele; H Zetterberg; K Blennow; G B Frisoni; V Garibotto; O Hansson Journal: Eur J Nucl Med Mol Imaging Date: 2021-03-05 Impact factor: 9.236
Authors: Olga Krasnovskaya; Daniil Spector; Alexander Zlobin; Kirill Pavlov; Peter Gorelkin; Alexander Erofeev; Elena Beloglazkina; Alexander Majouga Journal: Int J Mol Sci Date: 2020-12-02 Impact factor: 5.923
Authors: Cécile Tissot; Joseph Therriault; Tharick A Pascoal; Mira Chamoun; Firoza Z Lussier; Melissa Savard; Sulantha S Mathotaarachchi; Andréa L Benedet; Emilie M Thomas; Marlee Parsons; Ziad Nasreddine; Pedro Rosa-Neto; Serge Gauthier Journal: Alzheimers Dement (N Y) Date: 2021-03-31
Authors: Nicholas J Ashton; Tharick A Pascoal; Pedro Rosa-Neto; Kaj Blennow; Thomas K Karikari; Andréa L Benedet; Juan Lantero-Rodriguez; Gunnar Brinkmalm; Anniina Snellman; Michael Schöll; Claire Troakes; Abdul Hye; Serge Gauthier; Eugeen Vanmechelen; Henrik Zetterberg Journal: Acta Neuropathol Date: 2021-02-14 Impact factor: 17.088
Authors: Joseph Therriault; Tharick A Pascoal; Marcus Sefranek; Sulantha Mathotaarachchi; Andrea L Benedet; Mira Chamoun; Firoza Z Lussier; Cécile Tissot; Bruna Bellaver; Pamela S Lukasewicz; Eduardo R Zimmer; Paramita Saha-Chaudhuri; Serge Gauthier; Pedro Rosa-Neto Journal: Ann Clin Transl Neurol Date: 2021-10-07 Impact factor: 4.511
Authors: Yi-Ting T Wang; Tharick A Pascoal; Joseph Therriault; Min Su Kang; Andréa L Benedet; Melissa Savard; Cécile Tissot; Firoza Z Lussier; Jaime Fernandez Arias; Sulantha Mathotaarachchi; Maria Natasha Rajah; Serge Gauthier; Pedro Rosa-Neto Journal: Brain Commun Date: 2021-06-07