Julie Ottoy1, Jeroen Verhaeghe1, Ellis Niemantsverdriet2, Leonie Wyffels3, Charisse Somers2, Ellen De Roeck2,4, Hanne Struyfs2, Femke Soetewey2, Steven Deleye1, Tobi Van den Bossche2,5,6,7, Sara Van Mossevelde2,5,6,7, Sarah Ceyssens3, Jan Versijpt8, Sigrid Stroobants3, Sebastiaan Engelborghs2,7, Steven Staelens9. 1. Molecular Imaging Center Antwerp, University of Antwerp, Antwerp, Belgium. 2. Institute Born-Bunge, University of Antwerp, Antwerp, Belgium. 3. Department of Nuclear Medicine, Antwerp University Hospital, Edegem, Belgium. 4. Developmental and Lifespan Psychology, Vrije Universiteit Brussel, Brussels, Belgium. 5. Department of Molecular Genetics, VIB, University of Antwerp, Antwerp, Belgium. 6. Department of Neurology, Antwerp University Hospital, Edegem, Belgium. 7. Department of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA) Middelheim and Hoge Beuken, Antwerp, Belgium; and. 8. Department of Neurology, University Hospital Brussels, Brussels, Belgium. 9. Molecular Imaging Center Antwerp, University of Antwerp, Antwerp, Belgium steven.staelens@uantwerpen.be.
Abstract
Increased brain uptake of 18F-AV45 visualized by PET is a key biomarker for Alzheimer disease (AD). The SUV ratio (SUVR) is widely used for quantification, but is subject to variability based on choice of reference region and changes in cerebral blood flow. Here we validate the SUVR method against the gold standard volume of distribution (VT) to assess cross-sectional differences in plaque load. Methods: Dynamic 60-min 18F-AV45 (291 ± 67 MBq) and 1-min 15O-H2O (370 MBq) scans were obtained in 35 age-matched elderly subjects, including 10 probable AD, 15 amnestic mild cognitive impairment (aMCI), and 10 cognitively healthy controls (HCs). 18F-AV45 VT was determined from 2-tissue-compartment modeling using a metabolite-corrected plasma input function. Static SUVR was calculated at 50-60 min after injection, using either cerebellar gray matter (SUVRCB) or whole subcortical white matter (SUVRWM) as the reference. Additionally, whole cerebellum, pons, centrum semiovale, and a composite region were examined as alternative references. Blood flow was quantified by 15O-H2O SUV. Data are presented as mean ± SEM. Results: There was rapid metabolization of 18F-AV45, with only 35% of unchanged parent remaining at 10 min. Compared with VT, differences in cortical Aβ load between aMCI and AD were overestimated by SUVRWM (+4% ± 2%) and underestimated by SUVRCB (-10% ± 2%). VT correlated better with SUVRWM (Pearson r: from 0.63 for posterior cingulate to 0.89 for precuneus, P < 0.0001) than with SUVRCB (Pearson r: from 0.51 for temporal lobe [P = 0.002] to 0.82 for precuneus [P < 0.0001]) in all tested regions. Correlation results for the alternative references were in between those for CB and WM. 15O-H2O data showed that blood flow was decreased in AD compared with aMCI in cortical regions (-5% ± 1%) and in the reference regions (CB, -9% ± 8%; WM, -8% ± 8%). Conclusion: Increased brain uptake of 18F-AV45 assessed by the simplified static SUVR protocol does not truly reflect Aβ load. However, SUVRWM is better correlated with VT and more closely reflects VT differences between aMCI and AD than SUVRCB.
Increased brain uptake of 18F-AV45 visualized by PET is a key biomarker for Alzheimer disease (AD). The SUV ratio (SUVR) is widely used for quantification, but is subject to variability based on choice of reference region and changes in cerebral blood flow. Here we validate the SUVR method against the gold standard volume of distribution (VT) to assess cross-sectional differences in plaque load. Methods: Dynamic 60-min 18F-AV45 (291 ± 67 MBq) and 1-min 15O-H2O (370 MBq) scans were obtained in 35 age-matched elderly subjects, including 10 probable AD, 15 amnestic mild cognitive impairment (aMCI), and 10 cognitively healthy controls (HCs). 18F-AV45 VT was determined from 2-tissue-compartment modeling using a metabolite-corrected plasma input function. Static SUVR was calculated at 50-60 min after injection, using either cerebellar gray matter (SUVRCB) or whole subcortical white matter (SUVRWM) as the reference. Additionally, whole cerebellum, pons, centrum semiovale, and a composite region were examined as alternative references. Blood flow was quantified by 15O-H2O SUV. Data are presented as mean ± SEM. Results: There was rapid metabolization of 18F-AV45, with only 35% of unchanged parent remaining at 10 min. Compared with VT, differences in cortical Aβ load between aMCI and AD were overestimated by SUVRWM (+4% ± 2%) and underestimated by SUVRCB (-10% ± 2%). VT correlated better with SUVRWM (Pearson r: from 0.63 for posterior cingulate to 0.89 for precuneus, P < 0.0001) than with SUVRCB (Pearson r: from 0.51 for temporal lobe [P = 0.002] to 0.82 for precuneus [P < 0.0001]) in all tested regions. Correlation results for the alternative references were in between those for CB and WM. 15O-H2O data showed that blood flow was decreased in AD compared with aMCI in cortical regions (-5% ± 1%) and in the reference regions (CB, -9% ± 8%; WM, -8% ± 8%). Conclusion: Increased brain uptake of 18F-AV45 assessed by the simplified static SUVR protocol does not truly reflect Aβ load. However, SUVRWM is better correlated with VT and more closely reflects VT differences between aMCI and AD than SUVRCB.
Authors: Val J Lowe; Emily S Lundt; Matthew L Senjem; Christopher G Schwarz; Hoon-Ki Min; Scott A Przybelski; Kejal Kantarci; David Knopman; Ronald C Petersen; Clifford R Jack Journal: J Nucl Med Date: 2018-04-19 Impact factor: 10.057
Authors: Julie Ottoy; Ellis Niemantsverdriet; Jeroen Verhaeghe; Ellen De Roeck; Hanne Struyfs; Charisse Somers; Leonie Wyffels; Sarah Ceyssens; Sara Van Mossevelde; Tobi Van den Bossche; Christine Van Broeckhoven; Annemie Ribbens; Maria Bjerke; Sigrid Stroobants; Sebastiaan Engelborghs; Steven Staelens Journal: Neuroimage Clin Date: 2019-03-13 Impact factor: 4.881
Authors: Ellis Niemantsverdriet; Julie Ottoy; Charisse Somers; Ellen De Roeck; Hanne Struyfs; Femke Soetewey; Jeroen Verhaeghe; Tobi Van den Bossche; Sara Van Mossevelde; Johan Goeman; Peter Paul De Deyn; Peter Mariën; Jan Versijpt; Kristel Sleegers; Christine Van Broeckhoven; Leonie Wyffels; Adrien Albert; Sarah Ceyssens; Sigrid Stroobants; Steven Staelens; Maria Bjerke; Sebastiaan Engelborghs Journal: J Alzheimers Dis Date: 2017 Impact factor: 4.472
Authors: Fatemah A Sakr; Michel J Grothe; Enrica Cavedo; Irina Jelistratova; Marie-Odile Habert; Martin Dyrba; Gabriel Gonzalez-Escamilla; Hugo Bertin; Maxime Locatelli; Stephane Lehericy; Stefan Teipel; Bruno Dubois; Harald Hampel Journal: Alzheimers Res Ther Date: 2019-01-31 Impact factor: 8.823
Authors: Thomas D Parker; David M Cash; Christopher A S Lane; Kirsty Lu; Ian B Malone; Jennifer M Nicholas; Sarah-Naomi James; Ashvini Keshavan; Heidi Murray-Smith; Andrew Wong; Sarah M Buchanan; Sarah E Keuss; Carole H Sudre; Marc Modat; David L Thomas; Sebastian J Crutch; Marcus Richards; Nick C Fox; Jonathan M Schott Journal: PLoS One Date: 2019-10-17 Impact factor: 3.240