A multisite analysis of the concordance between visual image interpretation and quantitative analysis of [18F]flutemetamol amyloid PET images
Open accessAbstract
Background: [18 F]flutemetamol PET scanning provides information on brain amyloid load and has been approved for routine clinical use based upon visual interpretation as either negative (equating to none or sparse amyloid plaques) or amyloid positive (equating to moderate or frequent plaques). Quantitation is however fundamental to the practice of nuclear medicine and hence can be used to supplement amyloid reading methodology especially in unclear cases. Methods: A total of 2770 [18 F]flutemetamol images were collected from 3 clinical studies and 6 research cohorts with available visual reading of [18 F]flutemetamol and quantitative analysis of images. These were assessed further to examine both the discordance and concordance between visual and quantitative imaging primarily using thresholds robustly established using pathology as the standard of truth. Scans covered a wide range of cases (i.e. from cognitively unimpaired subjects to patients attending the memory clinics). Methods of quantifying amyloid ranged from using CE/510K cleared marked software (e.g. CortexID, Brass), to other research-based methods (e.g. PMOD, CapAIBL). Additionally, the clinical follow-up of two types of discordance between visual and quantitation (V+Q- and V-Q+) was examined with competing risk regression analysis to assess possible differences in prediction for progression to Alzheimer’s disease (AD) and other diagnoses (OD). Results: Weighted mean concordance between visual and quantitation using the autopsy-derived threshold was 94% using pons as the reference region. Concordance from a sensitivity analysis which assessed the maximum agreement for each cohort using a range of cut-off values was also estimated at approximately 96% (weighted mean). Agreement was generally higher in clinical cases compared to research cases. V-Q+ discordant cases were 11% more likely to progress to AD than V+Q- for the SUVr with pons as reference region. Conclusions: Quantitation of amyloid PET shows a high agreement vs binary visual reading and also allows for a continuous measure that, in conjunction with possible discordant analysis, could be used in the future to identify possible earlier pathological deposition as well as monitor disease progression and treatment effectiveness.
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Aβ PET and tau PET are not perfectly accurate in detection of neuropathology.
“Notably, Aβ PET and tau PET are not perfectly accurate in detection of neuropathology45,46”
From Highly accurate blood test for Alzheimer's disease is similar or superior to clinical cerebrospinal fluid tests (2024) · cites this paper as [45]
The Introduction (page unavailable) reports that [18F]flutemetamol amyloid PET was validated against pathology measures, with sensitivity and specificity over 90%—high, but not perfect. The paper does not address tau PET accuracy, so it supports only the amyloid PET part of the claim. (LLM, conf=0.90)
Experimental — accuracy not yet measured
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References 38 · Cited by 50
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- [3]Chételat G Arbizu J Barthel H Garibotto V Law I Morbelli S Amyloid-PET and 18F-FDG-PET in the diagnostic investigation of Alzheimer’s disease and other dementias The Lancet Neurology 2020 19 951 962 10.1016/S1474-4422(20)30314-8 33098804 · DOI · Paper page
- [4]Mirra SS Heyman A McKeel D Sumi SM Crain BJ Brownlee LM The Consortium to Establish a Registry for Alzheimer’s Disease (CERAD). Part II. Standardization of the neuropathologic assessment of Alzheimer’s disease Neurology. 1991 41 479 486 10.1212/wnl.41.4.479 2011243 · DOI · Paper page
- [5]Ikonomovic MD Buckley CJ Heurling K Sherwin P Jones PA Zanette M Post-mortem histopathology underlying β-amyloid PET imaging following flutemetamol F 18 injection Acta Neuropathol Commun 2016 4 130 10.1186/s40478-016-0399-z 27955679 PMC5154022 · DOI
- [7]Vandenberghe R Van Laere K Ivanoiu A Salmon E Bastin C Triau E 18F-flutemetamol amyloid imaging in Alzheimer disease and mild cognitive impairment: a phase 2 trial Ann Neurol 2010 68 319 329 10.1002/ana.22068 20687209 · DOI
- [8]Buckley CJ Sherwin PF Smith AP Wolber J Weick SM Brooks DJ Validation of an electronic image reader training programme for interpretation of [18F]flutemetamol β-amyloid PET brain images Nucl Med Commun 2017 38 234 241 10.1097/MNM.0000000000000633 27984539 PMC5318150 · DOI
- [10]Farrar G Molinuevo JL Zanette M Is there a difference in regional read [18 F] flutemetamol amyloid patterns between end-of-life subjects and those with amnestic mild cognitive impairment? Eur J Nucl Med Mol Imaging 2019 46 1299 1308 10.1007/s00259-019-04282-y 30863934 PMC6486895 · DOI
- [11]Fantoni ER Chalkidou A O’Brien JT Farrar G Hammers A A systematic review and aggregated analysis on the impact of amyloid PET brain imaging on the diagnosis, diagnostic confidence, and management of patients being evaluated for Alzheimer’s disease J Alzheimers Dis 2018 63 783 796 10.3233/JAD-171093 29689725 PMC5929301 · DOI · Paper page
- [12]Barthel H Sabri O Clinical use and utility of amyloid imaging J Nucl Med 2017 58 1711 1717 10.2967/jnumed.116.185017 28818990 · DOI
- [14]Chincarini A Peira E Morbelli S Pardini M Bauckneht M Arbizu J Semi-quantification and grading of amyloid PET: a project of the European Alzheimer’s Disease Consortium (EADC) Neuroimage Clin 2019 23 101846 10.1016/j.nicl.2019.101846 31077984 PMC6514268 · DOI
- [15]Pontecorvo MJ Arora AK Devine M Lu M Galante N Siderowf A Quantitation of PET signal as an adjunct to visual interpretation of florbetapir imaging Eur J Nucl Med Mol Imaging 2017 44 825 837 10.1007/s00259-016-3601-4 28064343 · DOI
- [16]Fakhry-Darian D Patel NH Khan S Barwick T Svensson W Khan S Optimisation and usefulness of quantitative analysis of 18F-florbetapir PET Br J Radiol 2019 92 20181020 10.1259/bjr.20181020 31017465 PMC6732916 · DOI
- [17]Thurfjell L Lilja J Lundqvist R Buckley C Smith A Vandenberghe R Automated quantification of 18F-flutemetamol PET activity for categorizing scans as negative or positive for brain amyloid: concordance with visual image reads J Nucl Med 2014 55 1623 1628 10.2967/jnumed.114.142109 25146124 · DOI
- [18]Leuzy A Savitcheva I Chiotis K Lilja J Andersen P Bogdanovic N Clinical impact of [18 F] flutemetamol PET among memory clinic patients with an unclear diagnosis Eur J Nucl Med Mol Imaging 2019 46 1276 1286 10.1007/s00259-019-04297-5 30915522 PMC6486908 · DOI
- [19]Sur C, Kost J, Scott D, Adamczuk K, Fox NC, Cummings JL, et al. BACE inhibition causes rapid, regional, and non-progressive volume reduction in Alzheimer’s disease brain. Brain. 2020. 10.1093/brain/awaa332. PMC8453290 33253354 · DOI
- [20]Hanseeuw BJ, Malotaux V, Dricot L, Quenon L, Sznajer Y, Cerman J, et al. Defining a Centiloid scale threshold predicting long-term progression to dementia in patients attending the memory clinic: an [Eur J Nucl Med Mol Imaging. 2020. doi:10.1007/s00259-020-04942-4. PMC7835306 32601802 · DOI
- [21]Zwan MD Bouwman FH Konijnenberg E Van Der Flier WM Lammertsma AA Verhey FR Diagnostic impact of [18 F] flutemetamol PET in early-onset dementia Alzheimers Res Ther 2017 9 2 10.1186/s13195-016-0228-4 28093088 PMC5240413 · DOI · Paper page
- [22]Salvadó G Molinuevo JL Brugulat-Serrat A Falcon C Grau-Rivera O Suárez-Calvet M Centiloid cut-off values for optimal agreement between PET and CSF core AD biomarkers Alzheimers Res Ther 2019 11 27 10.1186/s13195-019-0478-z 30902090 PMC6429814 · DOI · Paper page
- [24]Whittington A Gunn RN Initiative AsDN. amyloid load: a more sensitive biomarker for amyloid imaging J Nucl Med 2019 60 536 540 10.2967/jnumed.118.210518 30190305 PMC6448463 · DOI
- [25]van der Kall LM, Truong T, Burnham SC, Doré V, Mulligan RS, Bozinovski S, et al. Association of β-amyloid level, clinical progression and longitudinal cognitive change in normal older individuals. Neurology. 2020. 10.1212/WNL.0000000000011222. PMC7884996 33184233 · DOI
- [26]Sur C, Klein G, Mukai Y, Mo Y, Voss T, Zhang Y, et al. Baseline amyloid PET imaging characteristics in Verubecestat (MK-8931) prodromal trial. Conference Abstract Human Amyloid Imaging. Florida, Miami; 2016.
- [27]Molinuevo JL Gramunt N Gispert JD Fauria K Esteller M Minguillon C The ALFA project: a research platform to identify early pathophysiological features of Alzheimer’s disease Alzheimers Dement (N Y) 2016 2 82 92 10.1016/j.trci.2016.02.003 29067295 PMC5644283 · DOI
- [28]Ellis KA Bush AI Darby D De Fazio D Foster J Hudson P The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging: methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of Alzheimer’s disease Int Psychogeriatr 2009 21 672 687 10.1017/S1041610209009405 19470201 · DOI
- [29]Bourgeat P Villemagne VL Dore V Brown B Macaulay SL Martins R Comparison of MR-less PiB SUVR quantification methods Neurobiol Aging 2015 36 S159 SS66 10.1016/j.neurobiolaging.2014.04.033 25257985 · DOI
- [36]Donoghoe MW Gebski V The importance of censoring in competing risks analysis of the subdistribution hazard BMC Med Res Methodol 2017 17 52 10.1186/s12874-017-0327-3 28376736 PMC5379776 · DOI
- [37]Peira E, Grazzini M, Bauckneht M, Sensi F, Bosco P, Arnaldi D, et al. Probing the role of a regional quantitative assessment of amyloid PET. J Alzheimers Dis. 2021. 10.3233/JAD-201156. 33554908 · DOI
- [38]Sperling R Mormino E Johnson K The evolution of preclinical Alzheimer’s disease: implications for prevention trials Neuron. 2014 84 608 622 10.1016/j.neuron.2014.10.038 25442939 PMC4285623 · DOI · Paper page
- [39]Chincarini A Sensi F Rei L Bossert I Morbelli S Guerra UP Standardized uptake value ratio-independent evaluation of brain amyloidosis J Alzheimers Dis 2016 54 1437 1457 10.3233/JAD-160232 27662288 · DOI
- [40]Schmidt ME Chiao P Klein G Matthews D Thurfjell L Cole PE The influence of biological and technical factors on quantitative analysis of amyloid PET: points to consider and recommendations for controlling variability in longitudinal data Alzheimers Dement 2015 11 1050 1068 10.1016/j.jalz.2014.09.004 25457431 · DOI
- [41]Klunk WE Price JC Mathis CA Tsopelas ND Lopresti BJ Ziolko SK Amyloid deposition begins in the striatum of presenilin-1 mutation carriers from two unrelated pedigrees J Neurosci 2007 27 6174 6184 10.1523/JNEUROSCI.0730-07.2007 17553989 PMC3265970 · DOI · Paper page
- [42]Habert MO Bertin H Labit M Diallo M Marie S Martineau K Evaluation of amyloid status in a cohort of elderly individuals with memory complaints: validation of the method of quantification and determination of positivity thresholds Ann Nucl Med 2018 32 75 86 10.1007/s12149-017-1221-0 29218458 · DOI
- [43]Klunk WE Koeppe RA Price JC Benzinger TL Devous MD Jagust WJ The Centiloid Project: standardizing quantitative amyloid plaque estimation by PET Alzheimers Dement 2015 11 1–15.e1 1–15.e4 10.1016/j.jalz.2014.07.003 25443857 PMC4300247 · DOI · Paper page
- [45]Vandenberghe R Nelissen N Salmon E Ivanoiu A Hasselbalch S Andersen A Binary classification of 18F-flutemetamol PET using machine learning: comparison with visual reads and structural MRI Neuroimage. 2013 64 517 525 10.1016/j.neuroimage.2012.09.015 22982358 · DOI
- [46]Kim JP Kim J Kim Y Moon SH Park YH Yoo S Staging and quantification of florbetaben PET images using machine learning: impact of predicted regional cortical tracer uptake and amyloid stage on clinical outcomes Eur J Nucl Med Mol Imaging 2020 47 1971 1983 10.1007/s00259-019-04663-3 31884562 PMC7299909 · DOI
- [47]Leal SL Lockhart SN Maass A Bell RK Jagust WJ Subthreshold amyloid predicts tau deposition in aging J Neurosci 2018 38 4482 4489 10.1523/JNEUROSCI.0485-18.2018 29686045 PMC5943976 · DOI
- [48]Wolk DA Sadowsky C Safirstein B Rinne JO Duara R Perry R Use of flutemetamol F 18-labeled positron emission tomography and other biomarkers to assess risk of clinical progression in patients with amnestic mild cognitive impairment JAMA Neurol 2018 75 1114 1123 10.1001/jamaneurol.2018.0894 29799984 PMC6143120 · DOI
- Authors
- Bucci, M., Savitcheva, I., Farrar, G., Salvadó, G., Collij, L., Doré, V., Gispert, J. D., Gunn, R., Hanseeuw, B., Hansson, O., Shekari, M., Lhommel, R., Molinuevo, J. L., Rowe, C., Sur, C., Whittington, A., Buckley, C., & Nordberg, A.
- Affiliations
- Karolinska Institutet (SE)
- Karolinska University Hospital (SE)
- General Electric (United Kingdom) (GB)
- Pasqual Maragall Foundation (ES)
- Amsterdam University Medical Centers (NL)
- Commonwealth Scientific and Industrial Research Organisation (AU)
- Universitat Pompeu Fabra (ES)
- Invicro (United Kingdom) (GB)
- Cliniques Universitaires Saint-Luc (BE)
- Lund University (SE)
- The University of Melbourne (AU)
- Merck & Co., Inc., Rahway, NJ, USA (United States) (US)
- Corresponding author
- Bucci, Marco
- Nordberg, Agneta
- ORCID
- Venue
- European Journal of Nuclear Medicine and Molecular Imaging
- Published
- 2021-04-12
- Volume
- 48
- Issue
- 7
- Pages
- 2183-2199
- ISSN
- 1619-7070 (ISSN-L)
- 1619-7089
- PMC
- PMC8175298
- OpenAlex
- W3154120739
- Cited by
- 50
- References
- 38
- Citations by year
- 2026: 9 · 2025: 13 · 2024: 9 · 2023: 10 · 2022: 8 · 2021: 1
- FWCI
- 5.02
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- European Journal of Nuclear Medicine and Molecular Imaging (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Repositori digital de la UPF (Universitat Pompeu Fabra) (publishedVersion, cc-by)
- Minerva Access (University of Melbourne) (submittedVersion, other-oa)
- Digital Access to Libraries (submittedVersion, other-oa)
- Lund University Publications (Lund University) (submittedVersion)
- Funders
- Stiftelsen för Strategisk Forskning (RB13-0192)
- Center for Innovative Medicine, Karolinska institutet
- Stockholms Läns Landsting (SE) KI
- Hjärnfonden
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Keywords
- 18F-flutemetamol, amyloid PET, concordance, Alzheimer's disease progression, standardized uptake value ratio, memory clinic patients, competing risk regression
- MeSH terms
- Alzheimer Disease, Amyloid, Aniline Compounds, Brain, Humans, Amyloid beta-Peptides, Positron-Emission Tomography, Benzothiazoles
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