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Cotta Ramusino M, Massa F, Festari C, Gandolfo F, Nicolosi V, Orini S, Nobili F, Frisoni GB, Morbelli S, Garibotto V. Diagnostic performance of molecular imaging methods in predicting the progression from mild cognitive impairment to dementia: an updated systematic review. Eur J Nucl Med Mol Imaging 2024; 51:1876-1890. [PMID: 38355740 DOI: 10.1007/s00259-024-06631-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 01/27/2024] [Indexed: 02/16/2024]
Abstract
PURPOSE Epidemiological and logistical reasons are slowing the clinical validation of the molecular imaging biomarkers in the initial stages of neurocognitive disorders. We provide an updated systematic review of the recent advances (2017-2022), highlighting methodological shortcomings. METHODS Studies reporting the diagnostic accuracy values of the molecular imaging techniques (i.e., amyloid-, tau-, [18F]FDG-PETs, DaT-SPECT, and cardiac [123I]-MIBG scintigraphy) in predicting progression from mild cognitive impairment (MCI) to dementia were selected according to the Preferred Reporting Items for a Systematic Review and Meta-Analysis (PRISMA) method and evaluated with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Main eligibility criteria were as follows: (1) ≥ 50 subjects with MCI, (2) follow-up ≥ 3 years, (3) gold standard: progression to dementia or diagnosis on pathology, and (4) measures of prospective accuracy. RESULTS Sensitivity (SE) and specificity (SP) in predicting progression to dementia, mainly to Alzheimer's dementia were 43-100% and 63-94% for [18F]FDG-PET and 64-94% and 48-93% for amyloid-PET. Longitudinal studies were lacking for less common disorders (Dementia with Lewy bodies-DLB and Frontotemporal lobe degeneration-FTLD) and for tau-PET, DaT-SPECT, and [123I]-MIBG scintigraphy. Therefore, the accuracy values from cross-sectional studies in a smaller sample of subjects (n > 20, also including mild dementia stage) were chosen as surrogate outcomes. DaT-SPECT showed 47-100% SE and 71-100% SP in differentiating Lewy body disease (LBD) from non-LBD conditions; tau-PET: 88% SE and 100% SP in differentiating DLB from Posterior Cortical Atrophy. [123I]-MIBG scintigraphy differentiated LBD from non-LBD conditions with 47-100% SE and 71-100% SP. CONCLUSION Molecular imaging has a moderate-to-good accuracy in predicting the progression of MCI to Alzheimer's dementia. Longitudinal studies are sparse in non-AD conditions, requiring additional efforts in these settings.
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Affiliation(s)
- Matteo Cotta Ramusino
- Unit of Behavior Neurology and Dementia Research Center, IRCCS Mondino Foundation, via Mondino 2, 27100, Pavia, Italy.
| | - Federico Massa
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Cristina Festari
- Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni Di Dio Fatebenefratelli, Brescia, Italy
| | - Federica Gandolfo
- Department of Geriatric Care, Orthogeriatrics and Rehabilitation, E.O. Galliera Hospital, Genoa, Italy
| | - Valentina Nicolosi
- UOC Neurologia Ospedale Magalini Di Villafranca Di Verona (VR) ULSS 9, Verona, Italy
| | - Stefania Orini
- Alzheimer's Unit-Memory Clinic, IRCCS Istituto Centro San Giovanni Di Dio Fatebenefratelli, Brescia, Italy
- Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Flavio Nobili
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Giovanni B Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Department of Rehabilitation and Geriatrics, Geneva University and University Hospitals, Geneva, Switzerland
| | - Silvia Morbelli
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Valentina Garibotto
- Division of Nuclear Medicine and Molecular Imaging, Diagnostic Department, University Hospitals of Geneva, Geneva, Switzerland
- NIMTLab, Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- CIBM Center for Biomedical Imaging, Geneva, Switzerland
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Rhodius-Meester HFM, van Maurik IS, Collij LE, van Gils AM, Koikkalainen J, Tolonen A, Pijnenburg YAL, Berkhof J, Barkhof F, van de Giessen E, Lötjönen J, van der Flier WM. Computerized decision support is an effective approach to select memory clinic patients for amyloid-PET. PLoS One 2024; 19:e0303111. [PMID: 38768188 PMCID: PMC11104589 DOI: 10.1371/journal.pone.0303111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 04/18/2024] [Indexed: 05/22/2024] Open
Abstract
BACKGROUND The use of amyloid-PET in dementia workup is upcoming. At the same time, amyloid-PET is costly and limitedly available. While the appropriate use criteria (AUC) aim for optimal use of amyloid-PET, their limited sensitivity hinders the translation to clinical practice. Therefore, there is a need for tools that guide selection of patients for whom amyloid-PET has the most clinical utility. We aimed to develop a computerized decision support approach to select patients for amyloid-PET. METHODS We included 286 subjects (135 controls, 108 Alzheimer's disease dementia, 33 frontotemporal lobe dementia, and 10 vascular dementia) from the Amsterdam Dementia Cohort, with available neuropsychology, APOE, MRI and [18F]florbetaben amyloid-PET. In our computerized decision support approach, using supervised machine learning based on the DSI classifier, we first classified the subjects using only neuropsychology, APOE, and quantified MRI. Then, for subjects with uncertain classification (probability of correct class (PCC) < 0.75) we enriched classification by adding (hypothetical) amyloid positive (AD-like) and negative (normal) PET visual read results and assessed whether the diagnosis became more certain in at least one scenario (PPC≥0.75). If this was the case, the actual visual read result was used in the final classification. We compared the proportion of PET scans and patients diagnosed with sufficient certainty in the computerized approach with three scenarios: 1) without amyloid-PET, 2) amyloid-PET according to the AUC, and 3) amyloid-PET for all patients. RESULTS The computerized approach advised PET in n = 60(21%) patients, leading to a diagnosis with sufficient certainty in n = 188(66%) patients. This approach was more efficient than the other three scenarios: 1) without amyloid-PET, diagnostic classification was obtained in n = 155(54%), 2) applying the AUC resulted in amyloid-PET in n = 113(40%) and diagnostic classification in n = 156(55%), and 3) performing amyloid-PET in all resulted in diagnostic classification in n = 154(54%). CONCLUSION Our computerized data-driven approach selected 21% of memory clinic patients for amyloid-PET, without compromising diagnostic performance. Our work contributes to a cost-effective implementation and could support clinicians in making a balanced decision in ordering additional amyloid PET during the dementia workup.
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Affiliation(s)
- Hanneke F. M. Rhodius-Meester
- Alzheimer Center Amsterdam, Neurology, Amsterdam UMC Location VUmc, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Department of Internal Medicine, Geriatric Medicine Section, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Geriatric Medicine, The Memory Clinic, Oslo University Hospital, Oslo, Norway
| | - Ingrid S. van Maurik
- Alzheimer Center Amsterdam, Neurology, Amsterdam UMC Location VUmc, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Epidemiology and Data Science, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health, Methodology, Amsterdam, The Netherlands
| | - Lyduine E. Collij
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Aniek M. van Gils
- Alzheimer Center Amsterdam, Neurology, Amsterdam UMC Location VUmc, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | | | | | - Yolande A. L. Pijnenburg
- Alzheimer Center Amsterdam, Neurology, Amsterdam UMC Location VUmc, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Johannes Berkhof
- Epidemiology and Data Science, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health, Methodology, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Elsmarieke van de Giessen
- Alzheimer Center Amsterdam, Neurology, Amsterdam UMC Location VUmc, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | | | - Wiesje M. van der Flier
- Alzheimer Center Amsterdam, Neurology, Amsterdam UMC Location VUmc, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Epidemiology and Data Science, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health, Methodology, Amsterdam, The Netherlands
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Juengling F, Wuest F, Schirrmacher R, Abele J, Thiel A, Soucy JP, Camicioli R, Garibotto V. PET Imaging in Dementia: Mini-Review and Canadian Perspective for Clinical Use. Can J Neurol Sci 2024:1-13. [PMID: 38433571 DOI: 10.1017/cjn.2024.31] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2024]
Abstract
PET imaging is increasingly recognized as an important diagnostic tool to investigate patients with cognitive disturbances of possible neurodegenerative origin. PET with 2-[18F]fluoro-2-deoxy-D-glucose ([18F]FDG), assessing glucose metabolism, provides a measure of neurodegeneration and allows a precise differential diagnosis among the most common neurodegenerative diseases, such as Alzheimer's disease, frontotemporal dementia or dementia with Lewy bodies. PET tracers specific for the pathological deposits characteristic of different neurodegenerative processes, namely amyloid and tau deposits typical of Alzheimer's Disease, allow the visualization of these aggregates in vivo. [18F]FDG and amyloid PET imaging have reached a high level of clinical validity and are since 2022 investigations that can be offered to patients in standard clinical care in most of Canada.This article will briefly review and summarize the current knowledge on these diagnostic tools, their integration into diagnostic algorithms as well as perspectives for future developments.
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Affiliation(s)
- Freimut Juengling
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada
- Division of Oncologic Imaging and Radionuclide Therapy, Cross Cancer Institute, Edmonton, AB, Canada
- Medical Faculty, University of Bern, Bern, Switzerland
| | - Frank Wuest
- Division of Oncologic Imaging and Radionuclide Therapy, Cross Cancer Institute, Edmonton, AB, Canada
| | - Ralf Schirrmacher
- Division of Oncologic Imaging and Radionuclide Therapy, Cross Cancer Institute, Edmonton, AB, Canada
- Medical Isotope and Cyclotron Facility, University of Alberta, Edmonton, AB, Canada
| | - Jonathan Abele
- Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, AB, Canada
| | - Alexander Thiel
- Department of Neurology and Neurosurgery, Lady Davis Institute for Medical Research, McGill University, Montréal, QC, Canada
| | - Jean-Paul Soucy
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Richard Camicioli
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada
- Department of Medicine, Division of Neurology, University of Alberta, Edmonton, AB, Canada
| | - Valentina Garibotto
- Diagnostic Department, Nuclear Medicine and Molecular Imaging Division, University Hospitals of Geneva, Geneva, Switzerland
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Stricker NH, Stricker JL, Frank RD, Fan WZ, Christianson TJ, Patel JS, Karstens AJ, Kremers WK, Machulda MM, Fields JA, Graff-Radford J, Jack CR, Knopman DS, Mielke MM, Petersen RC. Stricker Learning Span criterion validity: a remote self-administered multi-device compatible digital word list memory measure shows similar ability to differentiate amyloid and tau PET-defined biomarker groups as in-person Auditory Verbal Learning Test. J Int Neuropsychol Soc 2024; 30:138-151. [PMID: 37385974 PMCID: PMC10756923 DOI: 10.1017/s1355617723000322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/01/2023]
Abstract
OBJECTIVE The Stricker Learning Span (SLS) is a computer-adaptive digital word list memory test specifically designed for remote assessment and self-administration on a web-based multi-device platform (Mayo Test Drive). We aimed to establish criterion validity of the SLS by comparing its ability to differentiate biomarker-defined groups to the person-administered Rey's Auditory Verbal Learning Test (AVLT). METHOD Participants (N = 353; mean age = 71, SD = 11; 93% cognitively unimpaired [CU]) completed the AVLT during an in-person visit, the SLS remotely (within 3 months) and had brain amyloid and tau PET scans available (within 3 years). Overlapping groups were formed for 1) those on the Alzheimer's disease (AD) continuum (amyloid PET positive, A+, n = 125) or not (A-, n = 228), and those with biological AD (amyloid and tau PET positive, A+T+, n = 55) vs no evidence of AD pathology (A-T-, n = 195). Analyses were repeated among CU participants only. RESULTS The SLS and AVLT showed similar ability to differentiate biomarker-defined groups when comparing AUROCs (p's > .05). In logistic regression models, SLS contributed significantly to predicting biomarker group beyond age, education, and sex, including when limited to CU participants. Medium (A- vs A+) to large (A-T- vs A+T+) unadjusted effect sizes were observed for both SLS and AVLT. Learning and delay variables were similar in terms of ability to separate biomarker groups. CONCLUSIONS Remotely administered SLS performed similarly to in-person-administered AVLT in its ability to separate biomarker-defined groups, providing evidence of criterion validity. Results suggest the SLS may be sensitive to detecting subtle objective cognitive decline in preclinical AD.
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Affiliation(s)
- Nikki H. Stricker
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - John L. Stricker
- Department of Information Technology, Mayo Clinic, Rochester, MN, USA
| | - Ryan D. Frank
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Winnie Z. Fan
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | | | - Jay S. Patel
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Aimee J. Karstens
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Walter K. Kremers
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Mary M. Machulda
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Julie A. Fields
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | | | | | | | - Michelle M. Mielke
- Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, NC, USA
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van Maurik IS, Bakker ED, van Unnik AAJM, Broulikova HM, Zwan MD, van de Giessen E, Berkhof J, Bouwman FH, Bosmans JE, van der Flier WM. How healthy participants value additional diagnostic testing with amyloid-PET in patients diagnosed with mild cognitive impairment - a bidding game experiment. Alzheimers Res Ther 2023; 15:208. [PMID: 38017549 PMCID: PMC10683285 DOI: 10.1186/s13195-023-01346-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 10/25/2023] [Indexed: 11/30/2023]
Abstract
BACKGROUND To estimate the perceived value of additional testing with amyloid-PET in Euros in healthy participants acting as analogue patients with mild cognitive impairment (MCI). METHODS One thousand four hundred thirty-one healthy participants acting as analogue MCI patients (mean age 65 ± 8, 929 (75%) female) were recruited via the Dutch Brain Research Registry. Participants were asked to identify with a presented case (video vignette) of an MCI patient and asked whether they would prefer additional diagnostic testing with amyloid PET in this situation. If yes, respondents were asked how much they would be willing to pay for additional diagnostic testing. Monetary value was elicited via a bidding game in which participants were randomized over three conditions: (A) additional testing results in better patient management, (B) Same as condition A and a delay in institutionalization of 3 months, and (C) same as A and a delay in institutionalization of 6 months. Participants who were not willing to take a test were compared with participants who were willing to take a test using logit models. The highest monetary value per condition was analyzed using random-parameter mixed models. RESULTS The vast majority of participants acting as analogue MCI patients (87% (n = 1238)) preferred additional testing with amyloid PET. Participants who were not interested were more often female (OR = 1.61 95% CI [1.09-2.40]) and expressed fewer worries to get AD (OR = 0.64 [0.47-0.87]). The median "a priori" (i.e., before randomization) monetary value of additional diagnostic testing was €1500 (IQR 500-1500). If an additional amyloid PET resulted in better patient management (not further specified; condition A), participants were willing to pay a median price of €2000 (IQR = 1000-3500). Participants were willing to pay significantly more than condition A (better patient management) if amyloid-PET testing additionally resulted in a delay in institutionalization of 3 months (€530 [255-805] on top of €2000, condition B) or 6 months (€596 [187-1005] on top of €2000, condition C). CONCLUSIONS Members of the general population acting as MCI patients are willing to pay a substantial amount of money for amyloid-PET and this increases when diagnostic testing leads to better patient management and the prospect to live longer at home.
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Affiliation(s)
- I S van Maurik
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, De Boelelaan 1118, Amsterdam, The Netherlands.
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands.
- Epidemiology and Data Science, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands.
- Amsterdam Public Health, Methodology, Amsterdam, The Netherlands.
| | - E D Bakker
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, De Boelelaan 1118, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - A A J M van Unnik
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, De Boelelaan 1118, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - H M Broulikova
- Department of Health Sciences, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - M D Zwan
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, De Boelelaan 1118, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - E van de Giessen
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, De Boelelaan 1118, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, the Netherlands
| | - J Berkhof
- Epidemiology and Data Science, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
- Amsterdam Public Health, Methodology, Amsterdam, The Netherlands
| | - F H Bouwman
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, De Boelelaan 1118, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - J E Bosmans
- Department of Health Sciences, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - W M van der Flier
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, De Boelelaan 1118, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Epidemiology and Data Science, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
- Amsterdam Public Health, Methodology, Amsterdam, The Netherlands
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Shekari M, Verwer EE, Yaqub M, Daamen M, Buckley C, Frisoni GB, Visser PJ, Farrar G, Barkhof F, Gispert JD, Boellaard R. Harmonization of brain PET images in multi-center PET studies using Hoffman phantom scan. EJNMMI Phys 2023; 10:68. [PMID: 37906338 PMCID: PMC10618151 DOI: 10.1186/s40658-023-00588-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 10/12/2023] [Indexed: 11/02/2023] Open
Abstract
BACKGROUND Image harmonization has been proposed to minimize heterogeneity in brain PET scans acquired in multi-center studies. However, standard validated methods and software tools are lacking. Here, we assessed the performance of a framework for the harmonization of brain PET scans in a multi-center European clinical trial. METHOD Hoffman 3D brain phantoms were acquired in 28 PET systems and reconstructed using site-specific settings. Full Width at Half Maximum (FWHM) of the Effective Image Resolution (EIR) and harmonization kernels were estimated for each scan. The target EIR was selected as the coarsest EIR in the imaging network. Using "Hoffman 3D brain Analysis tool," indicators of image quality were calculated before and after the harmonization: The Coefficient of Variance (COV%), Gray Matter Recovery Coefficient (GMRC), Contrast, Cold-Spot RC, and left-to-right GMRC ratio. A COV% ≤ 15% and Contrast ≥ 2.2 were set as acceptance criteria. The procedure was repeated to achieve a 6-mm target EIR in a subset of scans. The method's robustness against typical dose-calibrator-based errors was assessed. RESULTS The EIR across systems ranged from 3.3 to 8.1 mm, and an EIR of 8 mm was selected as the target resolution. After harmonization, all scans met acceptable image quality criteria, while only 13 (39.4%) did before. The harmonization procedure resulted in lower inter-system variability indicators: Mean ± SD COV% (from 16.97 ± 6.03 to 7.86 ± 1.47%), GMRC Inter-Quartile Range (0.040-0.012), and Contrast SD (0.14-0.05). Similar results were obtained with a 6-mm FWHM target EIR. Errors of ± 10% in the DRO activity resulted in differences below 1 mm in the estimated EIR. CONCLUSION Harmonizing the EIR of brain PET scans significantly reduced image quality variability while minimally affecting quantitative accuracy. This method can be used prospectively for harmonizing scans to target sharper resolutions and is robust against dose-calibrator errors. Comparable image quality is attainable in brain PET multi-center studies while maintaining quantitative accuracy.
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Affiliation(s)
- Mahnaz Shekari
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Eline E Verwer
- Department of Radiology and Nuclear Medicine, Amsterdam, University Medical Centers, Location VUmc, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Maqsood Yaqub
- Department of Radiology and Nuclear Medicine, Amsterdam, University Medical Centers, Location VUmc, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Marcel Daamen
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | | | - Giovanni B Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Memory Center, Department of Rehabilitation and Geriatrics, University Hospitals and University of Geneva, Geneva, Switzerland
| | - Pieter Jelle Visser
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Alzheimer Center Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
- Department of Neurobiology, Care Sciences and Society, Division of Neurogeriatrics, Karolinska Institutet, Stockholm, Sweden
| | | | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam, University Medical Centers, Location VUmc, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
- Queen Square Institute of Neurology, University College London, London, UK
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain.
- Universitat Pompeu Fabra, Barcelona, Spain.
- Centro de Investigación Biomédica en Red Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Barcelona, Spain.
| | - Ronald Boellaard
- Department of Radiology and Nuclear Medicine, Amsterdam, University Medical Centers, Location VUmc, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.
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Pemberton HG, Buckley C, Battle M, Bollack A, Patel V, Tomova P, Cooke D, Balhorn W, Hegedorn K, Lilja J, Brand C, Farrar G. Software compatibility analysis for quantitative measures of [ 18F]flutemetamol amyloid PET burden in mild cognitive impairment. EJNMMI Res 2023; 13:48. [PMID: 37225974 DOI: 10.1186/s13550-023-00994-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 05/05/2023] [Indexed: 05/26/2023] Open
Abstract
RATIONALE Amyloid-β (Aβ) pathology is one of the earliest detectable brain changes in Alzheimer's disease pathogenesis. In clinical practice, trained readers will visually categorise positron emission tomography (PET) scans as either Aβ positive or negative. However, adjunct quantitative analysis is becoming more widely available, where regulatory approved software can currently generate metrics such as standardised uptake value ratios (SUVr) and individual Z-scores. Therefore, it is of direct value to the imaging community to assess the compatibility of commercially available software packages. In this collaborative project, the compatibility of amyloid PET quantification was investigated across four regulatory approved software packages. In doing so, the intention is to increase visibility and understanding of clinically relevant quantitative methods. METHODS Composite SUVr using the pons as the reference region was generated from [18F]flutemetamol (GE Healthcare) PET in a retrospective cohort of 80 amnestic mild cognitive impairment (aMCI) patients (40 each male/female; mean age = 73 years, SD = 8.52). Based on previous autopsy validation work, an Aβ positivity threshold of ≥ 0.6 SUVrpons was applied. Quantitative results from MIM Software's MIMneuro, Syntermed's NeuroQ, Hermes Medical Solutions' BRASS and GE Healthcare's CortexID were analysed using intraclass correlation coefficient (ICC), percentage agreement around the Aβ positivity threshold and kappa scores. RESULTS Using an Aβ positivity threshold of ≥ 0.6 SUVrpons, 95% agreement was achieved across the four software packages. Two patients were narrowly classed as Aβ negative by one software package but positive by the others, and two patients vice versa. All kappa scores around the same Aβ positivity threshold, both combined (Fleiss') and individual software pairings (Cohen's), were ≥ 0.9 signifying "almost perfect" inter-rater reliability. Excellent reliability was found between composite SUVr measurements for all four software packages, with an average measure ICC of 0.97 and 95% confidence interval of 0.957-0.979. Correlation coefficient analysis between the two software packages reporting composite z-scores was strong (r2 = 0.98). CONCLUSION Using an optimised cortical mask, regulatory approved software packages provided highly correlated and reliable quantification of [18F]flutemetamol amyloid PET with a ≥ 0.6 SUVrpons positivity threshold. In particular, this work could be of interest to physicians performing routine clinical imaging rather than researchers performing more bespoke image analysis. Similar analysis is encouraged using other reference regions as well as the Centiloid scale, when it has been implemented by more software packages.
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Affiliation(s)
- Hugh G Pemberton
- GE Healthcare, Pollards Wood, Chalfont St Giles, Amersham, HP8 4SP, UK.
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK.
- UCL Queen Square Institute of Neurology, University College London, London, UK.
| | | | - Mark Battle
- GE Healthcare, Pollards Wood, Chalfont St Giles, Amersham, HP8 4SP, UK
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Ariane Bollack
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
| | - Vrajesh Patel
- GE Healthcare, Pollards Wood, Chalfont St Giles, Amersham, HP8 4SP, UK
| | - Petya Tomova
- GE Healthcare, Pollards Wood, Chalfont St Giles, Amersham, HP8 4SP, UK
| | | | | | | | | | - Christine Brand
- GE Healthcare, Pollards Wood, Chalfont St Giles, Amersham, HP8 4SP, UK
| | - Gill Farrar
- GE Healthcare, Pollards Wood, Chalfont St Giles, Amersham, HP8 4SP, UK
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Altomare D, Barkhof F, Caprioglio C, Collij LE, Scheltens P, Lopes Alves I, Bouwman F, Berkhof J, van Maurik IS, Garibotto V, Moro C, Delrieu J, Payoux P, Saint-Aubert L, Hitzel A, Molinuevo JL, Grau-Rivera O, Gispert JD, Drzezga A, Jessen F, Zeyen P, Nordberg A, Savitcheva I, Jelic V, Walker Z, Edison P, Demonet JF, Gismondi R, Farrar G, Stephens AW, Frisoni GB. Clinical Effect of Early vs Late Amyloid Positron Emission Tomography in Memory Clinic Patients: The AMYPAD-DPMS Randomized Clinical Trial. JAMA Neurol 2023:2804755. [PMID: 37155177 PMCID: PMC10167601 DOI: 10.1001/jamaneurol.2023.0997] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Importance Amyloid positron emission tomography (PET) allows the direct assessment of amyloid deposition, one of the main hallmarks of Alzheimer disease. However, this technique is currently not widely reimbursed because of the lack of appropriately designed studies demonstrating its clinical effect. Objective To assess the clinical effect of amyloid PET in memory clinic patients. Design, Setting, and Participants The AMYPAD-DPMS is a prospective randomized clinical trial in 8 European memory clinics. Participants were allocated (using a minimization method) to 3 study groups based on the performance of amyloid PET: arm 1, early in the diagnostic workup (within 1 month); arm 2, late in the diagnostic workup (after a mean [SD] 8 [2] months); or arm 3, if and when the managing physician chose. Participants were patients with subjective cognitive decline plus (SCD+; SCD plus clinical features increasing the likelihood of preclinical Alzheimer disease), mild cognitive impairment (MCI), or dementia; they were assessed at baseline and after 3 months. Recruitment took place between April 16, 2018, and October 30, 2020. Data analysis was performed from July 2022 to January 2023. Intervention Amyloid PET. Main Outcome and Measure The main outcome was the difference between arm 1 and arm 2 in the proportion of participants receiving an etiological diagnosis with a very high confidence (ie, ≥90% on a 50%-100% visual numeric scale) after 3 months. Results A total of 844 participants were screened, and 840 were enrolled (291 in arm 1, 271 in arm 2, 278 in arm 3). Baseline and 3-month visit data were available for 272 participants in arm 1 and 260 in arm 2 (median [IQR] age: 71 [65-77] and 71 [65-77] years; 150/272 male [55%] and 135/260 male [52%]; 122/272 female [45%] and 125/260 female [48%]; median [IQR] education: 12 [10-15] and 13 [10-16] years, respectively). After 3 months, 109 of 272 participants (40%) in arm 1 had a diagnosis with very high confidence vs 30 of 260 (11%) in arm 2 (P < .001). This was consistent across cognitive stages (SCD+: 25/84 [30%] vs 5/78 [6%]; P < .001; MCI: 45/108 [42%] vs 9/102 [9%]; P < .001; dementia: 39/80 [49%] vs 16/80 [20%]; P < .001). Conclusion and Relevance In this study, early amyloid PET allowed memory clinic patients to receive an etiological diagnosis with very high confidence after only 3 months compared with patients who had not undergone amyloid PET. These findings support the implementation of amyloid PET early in the diagnostic workup of memory clinic patients. Trial Registration EudraCT Number: 2017-002527-21.
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Affiliation(s)
- Daniele Altomare
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers (UMC)-Location VUmc, Amsterdam, the Netherlands
- Institute of Neurology, Institute of Healthcare Engineering, University College London, London, United Kingdom
| | - Camilla Caprioglio
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
| | - Lyduine E Collij
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers (UMC)-Location VUmc, Amsterdam, the Netherlands
| | - Philip Scheltens
- Alzheimer Center, Department of Neurology, Amsterdam University Medical Centers-Location VUmc, Amsterdam, the Netherlands
| | - Isadora Lopes Alves
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers (UMC)-Location VUmc, Amsterdam, the Netherlands
| | - Femke Bouwman
- Alzheimer Center, Department of Neurology, Amsterdam University Medical Centers-Location VUmc, Amsterdam, the Netherlands
| | - Johannes Berkhof
- Department of Epidemiology and Data Science, Amsterdam University Medical Centers-Location VUmc, Amsterdam, the Netherlands
| | - Ingrid S van Maurik
- Alzheimer Center, Department of Neurology, Amsterdam University Medical Centers-Location VUmc, Amsterdam, the Netherlands
- Department of Epidemiology and Data Science, Amsterdam University Medical Centers-Location VUmc, Amsterdam, the Netherlands
| | - Valentina Garibotto
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Geneva University Neurocenter and Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland
| | - Christian Moro
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
| | - Julien Delrieu
- Gérontopôle, Department of Geriatrics, Toulouse University Hospital, Toulouse, France
- Maintain Aging Research Team, CERPOP, Inserm, Université Paul Sabatier, Toulouse, France
| | - Pierre Payoux
- Department of Nuclear Medicine, Toulouse University Hospital, Toulouse, France
- Toulouse NeuroImaging Center (ToNIC), UMR1214 Inserm, Université de Toulouse III, Toulouse, France
| | - Laure Saint-Aubert
- Department of Nuclear Medicine, Toulouse University Hospital, Toulouse, France
- Toulouse NeuroImaging Center (ToNIC), UMR1214 Inserm, Université de Toulouse III, Toulouse, France
| | - Anne Hitzel
- Department of Nuclear Medicine, Toulouse University Hospital, Toulouse, France
| | - José Luis Molinuevo
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- H. Lundbeck, Copenhagen, Denmark
| | - Oriol Grau-Rivera
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
- Centro de Investigación Biomédica en Red Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Barcelona, Spain
| | - Alexander Drzezga
- Department of Nuclear Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- German Center for Neurodegenerative Diseases (DZNE), Bonn-Cologne, Germany
- Institute of Neuroscience and Medicine (INM-2), Molecular Organization of the Brain, Forschungszentrum Jülich, Germany
| | - Frank Jessen
- German Center for Neurodegenerative Diseases (DZNE), Bonn-Cologne, Germany
- Department of Psychiatry, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Excellence Cluster Cellular Stress Responses in Aging-Related Diseases (CECAD), Medical Faculty, University of Cologne, Cologne, Germany
| | - Philip Zeyen
- Department of Psychiatry, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Agneta Nordberg
- Department of Neurobiology, Care Sciences and Society, Center of Alzheimer Research, Karolinska Institutet, Stockholm, Sweden
- Theme Aging, Karolinska University Hospital, Stockholm, Sweden
| | - Irina Savitcheva
- Medical Radiation Physics and Nuclear Medicine, Section for Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Vesna Jelic
- Cognitive Disorders Clinic, Theme Inflammation and Aging, Karolinska University Hospital-Huddinge, Stockholm, Sweden
| | - Zuzana Walker
- Division of Psychiatry, University College London, London, United Kingdom
- St Margaret's Hospital, Essex Partnership University NHS Foundation Trust, Essex, United Kingdom
| | - Paul Edison
- Division of Neurology, Department of Brain Sciences, Imperial College London, London, United Kingdom
| | | | | | | | | | - Giovanni B Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
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9
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van Maurik IS, Broulikova HM, Mank A, Bakker ED, de Wilde A, Bouwman FH, Stephens AW, van Berckel BNM, Scheltens P, van der Flier WM. A more precise diagnosis by means of amyloid PET contributes to delayed institutionalization, lower mortality, and reduced care costs in a tertiary memory clinic setting. Alzheimers Dement 2022; 19:2006-2013. [PMID: 36419238 DOI: 10.1002/alz.12846] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 10/03/2022] [Accepted: 10/05/2022] [Indexed: 11/25/2022]
Abstract
INTRODUCTION We aim to study the effect of a more precise diagnosis, by means of amyloid positron emission tomography (PET), on institutionalization, mortality, and health-care costs. METHODS Between October 27, 2014 and December 31, 2016, we offered amyloid PET to all patients as part of their diagnostic work-up. Patients who accepted to undergo amyloid PET (n = 449) were propensity score matched with patients without amyloid PET (n = 571, i.e., no PET). Matched groups (both n = 444) were compared on rate of institutionalization, mortality, and health-care costs in the years after diagnosis. RESULTS Amyloid PET patients had a lower risk of institutionalization (10% [n = 45] vs. 21% [n = 92]; hazard ratio [HR] = 0.48 [0.33-0.70]) and mortality rate (11% [n = 49] vs. 18% [n = 81]; HR = 0.51 [0.36-0.73]) and lower health-care costs in the years after diagnosis compared to matched no-PET patients (β = -4573.49 [-6524.76 to -2523.74], P-value < 0.001). DISCUSSION A more precise diagnosis in tertiary memory clinic patients positively influenced the endpoints of institutionalization, death, and health-care costs.
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Affiliation(s)
- Ingrid S. van Maurik
- Alzheimer Center Amsterdam, Neurology Vrije Universiteit Amsterdam Amsterdam UMC location VUmc Amsterdam the Netherlands
- Amsterdam Neuroscience Neurodegeneration Amsterdam the Netherlands
- Amsterdam UMC location Vrije Universiteit Amsterdam Epidemiology and Data Science Amsterdam the Netherlands
- Amsterdam Public Health Methodology Amsterdam the Netherlands
| | - Hana M. Broulikova
- Department of Health Sciences Faculty of Science Vrije Universiteit Amsterdam, Amsterdam Public Health Research Institute Amsterdam the Netherlands
| | - Arenda Mank
- Alzheimer Center Amsterdam, Neurology Vrije Universiteit Amsterdam Amsterdam UMC location VUmc Amsterdam the Netherlands
- Amsterdam Neuroscience Neurodegeneration Amsterdam the Netherlands
- Amsterdam UMC location Vrije Universiteit Amsterdam Epidemiology and Data Science Amsterdam the Netherlands
- Amsterdam Public Health Methodology Amsterdam the Netherlands
| | - Els D. Bakker
- Alzheimer Center Amsterdam, Neurology Vrije Universiteit Amsterdam Amsterdam UMC location VUmc Amsterdam the Netherlands
- Amsterdam Neuroscience Neurodegeneration Amsterdam the Netherlands
| | - Arno de Wilde
- Alzheimer Center Amsterdam, Neurology Vrije Universiteit Amsterdam Amsterdam UMC location VUmc Amsterdam the Netherlands
- Amsterdam Neuroscience Neurodegeneration Amsterdam the Netherlands
- EQT Life Sciences Amsterdam the Netherlands
| | - Femke H. Bouwman
- Alzheimer Center Amsterdam, Neurology Vrije Universiteit Amsterdam Amsterdam UMC location VUmc Amsterdam the Netherlands
- Amsterdam Neuroscience Neurodegeneration Amsterdam the Netherlands
| | | | - Bart N. M. van Berckel
- Department of Radiology and Nuclear Medicine Vrije Universiteit Amsterdam, Amsterdam UMC Amsterdam the Netherlands
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Neurology Vrije Universiteit Amsterdam Amsterdam UMC location VUmc Amsterdam the Netherlands
- Amsterdam Neuroscience Neurodegeneration Amsterdam the Netherlands
- EQT Life Sciences Amsterdam the Netherlands
| | - Wiesje M. van der Flier
- Alzheimer Center Amsterdam, Neurology Vrije Universiteit Amsterdam Amsterdam UMC location VUmc Amsterdam the Netherlands
- Amsterdam Neuroscience Neurodegeneration Amsterdam the Netherlands
- Amsterdam UMC location Vrije Universiteit Amsterdam Epidemiology and Data Science Amsterdam the Netherlands
- Amsterdam Public Health Methodology Amsterdam the Netherlands
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10
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Hampel H, Au R, Mattke S, van der Flier WM, Aisen P, Apostolova L, Chen C, Cho M, De Santi S, Gao P, Iwata A, Kurzman R, Saykin AJ, Teipel S, Vellas B, Vergallo A, Wang H, Cummings J. Designing the next-generation clinical care pathway for Alzheimer's disease. NATURE AGING 2022; 2:692-703. [PMID: 37118137 PMCID: PMC10148953 DOI: 10.1038/s43587-022-00269-x] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 07/07/2022] [Indexed: 04/30/2023]
Abstract
The reconceptualization of Alzheimer's disease (AD) as a clinical and biological construct has facilitated the development of biomarker-guided, pathway-based targeted therapies, many of which have reached late-stage development with the near-term potential to enter global clinical practice. These medical advances mark an unprecedented paradigm shift and requires an optimized global framework for clinical care pathways for AD. In this Perspective, we describe the blueprint for transitioning from the current, clinical symptom-focused and inherently late-stage diagnosis and management of AD to the next-generation pathway that incorporates biomarker-guided and digitally facilitated decision-making algorithms for risk stratification, early detection, timely diagnosis, and preventative or therapeutic interventions. We address critical and high-priority challenges, propose evidence-based strategic solutions, and emphasize that the perspectives of affected individuals and care partners need to be considered and integrated.
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Affiliation(s)
| | - Rhoda Au
- Depts of Anatomy & Neurobiology, Neurology and Epidemiology, Boston University Schools of Medicine and Public Health, Boston, MA, USA
| | - Soeren Mattke
- Center for Improving Chronic Illness Care, University of Southern California, San Diego, San Diego, CA, USA
| | - Wiesje M van der Flier
- Alzheimer Center Amsterdam, Depts of Neurology and Epidemiology and Data Science, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Paul Aisen
- Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, San Diego, CA, USA
| | - Liana Apostolova
- Departments of Neurology, Radiology, Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Christopher Chen
- Memory Aging and Cognition Centre, Departments of Pharmacology and Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Min Cho
- Neurology Business Group, Eisai, Nutley, NJ, USA
| | | | - Peng Gao
- Neurology Business Group, Eisai, Nutley, NJ, USA
| | | | | | - Andrew J Saykin
- Indiana Alzheimer's Disease Research Center and the Departments of Radiology and Imaging Sciences, Medical and Molecular Genetics, and Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Stefan Teipel
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Rostock, Germany
- Department of Psychosomatic Medicine, University Medical Center Rostock, Rostock, Germany
| | - Bruno Vellas
- University Paul Sabatier, Gerontopole, Toulouse University Hospital, UMR INSERM 1285, Toulouse, France
| | | | - Huali Wang
- Dementia Care and Research Center, Peking University Institute of Mental Health (Sixth Hospital), National Clinical Research Center for Mental Disorders, Beijing, China
| | - Jeffrey Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, University of Nevada, Las Vegas (UNLV), Las Vegas, NV, USA
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11
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Pemberton HG, Collij LE, Heeman F, Bollack A, Shekari M, Salvadó G, Alves IL, Garcia DV, Battle M, Buckley C, Stephens AW, Bullich S, Garibotto V, Barkhof F, Gispert JD, Farrar G. Quantification of amyloid PET for future clinical use: a state-of-the-art review. Eur J Nucl Med Mol Imaging 2022; 49:3508-3528. [PMID: 35389071 PMCID: PMC9308604 DOI: 10.1007/s00259-022-05784-y] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 03/25/2022] [Indexed: 12/15/2022]
Abstract
Amyloid-β (Aβ) pathology is one of the earliest detectable brain changes in Alzheimer's disease (AD) pathogenesis. The overall load and spatial distribution of brain Aβ can be determined in vivo using positron emission tomography (PET), for which three fluorine-18 labelled radiotracers have been approved for clinical use. In clinical practice, trained readers will categorise scans as either Aβ positive or negative, based on visual inspection. Diagnostic decisions are often based on these reads and patient selection for clinical trials is increasingly guided by amyloid status. However, tracer deposition in the grey matter as a function of amyloid load is an inherently continuous process, which is not sufficiently appreciated through binary cut-offs alone. State-of-the-art methods for amyloid PET quantification can generate tracer-independent measures of Aβ burden. Recent research has shown the ability of these quantitative measures to highlight pathological changes at the earliest stages of the AD continuum and generate more sensitive thresholds, as well as improving diagnostic confidence around established binary cut-offs. With the recent FDA approval of aducanumab and more candidate drugs on the horizon, early identification of amyloid burden using quantitative measures is critical for enrolling appropriate subjects to help establish the optimal window for therapeutic intervention and secondary prevention. In addition, quantitative amyloid measurements are used for treatment response monitoring in clinical trials. In clinical settings, large multi-centre studies have shown that amyloid PET results change both diagnosis and patient management and that quantification can accurately predict rates of cognitive decline. Whether these changes in management reflect an improvement in clinical outcomes is yet to be determined and further validation work is required to establish the utility of quantification for supporting treatment endpoint decisions. In this state-of-the-art review, several tools and measures available for amyloid PET quantification are summarised and discussed. Use of these methods is growing both clinically and in the research domain. Concurrently, there is a duty of care to the wider dementia community to increase visibility and understanding of these methods.
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Affiliation(s)
- Hugh G Pemberton
- GE Healthcare, Amersham, UK.
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK.
- UCL Queen Square Institute of Neurology, University College London, London, UK.
| | - Lyduine E Collij
- Department of Radiology and Nuclear Medicine, Amsterdam Neurocience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Fiona Heeman
- Department of Radiology and Nuclear Medicine, Amsterdam Neurocience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Ariane Bollack
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
| | - Mahnaz Shekari
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Gemma Salvadó
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Isadora Lopes Alves
- Department of Radiology and Nuclear Medicine, Amsterdam Neurocience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Brain Research Center, Amsterdam, The Netherlands
| | - David Vallez Garcia
- Department of Radiology and Nuclear Medicine, Amsterdam Neurocience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Mark Battle
- GE Healthcare, Amersham, UK
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | | | | | | | - Valentina Garibotto
- Division of Nuclear Medicine and Molecular Imaging, University Hospitals of Geneva, Geneva, Switzerland
- NIMTLab, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Frederik Barkhof
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- UCL Queen Square Institute of Neurology, University College London, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam Neurocience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Centro de Investigación Biomédica en Red Bioingeniería, Biomateriales y Nanomedicina, Madrid, Spain
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12
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Cummings J, Kinney J. Biomarkers for Alzheimer's Disease: Context of Use, Qualification, and Roadmap for Clinical Implementation. MEDICINA (KAUNAS, LITHUANIA) 2022; 58:952. [PMID: 35888671 PMCID: PMC9318582 DOI: 10.3390/medicina58070952] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 07/13/2022] [Accepted: 07/15/2022] [Indexed: 12/30/2022]
Abstract
Background and Objectives: The US Food and Drug Administration (FDA) defines a biomarker as a characteristic that is measured as an indicator of normal biological processes, pathogenic processes, or responses to an exposure or intervention. Biomarkers may be used in clinical care or as drug development tools (DDTs) in clinical trials. The goal of this review and perspective is to provide insight into the regulatory guidance for the use of biomarkers in clinical trials and clinical care. Materials and Methods: We reviewed FDA guidances relevant to biomarker use in clinical trials and their transition to use in clinical care. We identified instructive examples of these biomarkers in Alzheimer's disease (AD) drug development and their application in clinical practice. Results: For use in clinical trials, biomarkers must have a defined context of use (COU) as a risk/susceptibility, diagnostic, monitoring, predictive, prognostic, pharmacodynamic, or safety biomarker. A four-stage process defines the pathway to establish the regulatory acceptance of the COU for a biomarker including submission of a letter of intent, description of the qualification plan, submission of a full qualification package, and acceptance through a qualification recommendation. Biomarkers used in clinical care may be companion biomarkers, in vitro diagnostic devices (IVDs), or laboratory developed tests (LDTs). A five-phase biomarker development process has been proposed to structure the biomarker development process. Conclusions: Biomarkers are increasingly important in drug development and clinical care. Adherence to regulatory guidance for biomarkers used in clinical trials and patient care is required to advance these important drug development and clinical tools.
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Affiliation(s)
- Jeffrey Cummings
- Pam Quirk Brain Health and Biomarker Laboratory, Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas, Las Vegas, NV 89154, USA;
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13
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Altomare D, Collij L, Caprioglio C, Scheltens P, van Berckel BNM, Alves IL, Berkhof J, de Gier Y, Garibotto V, Moro C, Poitrine L, Delrieu J, Payoux P, Saint-Aubert L, Molinuevo JL, Grau-Rivera O, Gispert JD, Minguillón C, Fauria K, Sanchez MF, Rădoi A, Drzezga A, Jessen F, Escher C, Zeyen P, Nordberg A, Savitcheva I, Jelic V, Walker Z, Lee HY, Lee L, Demonet JF, Plaza Wuthrich S, Gismondi R, Farrar G, Barkhof F, Stephens AW, Frisoni GB. Description of a European memory clinic cohort undergoing amyloid-PET: The AMYPAD Diagnostic and Patient Management Study. Alzheimers Dement 2022; 19:844-856. [PMID: 35715930 DOI: 10.1002/alz.12696] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 03/11/2022] [Accepted: 04/29/2022] [Indexed: 11/11/2022]
Abstract
INTRODUCTION AMYPAD Diagnostic and Patient Management Study (DPMS) aims to investigate the clinical utility and cost-effectiveness of amyloid-PET in Europe. Here we present participants' baseline features and discuss the representativeness of the cohort. METHODS Participants with subjective cognitive decline plus (SCD+), mild cognitive impairment (MCI), or dementia were recruited in eight European memory clinics from April 16, 2018, to October 30, 2020, and randomized into three arms: ARM1, early amyloid-PET; ARM2, late amyloid-PET; and ARM3, free-choice. RESULTS A total of 840 participants (244 SCD+, 341 MCI, and 255 dementia) were enrolled. Sociodemographic/clinical features did not differ significantly among recruiting memory clinics or with previously reported cohorts. The randomization assigned 35% of participants to ARM1, 32% to ARM2, and 33% to ARM3; cognitive stages were distributed equally across the arms. DISCUSSION The features of AMYPAD-DPMS participants are as expected for a memory clinic population. This ensures the generalizability of future study results.
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Affiliation(s)
- Daniele Altomare
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
| | - Lyduine Collij
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers (UMC) - Location VUmc, Amsterdam, The Netherlands
| | - Camilla Caprioglio
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
| | - Philip Scheltens
- Alzheimer Center, Department of Neurology, Amsterdam University Medical Centers (UMC) - Location VUmc, Amsterdam, The Netherlands
| | - Bart N M van Berckel
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers (UMC) - Location VUmc, Amsterdam, The Netherlands
| | - Isadora Lopes Alves
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers (UMC) - Location VUmc, Amsterdam, The Netherlands
| | - Johannes Berkhof
- Department of Epidemiology and Data Science, Amsterdam University Medical Centers (UMC) - Location VUmc, Amsterdam, The Netherlands
| | - Yvonne de Gier
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers (UMC) - Location VUmc, Amsterdam, The Netherlands
| | - Valentina Garibotto
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Geneva University Neurocenter and Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland
| | - Christian Moro
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
| | - Léa Poitrine
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
| | - Julien Delrieu
- Gérontopôle, Department of Geriatrics, Toulouse University Hospital, Toulouse, France
- Maintain Aging Research team, CERPOP, Inserm, Université Paul Sabatier, Toulouse, France
| | - Pierre Payoux
- Department of Nuclear Medicine, Toulouse University Hospital, Toulouse, France
- Toulouse NeuroImaging Center (ToNIC), Inserm, Université Paul Sabatier, Toulouse, France
| | - Laure Saint-Aubert
- Department of Nuclear Medicine, Toulouse University Hospital, Toulouse, France
- Toulouse NeuroImaging Center (ToNIC), Université de Toulouse, Inserm, Université Paul Sabatier, Toulouse, France
| | - José Luis Molinuevo
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- H. Lundbeck A/S, Copenhagen, Denmark
| | - Oriol Grau-Rivera
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
| | - Juan-Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
- Centro de Investigación Biomédica en Red Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Barcelona, Spain
| | - Carolina Minguillón
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
| | - Karine Fauria
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
| | - Marta Felez Sanchez
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
| | - Andreea Rădoi
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
| | - Alexander Drzezga
- Department of Nuclear Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany
- German Center for Neurodegenerative Diseases (DZNE), Bonn-Cologne, Germany
- Institute of Neuroscience and Medicine (INM-2), Molecular Organization of the Brain, Forschungszentrum Jülich, Germany
| | - Frank Jessen
- Department of Psychiatry, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Claus Escher
- Department of Psychiatry, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Philip Zeyen
- Department of Psychiatry, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Agneta Nordberg
- Department of Neurobiology, Care Sciences and Society, Center of Alzheimer Research, Karolinska Institutet, Stockholm, Sweden
- Theme Aging, Karolinska University Hospital, Stockholm, Sweden
| | - Irina Savitcheva
- Medical Radiation Physics and Nuclear Medicine, Section for Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Vesna Jelic
- Cognitive Disorders Clinic, Theme Inflammation and Aging, Karolinska University Hospital-Huddinge, Stockholm, Sweden
| | - Zuzana Walker
- Division of Psychiatry, University College London, London, UK
- St. Margaret's Hospital, Essex Partnership University NHS Foundation Trust, Essex, UK
| | - Ho-Yun Lee
- St. Margaret's Hospital, Essex Partnership University NHS Foundation Trust, Essex, UK
| | - Lean Lee
- Division of Psychiatry, University College London, London, UK
| | | | - Sonia Plaza Wuthrich
- Leenaards Memory Center, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | | | | | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers (UMC) - Location VUmc, Amsterdam, The Netherlands
- Institutes of Neurology and Healthcare Engineering, UCL, London, UK
| | | | - Giovanni B Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
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14
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Nabizadeh F, Pourhamzeh M, Khani S, Rezaei A, Ranjbaran F, Deravi N. Plasma phosphorylated-tau181 levels reflect white matter microstructural changes across Alzheimer's disease progression. Metab Brain Dis 2022; 37:761-771. [PMID: 35015198 DOI: 10.1007/s11011-022-00908-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 01/06/2022] [Indexed: 01/25/2023]
Abstract
Alzheimer's Disease (AD) is characterized by cognitive impairments that hinder daily activities and lead to personal and behavioral problems. Plasma hyperphosphorylated tau protein at threonine 181 (p-tau181) has recently emerged as a new sensitive tool for the diagnosis of AD patients. We herein investigated the association of plasma P-tau181 and white matter (WM) microstructural changes in AD. We obtained data from a large prospective cohort of elderly individuals participating in the Alzheimer's Disease Neuroimaging Initiative (ADNI), which included baseline measurements of plasma P-tau181 and imaging findings. A subset of 41 patients with AD, 119 patients with mild cognitive impairments (MCI), and 43 healthy controls (HC) was included in the study, all of whom had baseline blood P-tau181 levels and had also undergone Diffusion Tensor Imaging. The analysis revealed that the plasma level of P-tau181 has a positive correlation with changes in Mean Diffusivity (MD), Radial Diffusivity (RD), and Axial Diffusivity (AxD), but a negative with Fractional Anisotropy (FA) parameters in WM regions of all participants. There is also a significant association between WM microstructural changes in different regions and P-tau181 plasma measurements within each MCI, HC, and AD group. In conclusion, our findings clarified that plasma P-tau181 levels are associated with changes in WM integrity in AD. P-tau181 could improve the accuracy of diagnostic procedures and support the application of blood-based biomarkers to diagnose WM neurodegeneration. Longitudinal clinical studies are also needed to demonstrate the efficacy of the P-tau181 biomarker and predict its role in structural changes.
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Affiliation(s)
- Fardin Nabizadeh
- School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
- Neuroscience Research Group (NRG), Universal Scientific Education and Research Network (USERN), Tehran, Iran.
| | - Mahsa Pourhamzeh
- Division of Neuroscience, Cellular and Molecular Research Center, Iran University of Medical Sciences, Tehran, Iran.
| | - Saghar Khani
- School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Ayda Rezaei
- Neuroscience Research Group (NRG), Universal Scientific Education and Research Network (USERN), Tehran, Iran
| | - Fatemeh Ranjbaran
- School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Niloofar Deravi
- Student Research Committee, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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15
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Gallucci M, Cenesi L, White C, Antuono P, Quaglio G, Bonanni L. Lights and Shadows of Cerebrospinal Fluid Biomarkers in the Current Alzheimer's Disease Framework. J Alzheimers Dis 2022; 86:1061-1072. [PMID: 35180122 PMCID: PMC9108561 DOI: 10.3233/jad-215432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND The most significant biomarkers that are included in the Alzheimer's disease (AD) research framework are amyloid-β plaques deposition, p-tau, t-tau, and neurodegeneration.Although cerebrospinal fluid (CSF) biomarkers are included in the most recent AD research criteria, their use is increasing in the routine clinical practice and is applied also to the preclinical phases of AD, including mild cognitive impairment. The role of these biomarkers is still unclear concerning the preclinical stage of AD diagnosis, the CSF methodology, and the costs-benefits of the biomarkers' tests. The controversies regarding the use of biomarkers in the clinical practice are related to the concepts of analytical validity, clinical validity, and clinical utility and to the question of whether they are able to diagnose AD without the support of AD clinical phenotypes. OBJECTIVE The objective of the present work is to expose the strengths and weaknesses of the use of CSF biomarkers in the diagnosis of AD in a clinical context. METHODS We used PubMed as main source for articles published and the final reference list was generated on the basis of relevance to the topics covered in this work. RESULTS The use of CSF biomarkers for AD diagnosis is certainly important but its indication in routine clinical practice, especially for prodromal conditions, needs to be regulated and also contextualized considering the variety of possible clinical AD phenotypes. CONCLUSION We suggest that the diagnosis of AD should be understood both as clinical and pathological.
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Affiliation(s)
- Maurizio Gallucci
- Cognitive Impairment Center, Local Health Authority n. 2 Marca Trevigiana, Treviso, Italy.,Associazione Alzheimer Treviso Onlus, Treviso, Italy
| | - Leandro Cenesi
- Cognitive Impairment Center, Local Health Authority n. 2 Marca Trevigiana, Treviso, Italy
| | - Céline White
- Cognitive Impairment Center, Local Health Authority n. 2 Marca Trevigiana, Treviso, Italy
| | - Piero Antuono
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Gianluca Quaglio
- Scientific Foresight Unit (STOA), European Parliamentary Research Service, European Parliament, Brussels, Belgium
| | - Laura Bonanni
- Department of Medicine and Aging Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
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16
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PET imaging in dementia. Nucl Med Mol Imaging 2022. [DOI: 10.1016/b978-0-12-822960-6.00089-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
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17
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Müller EG, Edwin TH, Strand BH, Stokke C, Revheim ME, Knapskog AB. Is Amyloid Burden Measured by 18F-Flutemetamol PET Associated with Progression in Clinical Alzheimer's Disease? J Alzheimers Dis 2021; 85:197-205. [PMID: 34776444 PMCID: PMC8842772 DOI: 10.3233/jad-215046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: Patients with Alzheimer’s disease (AD) show heterogeneity in clinical progression rate, and we have limited tools to predict prognosis. Amyloid burden from 18F-Flutemetamol positron emission tomography (PET), as measured by standardized uptake value ratios (SUVR), might provide prognostic information. Objective: We investigate whether 18F-Flutemetamol PET composite or regional SUVRs are associated with trajectories of clinical progression. Methods: This observational longitudinal study included 94 patients with clinical AD. PET images were semi-quantified with normalization to pons. Group-based trajectory modeling was applied to identify trajectory groups according to change in the Clinical Dementia Rating Scale Sum of Boxes (CDR-SB) over time. Multinomial logistic regression models assessed the association of SUVRs with trajectory group membership. Results: Three trajectory groups were identified. In the regression models, neither composite nor regional SUVRs were associated with trajectory group membership. Conclusion: There were no associations between CDR progression and 18F-Flutemetamol PET-derived composite SUVRs or regional SUVRs in clinical AD.
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Affiliation(s)
- Ebba Gløersen Müller
- Division of Radiology and Nuclear Medicine, Department of Nuclear Medicine, Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Trine Holt Edwin
- Institute of Health and Society, Faculty of Medicine, University of Oslo, Oslo, Norway.,Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway.,Norwegian National Advisory Unit on Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway
| | - Bjørn Heine Strand
- Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway.,Norwegian National Advisory Unit on Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway.,Department of Chronic Diseases and Ageing, Norwegian Institute of Public Health, Oslo, Norway
| | - Caroline Stokke
- Division of Radiology and Nuclear Medicine, Department of Nuclear Medicine, Oslo University Hospital, Oslo, Norway.,Department of Physics, University of Oslo, Oslo, Norway
| | - Mona Elisabeth Revheim
- Division of Radiology and Nuclear Medicine, Department of Nuclear Medicine, Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
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18
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Lagarde J, Olivieri P, Bottlaender M, Sarazin M. Diagnosi clinicolaboratoristica della malattia di Alzheimer. Neurologia 2021. [DOI: 10.1016/s1634-7072(21)45320-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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19
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Ashton NJ, Leuzy A, Karikari TK, Mattsson-Carlgren N, Dodich A, Boccardi M, Corre J, Drzezga A, Nordberg A, Ossenkoppele R, Zetterberg H, Blennow K, Frisoni GB, Garibotto V, Hansson O. The validation status of blood biomarkers of amyloid and phospho-tau assessed with the 5-phase development framework for AD biomarkers. Eur J Nucl Med Mol Imaging 2021; 48:2140-2156. [PMID: 33677733 PMCID: PMC8175325 DOI: 10.1007/s00259-021-05253-y] [Citation(s) in RCA: 75] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 02/09/2021] [Indexed: 12/14/2022]
Abstract
PURPOSE The development of blood biomarkers that reflect Alzheimer's disease (AD) pathophysiology (phosphorylated tau and amyloid-β) has offered potential as scalable tests for dementia differential diagnosis and early detection. In 2019, the Geneva AD Biomarker Roadmap Initiative included blood biomarkers in the systematic validation of AD biomarkers. METHODS A panel of experts convened in November 2019 at a two-day workshop in Geneva. The level of maturity (fully achieved, partly achieved, preliminary evidence, not achieved, unsuccessful) of blood biomarkers was assessed based on the Biomarker Roadmap methodology and discussed fully during the workshop which also evaluated cerebrospinal fluid (CSF) and positron emission tomography (PET) biomarkers. RESULTS Plasma p-tau has shown analytical validity (phase 2 primary aim 1) and first evidence of clinical validity (phase 3 primary aim 1), whereas the maturity level for Aβ remains to be partially achieved. Full and partial achievement has been assigned to p-tau and Aβ, respectively, in their associations to ante-mortem measures (phase 2 secondary aim 2). However, only preliminary evidence exists for the influence of covariates, assay comparison and cut-off criteria. CONCLUSIONS Despite the relative infancy of blood biomarkers, in comparison to CSF biomarkers, much has already been achieved for phases 1 through 3 - with p-tau having greater success in detecting AD and predicting disease progression. However, sufficient data about the effect of covariates on the biomarker measurement is lacking. No phase 4 (real-world performance) or phase 5 (assessment of impact/cost) aim has been tested, thus not achieved.
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Affiliation(s)
- N J Ashton
- Institute of Neuroscience & Physiology, Department of Psychiatry & Neurochemistry, Sahlgrenska Academy, University of Gothenburg, House V3/SU, SE-431 80, Mölndal, Sweden.
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden.
| | - A Leuzy
- Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Lund, Sweden
| | - T K Karikari
- Institute of Neuroscience & Physiology, Department of Psychiatry & Neurochemistry, Sahlgrenska Academy, University of Gothenburg, House V3/SU, SE-431 80, Mölndal, Sweden
| | - N Mattsson-Carlgren
- Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Lund, Sweden
- Department of Neurology, Skåne University Hospital, Lund, Sweden
- Wallenberg Centre for Molecular Medicine, Lund University, Lund, Sweden
| | - A Dodich
- NIMTlab - Neuroimaging and Innovative Molecular Tracers Laboratory, University of Geneva, Geneva, Switzerland
- Center for Neurocognitive Rehabilitation (CeRiN), CIMeC, University of Trento, Trento, Italy
| | - M Boccardi
- German Center for Neurodegenerative Diseases (DZNE), Rostock-Greifswald, Rostock, Germany
- LANVIE - Laboratory of Neuroimaging of Aging, University of Geneva, Geneva, Switzerland
| | - J Corre
- Centre National de la Recherche Scientifique, Montpellier, France
| | - A Drzezga
- Medical Faculty and University Hospital of Cologne, Cologne, Germany
| | - A Nordberg
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Theme Aging, Karolinska University Hospital Stockholm, Stockholm, Sweden
| | - R Ossenkoppele
- Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Lund, Sweden
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - H Zetterberg
- Institute of Neuroscience & Physiology, Department of Psychiatry & Neurochemistry, Sahlgrenska Academy, University of Gothenburg, House V3/SU, SE-431 80, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Gothenburg, Sweden
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, London, UK
| | - K Blennow
- Institute of Neuroscience & Physiology, Department of Psychiatry & Neurochemistry, Sahlgrenska Academy, University of Gothenburg, House V3/SU, SE-431 80, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - G B Frisoni
- German Center for Neurodegenerative Diseases (DZNE), Rostock-Greifswald, Rostock, Germany
- Memory Clinic, Geneva University Hospitals, Geneva, Switzerland
| | - V Garibotto
- NIMTlab - Neuroimaging and Innovative Molecular Tracers Laboratory, University of Geneva, Geneva, Switzerland
- Diagnostic Department, University Hospitals of Geneva, Geneva, Switzerland
| | - O Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Lund, Sweden.
- UK Dementia Research Institute at UCL, London, UK.
- Memory Clinic, Skåne University Hospital, SE-205 02, Malmö, Sweden.
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20
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Alongi P, Chiaravalloti A, Berti V, Vellani C, Trifirò G, Puccini G, Carli G, Chincarini A, Morbelli S, Perani D, Sestini S. Amyloid PET in the diagnostic workup of neurodegenerative disease. Clin Transl Imaging 2021. [DOI: 10.1007/s40336-021-00428-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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21
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Cecchin D, Garibotto V, Law I, Goffin K. PET Imaging in Neurodegeneration and Neuro-oncology: Variants and Pitfalls. Semin Nucl Med 2021; 51:408-418. [PMID: 33820651 DOI: 10.1053/j.semnuclmed.2021.03.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
In neurodegenerative diseases, positron emission tomography (PET) imaging plays an important role in the early identification and differential diagnosis in particular in clinically challenging patients. 18F-FDG is still the most widely used and established tracer in this patient group, with different cortical and subcortical regions being preferentially affected in different neurodegenerative diseases, such as frontotemporal dementia, Alzheimer's disease or Lewy Body dementia, resulting in typical hypometabolic patterns. Over the last decades, however, the implementation of tracers specific for the pathological deposits characteristic of the different diseases, such as amyloid and tau, has revolutionized the way of classifying and reporting cases of cognitive impairment of neurodegenerative origin, providing complementary information to 18F-FDG PET. In neuro-oncology, PET imaging can be performed in several clinical indications, as highlighted in the joint European Association of Nuclear Medicine (EANM)/European Association of Neuro-Oncology (EANO)/Response assessment in neuro-oncology (RANO) practice guidelines on imaging in neuro-oncology. For assessment of glioma, amino-acid analogues, such as 11C-methionine or 18F-FET, are used whenever clinically available, as they offer excellent tumor-to-background ratios in malignant tumors. Moreover, dynamic acquisition of amino-acid analogue tracers and assessment of the shape of the time-activity curve can be used to perform noninvasive grading of brain gliomas, differentiating low from high grade presentations. In both settings, however, thorough knowledge of the normal physiological tracer distribution and the variants and pitfalls that can occur during image acquisition, processing and interpretation is mandatory in order to provide optimal diagnostic information to referring physicians and patients. Especially in neuro-oncology, this process can be aided by the active use of coregistered magnetic resonance imaging to accurately identify the imaging correlates of developmental origin, acute and chronic stroke, inflammation, infection and seizure related activity.
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Affiliation(s)
- Diego Cecchin
- Nuclear Medicine Unit, Department of Medicine DIMED, Padua University Hospital, Padua, Italy
| | - Valentina Garibotto
- Nuclear Medicine and Molecular Imaging Division, Diagnostic Department, University Hospitals of Geneva and NIMTLab, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Ian Law
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, Copenhagen, Denmark
| | - Karolien Goffin
- Department of Nuclear Medicine, University Hospital Leuven, Division of Nuclear Medicine and Molecular Imaging, KU Leuven, Leuven, Belgium.
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22
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Clinical validity of increased cortical binding of tau ligands of the THK family and PBB3 on PET as biomarkers for Alzheimer's disease in the context of a structured 5-phase development framework. Eur J Nucl Med Mol Imaging 2021; 48:2086-2096. [PMID: 33723628 PMCID: PMC8175292 DOI: 10.1007/s00259-021-05277-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 02/21/2021] [Indexed: 12/28/2022]
Abstract
PURPOSE The research community has focused on defining reliable biomarkers for the early detection of the pathological hallmarks of Alzheimer's disease (AD). In 2017, the Geneva AD Biomarker Roadmap initiative adapted the framework for the systematic validation of oncological biomarkers to AD, with the aim to accelerate their development and implementation in clinical practice. The aim of this work was to assess the validation status of tau PET ligands of the THK family and PBB3 as imaging biomarkers for AD, based on the Biomarker Roadmap methodology. METHODS A panel of experts in AD biomarkers convened in November 2019 at a 2-day workshop in Geneva. The level of clinical validity of tau PET ligands of the THK family and PBB3 was assessed based on the 5-phase development framework before the meeting and discussed during the workshop. RESULTS PET radioligands of the THK family discriminate well between healthy controls and patients with AD dementia (phase 2; partly achieved) and recent evidence suggests an accurate diagnostic accuracy at the mild cognitive impairment (MCI) stage of the disease (phase 3; partly achieved). The phases 2 and 3 were considered not achieved for PBB3 since no evidence exists about the ligand's diagnostic accuracy. Preliminary evidence exists about the secondary aims of each phase for all ligands. CONCLUSION Much work remains for completing the aims of phases 2 and 3 and replicating the available evidence. However, it is unlikely that the validation process for these tracers will be completed, given the presence of off-target binding and the development of second-generation tracers with improved binding and pharmacokinetic properties.
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23
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Boccardi M, Dodich A, Albanese E, Gayet-Ageron A, Festari C, Ashton NJ, Bischof GN, Chiotis K, Leuzy A, Wolters EE, Walter MA, Rabinovici GD, Carrillo M, Drzezga A, Hansson O, Nordberg A, Ossenkoppele R, Villemagne VL, Winblad B, Frisoni GB, Garibotto V. The strategic biomarker roadmap for the validation of Alzheimer's diagnostic biomarkers: methodological update. Eur J Nucl Med Mol Imaging 2021; 48:2070-2085. [PMID: 33688996 PMCID: PMC8175304 DOI: 10.1007/s00259-020-05120-2] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 11/15/2020] [Indexed: 12/11/2022]
Abstract
Background The 2017 Alzheimer’s disease (AD) Strategic Biomarker Roadmap (SBR) structured the validation of AD diagnostic biomarkers into 5 phases, systematically assessing analytical validity (Phases 1–2), clinical validity (Phases 3–4), and clinical utility (Phase 5) through primary and secondary Aims. This framework allows to map knowledge gaps and research priorities, accelerating the route towards clinical implementation. Within an initiative aimed to assess the development of biomarkers of tau pathology, we revised this methodology consistently with progress in AD research. Methods We critically appraised the adequacy of the 2017 Biomarker Roadmap within current diagnostic frameworks, discussed updates at a workshop convening the Alzheimer’s Association and 8 leading AD biomarker research groups, and detailed the methods to allow consistent assessment of aims achievement for tau and other AD diagnostic biomarkers. Results The 2020 update applies to all AD diagnostic biomarkers. In Phases 2–3, we admitted a greater variety of study designs (e.g., cross-sectional in addition to longitudinal) and reference standards (e.g., biomarker confirmation in addition to clinical progression) based on construct (in addition to criterion) validity. We structured a systematic data extraction to enable transparent and formal evidence assessment procedures. Finally, we have clarified issues that need to be addressed to generate data eligible to evidence-to-decision procedures. Discussion This revision allows for more versatile and precise assessment of existing evidence, keeps up with theoretical developments, and helps clinical researchers in producing evidence suitable for evidence-to-decision procedures. Compliance with this methodology is essential to implement AD biomarkers efficiently in clinical research and diagnostics.
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Affiliation(s)
- Marina Boccardi
- German Center for Neurodegenerative Diseases DZNE-Standort Rostock/Greifswald, Gehlsheimer Str. 20, 18147, Rostock, Germany.
- LANVIE - Laboratory of Neuroimaging of Aging, University of Geneva, Geneva, Switzerland.
| | - Alessandra Dodich
- Center for Neurocognitive Rehabilitation (CeRiN), CIMeC, University of Trento, Trento, Italy
- NIMTlab - Neuroimaging and Innovative Molecular Tracers Laboratory, University of Geneva, Geneva, Switzerland
| | - Emiliano Albanese
- USI - Università della Svizzera Italiana, Institute of Public Health (IPH), Lugano, Switzerland
| | - Angèle Gayet-Ageron
- Division of Clinical Epidemiology, Department of Health and Community Medicine, University of Geneva & University Hospitals of Geneva, Geneva, Switzerland
| | - Cristina Festari
- LANE - Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Nicholas J Ashton
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience & Physiology, The Sahlgrenska Academy at The University of Gothenburg, Molndal, Sweden
- NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation, London, UK
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Gérard N Bischof
- Department of Nuclear Medicine, University Hospital Cologne, Cologne, Germany
| | - Konstantinos Chiotis
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Theme Neurology, Karolinska University Hospital, Stockholm, Sweden
| | - Antoine Leuzy
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Emma E Wolters
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Martin A Walter
- Nuclear Medicine and Molecular Division, Geneva Medical Hospital, Geneva, Switzerland
| | - Gil D Rabinovici
- Departments of Neurology, Radiology & Biomedical Imaging, University of California, San Francisco, CA, USA
| | | | - Alexander Drzezga
- Faculty of Medicine, University of Cologne, Cologne, Germany
- German Center for Neurodegenerative Diseases (DZNE), Bonn/Cologne, Germany
- Molecular Organization of the Brain, Research Center Jülich, Institute of Neuroscience and Medicine (INM-2), Julich, Germany
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmo, Sweden
| | - Agneta Nordberg
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Karolinska University Hospital, Theme Aging, Geriatric Clinic, Huddinge, Sweden
| | - Rik Ossenkoppele
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
- Department of Clinical Memory Research, Lund University, Lund, Sweden
| | - Victor L Villemagne
- Department of Molecular Imaging & Therapy, Austin Health, Melbourne, VIC, Australia
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsilvania, USA
| | - Bengt Winblad
- Karolinska University Hospital, Theme Aging, Geriatric Clinic, Huddinge, Sweden
- Department of Neurobiology, Care Sciences and Society, Division of Neurogeriatrics, Karolinska Institutet, Stockholm, Sweden
| | - Giovanni B Frisoni
- LANVIE - Laboratory of Neuroimaging of Aging, University of Geneva, Geneva, Switzerland
- Memory Clinic, University Hospital, Geneva, Switzerland
| | - Valentina Garibotto
- NIMTlab - Neuroimaging and Innovative Molecular Tracers Laboratory, University of Geneva, Geneva, Switzerland
- Nuclear Medicine and Molecular Division, Geneva Medical Hospital, Geneva, Switzerland
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24
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2020 update on the clinical validity of cerebrospinal fluid amyloid, tau, and phospho-tau as biomarkers for Alzheimer's disease in the context of a structured 5-phase development framework. Eur J Nucl Med Mol Imaging 2021; 48:2121-2139. [PMID: 33674895 PMCID: PMC8175301 DOI: 10.1007/s00259-021-05258-7] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 02/11/2021] [Indexed: 12/15/2022]
Abstract
Purpose In the last decade, the research community has focused on defining reliable biomarkers for the early detection of Alzheimer’s disease (AD) pathology. In 2017, the Geneva AD Biomarker Roadmap Initiative adapted a framework for the systematic validation of oncological biomarkers to cerebrospinal fluid (CSF) AD biomarkers—encompassing the 42 amino-acid isoform of amyloid-β (Aβ42), phosphorylated-tau (P-tau), and Total-tau (T-tau)—with the aim to accelerate their development and clinical implementation. The aim of this work is to update the current validation status of CSF AD biomarkers based on the Biomarker Roadmap methodology. Methods A panel of experts in AD biomarkers convened in November 2019 at a 2-day workshop in Geneva. The level of maturity (fully achieved, partly achieved, preliminary evidence, not achieved, unsuccessful) of CSF AD biomarkers was assessed based on the Biomarker Roadmap methodology before the meeting and presented and discussed during the workshop. Results By comparison to the previous 2017 Geneva Roadmap meeting, the primary advances in CSF AD biomarkers have been in the area of a unified protocol for CSF sampling, handling and storage, the introduction of certified reference methods and materials for Aβ42, and the introduction of fully automated assays. Additional advances have occurred in the form of defining thresholds for biomarker positivity and assessing the impact of covariates on their discriminatory ability. Conclusions Though much has been achieved for phases one through three, much work remains in phases four (real world performance) and five (assessment of impact/cost). To a large degree, this will depend on the availability of disease-modifying treatments for AD, given these will make accurate and generally available diagnostic tools key to initiate therapy. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-021-05258-7.
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Outcomes of clinical utility in amyloid-PET studies: state of art and future perspectives. Eur J Nucl Med Mol Imaging 2021; 48:2157-2168. [PMID: 33594474 PMCID: PMC8175294 DOI: 10.1007/s00259-020-05187-x] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 12/28/2020] [Indexed: 12/15/2022]
Abstract
PURPOSE To review how outcomes of clinical utility are operationalized in current amyloid-PET validation studies, to prepare for formal assessment of clinical utility of amyloid-PET-based diagnosis. METHODS Systematic review of amyloid-PET research studies published up to April 2020 that included outcomes of clinical utility. We extracted and analyzed (a) outcome categories, (b) their definition, and (c) their methods of assessment. RESULTS Thirty-two studies were eligible. (a) Outcome categories were clinician-centered (found in 25/32 studies, 78%), patient-/caregiver-centered (in 9/32 studies, 28%), and health economics-centered (5/32, 16%). (b) Definition: Outcomes were mainly defined by clinical researchers; only the ABIDE study expressly included stakeholders in group discussions. Clinician-centered outcomes mainly consisted of incremental diagnostic value (25/32, 78%) and change in patient management (17/32, 53%); patient-/caregiver-centered outcomes considered distress after amyloid-pet-based diagnosis disclosure (8/32, 25%), including quantified burden of procedure for patients' outcomes (n = 8) (1/8, 12.5%), impact of disclosure of results (6/8, 75%), and psychological implications of biomarker-based diagnosis (75%); and health economics outcomes focused on costs to achieve a high-confidence etiological diagnosis (5/32, 16%) and impact on quality of life (1/32, 3%). (c) Assessment: all outcome categories were operationalized inconsistently across studies, employing 26 different tools without formal rationale for selection. CONCLUSION Current studies validating amyloid-PET already assessed outcomes for clinical utility, although non-clinician-based outcomes were inconsistent. A wider participation of stakeholders may help produce a more thorough and systematic definition and assessment of outcomes of clinical utility and help collect evidence informing decisions on reimbursement of amyloid-PET.
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Clinical validity of increased cortical uptake of [ 18F]flortaucipir on PET as a biomarker for Alzheimer's disease in the context of a structured 5-phase biomarker development framework. Eur J Nucl Med Mol Imaging 2021; 48:2097-2109. [PMID: 33547556 PMCID: PMC8175307 DOI: 10.1007/s00259-020-05118-w] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2020] [Accepted: 11/15/2020] [Indexed: 12/14/2022]
Abstract
PURPOSE In 2017, the Geneva Alzheimer's disease (AD) Biomarker Roadmap initiative adapted the framework of the systematic validation of oncological diagnostic biomarkers to AD biomarkers, with the aim to accelerate their development and implementation in clinical practice. With this work, we assess the maturity of [18F]flortaucipir PET and define its research priorities. METHODS The level of maturity of [18F]flortaucipir was assessed based on the AD Biomarker Roadmap. The framework assesses analytical validity (phases 1-2), clinical validity (phases 3-4), and clinical utility (phase 5). RESULTS The main aims of phases 1 (rationale for use) and 2 (discriminative ability) have been achieved. [18F]Flortaucipir binds with high affinity to paired helical filaments of tau and has favorable kinetic properties and excellent discriminative accuracy for AD. The majority of secondary aims of phase 2 were fully achieved. Multiple studies showed high correlations between ante-mortem [18F]flortaucipir PET and post-mortem tau (as assessed by histopathology), and also the effects of covariates on tracer binding are well studied. The aims of phase 3 (early detection ability) were only partially or preliminarily achieved, and the aims of phases 4 and 5 were not achieved. CONCLUSION Current literature provides partial evidence for clinical utility of [18F]flortaucipir PET. The aims for phases 1 and 2 were mostly achieved. Phase 3 studies are currently ongoing. Future studies including representative MCI populations and a focus on healthcare outcomes are required to establish full maturity of phases 4 and 5.
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Villemagne VL, Barkhof F, Garibotto V, Landau SM, Nordberg A, van Berckel BNM. Molecular Imaging Approaches in Dementia. Radiology 2021; 298:517-530. [PMID: 33464184 DOI: 10.1148/radiol.2020200028] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The increasing prevalence of dementia worldwide places a high demand on healthcare providers to perform a diagnostic work-up in relatively early stages of the disease, given that the pathologic process usually begins decades before symptoms are evident. Structural imaging is recommended to rule out other disorders and can only provide diagnosis in a late stage with limited specificity. Where PET imaging previously focused on the spatial pattern of hypometabolism, the past decade has seen the development of novel tracers to demonstrate characteristic protein abnormalities. Molecular imaging using PET/SPECT is able to show amyloid and tau deposition in Alzheimer disease and dopamine depletion in parkinsonian disorders starting decades before symptom onset. Novel tracers for neuroinflammation and synaptic density are being developed to further unravel the molecular pathologic characteristics of dementia disorders. In this article, the authors review the current status of established and emerging PET tracers in a diagnostic setting and also their value as prognostic markers in research studies and outcome measures for clinical trials in Alzheimer disease.
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Affiliation(s)
- Victor L Villemagne
- From the Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pa (V.L.V.); Department of Medicine, the University of Melbourne, Melbourne, Australia (V.L.V.); Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, VU University Medical Center, Amsterdam, the Netherlands (F.B., B.N.M.v.B.); UCL institutes of Neurology and Healthcare Engineering, London, England (F.B.); Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals and Laboratory of Neuroimaging and Innovative Molecular Tracers, Geneva University, Geneva, Switzerland (V.G.); Helen Wills Neuroscience Institute, University of California, Berkeley, Calif (S.M.L.); Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, Calif (S.M.L.); Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Karolinska Institutet, Stockholm, Sweden (A.N.); and Theme Aging, Karolinska University Hospital, Stockholm, Sweden (A.N.)
| | - Frederik Barkhof
- From the Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pa (V.L.V.); Department of Medicine, the University of Melbourne, Melbourne, Australia (V.L.V.); Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, VU University Medical Center, Amsterdam, the Netherlands (F.B., B.N.M.v.B.); UCL institutes of Neurology and Healthcare Engineering, London, England (F.B.); Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals and Laboratory of Neuroimaging and Innovative Molecular Tracers, Geneva University, Geneva, Switzerland (V.G.); Helen Wills Neuroscience Institute, University of California, Berkeley, Calif (S.M.L.); Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, Calif (S.M.L.); Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Karolinska Institutet, Stockholm, Sweden (A.N.); and Theme Aging, Karolinska University Hospital, Stockholm, Sweden (A.N.)
| | - Valentina Garibotto
- From the Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pa (V.L.V.); Department of Medicine, the University of Melbourne, Melbourne, Australia (V.L.V.); Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, VU University Medical Center, Amsterdam, the Netherlands (F.B., B.N.M.v.B.); UCL institutes of Neurology and Healthcare Engineering, London, England (F.B.); Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals and Laboratory of Neuroimaging and Innovative Molecular Tracers, Geneva University, Geneva, Switzerland (V.G.); Helen Wills Neuroscience Institute, University of California, Berkeley, Calif (S.M.L.); Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, Calif (S.M.L.); Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Karolinska Institutet, Stockholm, Sweden (A.N.); and Theme Aging, Karolinska University Hospital, Stockholm, Sweden (A.N.)
| | - Susan M Landau
- From the Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pa (V.L.V.); Department of Medicine, the University of Melbourne, Melbourne, Australia (V.L.V.); Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, VU University Medical Center, Amsterdam, the Netherlands (F.B., B.N.M.v.B.); UCL institutes of Neurology and Healthcare Engineering, London, England (F.B.); Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals and Laboratory of Neuroimaging and Innovative Molecular Tracers, Geneva University, Geneva, Switzerland (V.G.); Helen Wills Neuroscience Institute, University of California, Berkeley, Calif (S.M.L.); Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, Calif (S.M.L.); Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Karolinska Institutet, Stockholm, Sweden (A.N.); and Theme Aging, Karolinska University Hospital, Stockholm, Sweden (A.N.)
| | - Agneta Nordberg
- From the Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pa (V.L.V.); Department of Medicine, the University of Melbourne, Melbourne, Australia (V.L.V.); Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, VU University Medical Center, Amsterdam, the Netherlands (F.B., B.N.M.v.B.); UCL institutes of Neurology and Healthcare Engineering, London, England (F.B.); Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals and Laboratory of Neuroimaging and Innovative Molecular Tracers, Geneva University, Geneva, Switzerland (V.G.); Helen Wills Neuroscience Institute, University of California, Berkeley, Calif (S.M.L.); Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, Calif (S.M.L.); Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Karolinska Institutet, Stockholm, Sweden (A.N.); and Theme Aging, Karolinska University Hospital, Stockholm, Sweden (A.N.)
| | - Bart N M van Berckel
- From the Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pa (V.L.V.); Department of Medicine, the University of Melbourne, Melbourne, Australia (V.L.V.); Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, VU University Medical Center, Amsterdam, the Netherlands (F.B., B.N.M.v.B.); UCL institutes of Neurology and Healthcare Engineering, London, England (F.B.); Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals and Laboratory of Neuroimaging and Innovative Molecular Tracers, Geneva University, Geneva, Switzerland (V.G.); Helen Wills Neuroscience Institute, University of California, Berkeley, Calif (S.M.L.); Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, Calif (S.M.L.); Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Karolinska Institutet, Stockholm, Sweden (A.N.); and Theme Aging, Karolinska University Hospital, Stockholm, Sweden (A.N.)
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Drzezga A, Bischof GN, Giehl K, van Eimeren T. PET and SPECT Imaging of Neurodegenerative Diseases. Mol Imaging 2021. [DOI: 10.1016/b978-0-12-816386-3.00085-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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Chiotis K, Savitcheva I, Poulakis K, Saint-Aubert L, Wall A, Antoni G, Nordberg A. [ 18F]THK5317 imaging as a tool for predicting prospective cognitive decline in Alzheimer's disease. Mol Psychiatry 2021; 26:5875-5887. [PMID: 32616831 PMCID: PMC8758479 DOI: 10.1038/s41380-020-0815-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2019] [Revised: 05/09/2020] [Accepted: 06/08/2020] [Indexed: 11/09/2022]
Abstract
Cross-sectional studies have indicated potential for positron emission tomography (PET) in imaging tau pathology in Alzheimer's disease (AD); however, its prognostic utility remains unproven. In a longitudinal, multi-modal, prognostic study of cognitive decline, 20 patients with a clinical biomarker-based diagnosis in the AD spectrum (mild cognitive impairment or dementia and a positive amyloid-beta PET scan) were recruited from the Cognitive Clinic at Karolinska University Hospital. The participants underwent baseline neuropsychological assessment, PET imaging with [18F]THK5317, [11C]PIB and [18F]FDG, magnetic resonance imaging, and in a subgroup cerebrospinal fluid (CSF) sampling, with clinical follow-up after a median 48 months (interquartile range = 32:56). In total, 11 patients declined cognitively over time, while 9 remained cognitively stable. The accuracy of baseline [18F]THK5317 binding in temporal areas was excellent at predicting future cognitive decline (area under the receiver operating curve 0.84-1.00) and the biomarker levels were strongly associated with the rate of cognitive decline (β estimate -33.67 to -31.02, p < 0.05). The predictive accuracy of the other baseline biomarkers was poor (area under the receiver operating curve 0.58-0.77) and their levels were not associated with the rate of cognitive decline (β estimate -4.64 to 15.78, p > 0.05). Baseline [18F]THK5317 binding and CSF tau levels were more strongly associated with the MMSE score at follow-up than at baseline (p < 0.05). These findings support a temporal dissociation between tau deposition and cognitive impairment, and suggest that [18F]THK5317 predicts future cognitive decline better than other biomarkers. The use of imaging markers for tau pathology could prove useful for clinical prognostic assessment and screening before inclusion in relevant clinical trials.
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Affiliation(s)
- Konstantinos Chiotis
- grid.4714.60000 0004 1937 0626Nordberg Translational Molecular Imaging Lab, Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden ,grid.24381.3c0000 0000 9241 5705Theme Neurology, Karolinska University Hospital, Stockholm, Sweden
| | - Irina Savitcheva
- grid.24381.3c0000 0000 9241 5705Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Konstantinos Poulakis
- grid.4714.60000 0004 1937 0626Westman neuroimaging group, Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Laure Saint-Aubert
- grid.4714.60000 0004 1937 0626Nordberg Translational Molecular Imaging Lab, Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden ,grid.15781.3a0000 0001 0723 035XToulouse NeuroImaging Center, University of Toulouse, Inserm, UPS, Toulouse, France
| | - Anders Wall
- grid.8993.b0000 0004 1936 9457Section for Nuclear Medicine and PET, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Gunnar Antoni
- grid.8993.b0000 0004 1936 9457Department of Medicinal Chemistry, Uppsala University, Uppsala, Sweden
| | - Agneta Nordberg
- Nordberg Translational Molecular Imaging Lab, Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden. .,Theme Aging, Karolinska University Hospital, Stockholm, Sweden.
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Juengling FD, Allenbach G, Bruehlmeier M, Klaeser B, Wissmeyer MP, Garibotto V, Felbecker A, Georgescu D. Appropriate use criteria for dementia amyloid imaging in Switzerland - mini-review and statement on behalf of the Swiss Society of Nuclear Medicine and the Swiss Memory Clinics. Nuklearmedizin 2020; 60:7-9. [PMID: 33080626 DOI: 10.1055/a-1277-6014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
While FDG-PET imaging of the brain for the differential diagnosis of dementia has been covered by the compulsory health insurance in Switzerland for more than a decade, beta-amyloid-PET just recently has been added to the catalogue of procedures that have been cleared for routine use, provided that a set of appropriate use criteria (AUC) be followed. To provide guidance to dementia care practitioners, the Swiss Society of Nuclear Medicine and the Swiss Memory Clinics jointly report a mini-review on beta-amyloid-PET and discuss the AUC set into effect by the Swiss Federal Office of Public Health, as well as their application and limitations.
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Affiliation(s)
| | - Gilles Allenbach
- Centre hospitalier universitaire vaudois (CHUV), Lausanne, Switzerland
| | | | - Bernd Klaeser
- Cantonal hospital Winterthur, Winterthur, Switzerland
| | | | | | - Ansgar Felbecker
- Clinic for Neurology, Cantonal Hospital St. Gallen, St. Gallen, Switzerland
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Haller S, Montandon ML, Lilja J, Rodriguez C, Garibotto V, Herrmann FR, Giannakopoulos P. PET amyloid in normal aging: direct comparison of visual and automatic processing methods. Sci Rep 2020; 10:16665. [PMID: 33028945 PMCID: PMC7542434 DOI: 10.1038/s41598-020-73673-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 09/21/2020] [Indexed: 12/20/2022] Open
Abstract
Assessment of amyloid deposits is a critical step for the identification of Alzheimer disease (AD) signature in asymptomatic elders. Whether the different amyloid processing methods impacts on the quality of clinico-radiological correlations is still unclear. We directly compared in 155 elderly controls with extensive neuropsychological testing at baseline and 4.5 years follow-up three approaches: (i) operator-dependent standard visual reading, (ii) operator-independent automatic SUVR with four different reference regions, and (iii) novel operator and region of reference-independent automatic Aβ-index. The coefficient of variance was used to examine inter-individual variability for each processing method. Using visually-established amyloid positivity as the gold standard, the area under the receiver operating characteristic curve (ROC) was computed. Linear regression models were used to assess the association between changes in continuous cognitive score and amyloid uptake values. In SUVR analyses, the coefficient of variance varied from 1.718 to 1.762 according to the area of reference and was of − 3.045 for the Aβ-index method. Compared to the visual rating, Aβ-index method showed the largest area under the ROC curve [0.9568 (95% CI 0.9252, 0.98833)]. The best cut-off score was of − 0.3359 with sensitivity and specificity values of 0.97 and 0.83, respectively. Only the Aß-index was related to more severe decrement of cognitive performances [regression coefficient: 9.103 (95% CI 1.148, 17.058)]. The Aβ-index is considered as preferred option in asymptomatic elders, since it is operator-independent, avoids the selection of reference area, is closer to established visual scoring and correlates with the evolution of cognitive performances.
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Affiliation(s)
- Sven Haller
- CIRD Centre d'imagerie Rive Droite, Geneva, Switzerland. .,Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden. .,Faculty of Medicine, University of Geneva, Geneva, Switzerland.
| | - Marie-Louise Montandon
- Department of Rehabilitation and Geriatrics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland.,Department of Psychiatry, University of Geneva, Geneva, Switzerland
| | - Johan Lilja
- Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden.,Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden.,Hermes Medical Solutions, Stockholm, Sweden
| | - Cristelle Rodriguez
- Department of Psychiatry, University of Geneva, Geneva, Switzerland.,Division of Institutional Measures, Medical Direction, Geneva University Hospitals, Geneva, Switzerland
| | - Valentina Garibotto
- Faculty of Medicine, University of Geneva, Geneva, Switzerland.,Division of Nuclear Medicine and Molecular Imaging, Diagnostic Department, Geneva University Hospitals, Geneva, Switzerland
| | - François R Herrmann
- Department of Rehabilitation and Geriatrics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
| | - Panteleimon Giannakopoulos
- Department of Psychiatry, University of Geneva, Geneva, Switzerland.,Division of Institutional Measures, Medical Direction, Geneva University Hospitals, Geneva, Switzerland
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Grey zone amyloid burden affects memory function: the SCIENCe project. Eur J Nucl Med Mol Imaging 2020; 48:747-756. [PMID: 32888039 PMCID: PMC8036199 DOI: 10.1007/s00259-020-05012-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 08/20/2020] [Indexed: 12/24/2022]
Abstract
Purpose To determine thresholds for amyloid beta pathology and evaluate associations with longitudinal memory performance with the aim to identify a grey zone of early amyloid beta accumulation and investigate its clinical relevance. Methods We included 162 cognitively normal participants with subjective cognitive decline from the SCIENCe cohort (64 ± 8 years, 38% F, MMSE 29 ± 1). Each underwent a dynamic [18F] florbetapir PET scan, a T1-weighted MRI scan and longitudinal memory assessments (RAVLT delayed recall, n = 655 examinations). PET scans were visually assessed as amyloid positive/negative. Additionally, we calculated the mean binding potential (BPND) and standardized uptake value ratio (SUVr50–70) for an a priori defined composite region of interest. We determined six amyloid positivity thresholds using various data-driven methods (resulting thresholds: BPND 0.19/0.23/0.29; SUVr 1.28/1.34/1.43). We used Cohen’s kappa to analyse concordance between thresholds and visual assessment. Next, we used quantiles to divide the sample into two to five subgroups of equal numbers (median, tertiles, quartiles, quintiles), and operationalized a grey zone as the range between the thresholds (0.19–0.29 BPND/1.28–1.43 SUVr). We used linear mixed models to determine associations between thresholds and memory slope. Results As determined by visual assessment, 24% of 162 individuals were amyloid positive. Concordance with visual assessment was comparable but slightly higher for BPND thresholds (range kappa 0.65–0.70 versus 0.60–0.63). All thresholds predicted memory decline (range beta − 0.29 to − 0.21, all p < 0.05). Analyses in subgroups showed memory slopes gradually became steeper with higher amyloid load (all p for trend < 0.05). Participants with a low amyloid burden benefited from a practice effect (i.e. increase in memory), whilst high amyloid burden was associated with memory decline. Memory slopes of individuals in the grey zone were intermediate. Conclusion We provide evidence that not only high but also grey zone amyloid burden subtly impacts memory function. Therefore, in case a binary classification is required, we suggest using a relatively low threshold which includes grey zone amyloid pathology.
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Arabi H, Bortolin K, Ginovart N, Garibotto V, Zaidi H. Deep learning-guided joint attenuation and scatter correction in multitracer neuroimaging studies. Hum Brain Mapp 2020; 41:3667-3679. [PMID: 32436261 PMCID: PMC7416024 DOI: 10.1002/hbm.25039] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 04/15/2020] [Accepted: 05/08/2020] [Indexed: 12/25/2022] Open
Abstract
PET attenuation correction (AC) on systems lacking CT/transmission scanning, such as dedicated brain PET scanners and hybrid PET/MRI, is challenging. Direct AC in image-space, wherein PET images corrected for attenuation and scatter are synthesized from nonattenuation corrected PET (PET-nonAC) images in an end-to-end fashion using deep learning approaches (DLAC) is evaluated for various radiotracers used in molecular neuroimaging studies. One hundred eighty brain PET scans acquired using 18 F-FDG, 18 F-DOPA, 18 F-Flortaucipir (targeting tau pathology), and 18 F-Flutemetamol (targeting amyloid pathology) radiotracers (40 + 5, training/validation + external test, subjects for each radiotracer) were included. The PET data were reconstructed using CT-based AC (CTAC) to generate reference PET-CTAC and without AC to produce PET-nonAC images. A deep convolutional neural network was trained to generate PET attenuation corrected images (PET-DLAC) from PET-nonAC. The quantitative accuracy of this approach was investigated separately for each radiotracer considering the values obtained from PET-CTAC images as reference. A segmented AC map (PET-SegAC) containing soft-tissue and background air was also included in the evaluation. Quantitative analysis of PET images demonstrated superior performance of the DLAC approach compared to SegAC technique for all tracers. Despite the relatively low quantitative bias observed when using the DLAC approach, this approach appears vulnerable to outliers, resulting in noticeable local pseudo uptake and false cold regions. Direct AC in image-space using deep learning demonstrated quantitatively acceptable performance with less than 9% absolute SUV bias for the four different investigated neuroimaging radiotracers. However, this approach is vulnerable to outliers which result in large local quantitative bias.
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Affiliation(s)
- Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical ImagingGeneva University HospitalGenevaSwitzerland
| | - Karin Bortolin
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical ImagingGeneva University HospitalGenevaSwitzerland
| | - Nathalie Ginovart
- Department of PsychiatryGeneva UniversityGenevaSwitzerland
- Department of Basic NeurosciencesGeneva UniversityGenevaSwitzerland
| | - Valentina Garibotto
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical ImagingGeneva University HospitalGenevaSwitzerland
- Geneva Neuroscience CenterGeneva UniversityGenevaSwitzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical ImagingGeneva University HospitalGenevaSwitzerland
- Geneva Neuroscience CenterGeneva UniversityGenevaSwitzerland
- Department of Nuclear Medicine and Molecular ImagingUniversity of Groningen, University Medical Center GroningenGroningenNetherlands
- Department of Nuclear MedicineUniversity of Southern DenmarkOdenseDenmark
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Advantages and Pitfalls in Fluid Biomarkers for Diagnosis of Alzheimer's Disease. J Pers Med 2020; 10:jpm10030063. [PMID: 32708853 PMCID: PMC7563364 DOI: 10.3390/jpm10030063] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 07/04/2020] [Accepted: 07/06/2020] [Indexed: 01/08/2023] Open
Abstract
Alzheimer’s disease (AD) is a commonly occurring neurodegenerative disease in the advanced-age population, with a doubling of prevalence for each 5 years of age above 60 years. In the past two decades, there has been a sustained effort to find suitable biomarkers that may not only aide with the diagnosis of AD early in the disease process but also predict the onset of the disease in asymptomatic individuals. Current diagnostic evidence is supportive of some biomarker candidates isolated from cerebrospinal fluid (CSF), including amyloid beta peptide (Aβ), total tau (t-tau), and phosphorylated tau (p-tau) as being involved in the pathophysiology of AD. However, there are a few biomarkers that have been shown to be helpful, such as proteomic, inflammatory, oral, ocular and olfactory in the early detection of AD, especially in the individuals with mild cognitive impairment (MCI). To date, biomarkers are collected through invasive techniques, especially CSF from lumbar puncture; however, non-invasive (radio imaging) methods are used in practice to diagnose AD. In order to reduce invasive testing on the patients, present literature has highlighted the potential importance of biomarkers in blood to assist with diagnosing AD.
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Tiiman A, Jelić V, Jarvet J, Järemo P, Bogdanović N, Rigler R, Terenius L, Gräslund A, Vukojević V. Amyloidogenic Nanoplaques in Blood Serum of Patients with Alzheimer's Disease Revealed by Time-Resolved Thioflavin T Fluorescence Intensity Fluctuation Analysis. J Alzheimers Dis 2020; 68:571-582. [PMID: 30814355 PMCID: PMC6484272 DOI: 10.3233/jad-181144] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
BACKGROUND Biomarkers are central to current research on molecular mechanisms underlying Alzheimer's disease (AD). Their further development is of paramount importance for understanding pathophysiological processes that eventually lead to disease onset. Biomarkers are also crucial for early disease detection, before clinical manifestation, and for development of new disease modifying therapies. OBJECTIVE The overall aim of this work is to develop a minimally invasive method for fast, ultra-sensitive and cost-effective detection of structurally modified peptide/protein self-assemblies in the peripheral blood and in other biological fluids. Specifically, we focus here on using this method to detect structured amyloidogenic oligomeric aggregates in the blood serum of apparently healthy individuals and patients in early AD stage, and measure their concentration and size. METHODS Time-resolved detection of Thioflavin T (ThT) fluorescence intensity fluctuations in a sub-femtoliter observation volume element was used to identify in blood serum ThT-active structured amyloidogenic oligomeric aggregates, hereafter called nanoplaques, and measure with single-particle sensitivity their concentration and size. RESULTS The concentration and size of structured amyloidogenic nanoplaques are significantly higher in the blood serum of individuals diagnosed with AD than in control subjects. CONCLUSION A new method with the ultimate, single-particle sensitivity was successfully developed. The proposed approach neither relies on the use of immune-based probes, nor on the use of radiotracers, signal-amplification or protein separation techniques, and provides a minimally invasive test for fast and cost-effective early determination of structurally modified peptides/proteins in the peripheral blood, as shown here, but also in other biological fluids.
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Affiliation(s)
- Ann Tiiman
- Department of Clinical Neuroscience (CNS), Center for Molecular Medicine CMM L8:01, Karolinska Institutet, Stockholm, Sweden
| | - Vesna Jelić
- Department of Neurobiology, Care Sciences and Society (NVS), Division of Clinical Geriatrics, Karolinska Institutet, Huddinge, Sweden
| | - Jüri Jarvet
- Department of Biochemistry and Biophysics, Arrhenius Laboratories, Stockholm University, Stockholm, Sweden.,The National Institute of Chemical Physics and Biophysics, Tallinn, Estonia
| | - Petter Järemo
- Department of Internal Medicine, The Vrinnevi Hospital, Norrköping, Sweden
| | - Nenad Bogdanović
- Department of Neurobiology, Care Sciences and Society (NVS), Division of Clinical Geriatrics, Karolinska Institutet, Huddinge, Sweden.,Department of Geriatric Medicine, University of Oslo, Oslo, Norway
| | - Rudolf Rigler
- Department of Medical Biochemistry and Biophysics (MBB), Karolinska Institutet, Stockholm, Sweden
| | - Lars Terenius
- Department of Clinical Neuroscience (CNS), Center for Molecular Medicine CMM L8:01, Karolinska Institutet, Stockholm, Sweden
| | - Astrid Gräslund
- Department of Biochemistry and Biophysics, Arrhenius Laboratories, Stockholm University, Stockholm, Sweden
| | - Vladana Vukojević
- Department of Clinical Neuroscience (CNS), Center for Molecular Medicine CMM L8:01, Karolinska Institutet, Stockholm, Sweden
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Hanseeuw BJ, Malotaux V, Dricot L, Quenon L, Sznajer Y, Cerman J, Woodard JL, Buckley C, Farrar G, Ivanoiu A, Lhommel R. Defining a Centiloid scale threshold predicting long-term progression to dementia in patients attending the memory clinic: an [ 18F] flutemetamol amyloid PET study. Eur J Nucl Med Mol Imaging 2020; 48:302-310. [PMID: 32601802 PMCID: PMC7835306 DOI: 10.1007/s00259-020-04942-4] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Accepted: 06/22/2020] [Indexed: 12/14/2022]
Abstract
PURPOSE To evaluate cerebral amyloid-β(Aβ) pathology in older adults with cognitive complaints, visual assessment of PET images is approved as the routine method for image interpretation. In research studies however, Aβ-PET semi-quantitative measures are associated with greater risk of progression to dementia; but until recently, these measures lacked standardization. Therefore, the Centiloid scale, providing standardized Aβ-PET semi-quantitation, was recently validated. We aimed to determine the predictive values of visual assessments and Centiloids in non-demented patients, using long-term progression to dementia as our standard of truth. METHODS One hundred sixty non-demented participants (age, 54-86) were enrolled in a monocentric [18F] flutemetamol Aβ-PET study. Flutemetamol images were interpreted visually following the manufacturers recommendations. SUVr values were converted to the Centiloid scale using the GAAIN guidelines. Ninety-eight persons were followed until dementia diagnosis or were clinically stable for a median of 6 years (min = 4.0; max = 8.0). Twenty-five patients with short follow-up (median = 2.0 years; min = 0.8; max = 3.9) and 37 patients with no follow-up were excluded. We computed ROC curves predicting subsequent dementia using baseline PET data and calculated negative (NPV) and positive (PPV) predictive values. RESULTS In the 98 participants with long follow-up, Centiloid = 26 provided the highest overall predictive value = 87% (NPV = 85%, PPV = 88%). Visual assessment corresponded to Centiloid = 40, which predicted dementia with an overall predictive value = 86% (NPV = 81%, PPV = 92%). Inclusion of the 25 patients who only had a 2-year follow-up decreased the PPV = 67% (NPV = 88%), reflecting the many positive cases that did not progress to dementia after short follow-ups. CONCLUSION A Centiloid threshold = 26 optimally predicts progression to dementia 6 years after PET. Visual assessment provides similar predictive value, with higher specificity and lower sensitivity. TRIAL REGISTRATION Eudra-CT number: 2011-001756-12.
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Affiliation(s)
- Bernard J Hanseeuw
- Institute of Neuroscience, Université Catholique de Louvain, Brussels, Belgium. .,Neurology Department, Saint-Luc University Hospital, Av. Hippocrate, 10, 1200, Brussels, Belgium. .,Gordon Center for Medical Imaging, Radiology Department, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Vincent Malotaux
- Institute of Neuroscience, Université Catholique de Louvain, Brussels, Belgium
| | - Laurence Dricot
- Institute of Neuroscience, Université Catholique de Louvain, Brussels, Belgium
| | - Lisa Quenon
- Neurology Department, Saint-Luc University Hospital, Av. Hippocrate, 10, 1200, Brussels, Belgium
| | - Yves Sznajer
- Genetics Department, Saint-Luc University Hospital, Brussels, Belgium
| | - Jiri Cerman
- Department of Neurology, Charles University, 2nd Faculty of Medicine and Motol University Hospital, Prague, Czech Republic
| | - John L Woodard
- Institute of Neuroscience, Université Catholique de Louvain, Brussels, Belgium.,Department of Psychology, Wayne State University, Detroit, MI, USA
| | | | | | - Adrian Ivanoiu
- Institute of Neuroscience, Université Catholique de Louvain, Brussels, Belgium.,Neurology Department, Saint-Luc University Hospital, Av. Hippocrate, 10, 1200, Brussels, Belgium
| | - Renaud Lhommel
- Institute of Neuroscience, Université Catholique de Louvain, Brussels, Belgium.,Nuclear Medicine Department, Saint-Luc University Hospital, Brussels, Belgium.,Institute of Experimental and Clinical Research, Université Catholique de Louvain, Brussels, Belgium
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Uzuegbunam BC, Librizzi D, Hooshyar Yousefi B. PET Radiopharmaceuticals for Alzheimer's Disease and Parkinson's Disease Diagnosis, the Current and Future Landscape. Molecules 2020; 25:E977. [PMID: 32098280 PMCID: PMC7070523 DOI: 10.3390/molecules25040977] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2020] [Revised: 02/17/2020] [Accepted: 02/17/2020] [Indexed: 02/06/2023] Open
Abstract
Ironically, population aging which is considered a public health success has been accompanied by a myriad of new health challenges, which include neurodegenerative disorders (NDDs), the incidence of which increases proportionally to age. Among them, Alzheimer's disease (AD) and Parkinson's disease (PD) are the most common, with the misfolding and the aggregation of proteins being common and causal in the pathogenesis of both diseases. AD is characterized by the presence of hyperphosphorylated τ protein (tau), which is the main component of neurofibrillary tangles (NFTs), and senile plaques the main component of which is β-amyloid peptide aggregates (Aβ). The neuropathological hallmark of PD is α-synuclein aggregates (α-syn), which are present as insoluble fibrils, the primary structural component of Lewy body (LB) and neurites (LN). An increasing number of non-invasive PET examinations have been used for AD, to monitor the pathological progress (hallmarks) of disease. Notwithstanding, still the need for the development of novel detection tools for other proteinopathies still remains. This review, although not exhaustively, looks at the timeline of the development of existing tracers used in the imaging of Aβ and important moments that led to the development of these tracers.
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Affiliation(s)
| | - Damiano Librizzi
- Department of Nuclear Medicine, Philipps-University of Marburg, 35043 Marburg, Germany;
| | - Behrooz Hooshyar Yousefi
- Nuclear Medicine Department, and Neuroimaging Center, Technical University of Munich, 81675 Munich, Germany;
- Department of Nuclear Medicine, Philipps-University of Marburg, 35043 Marburg, Germany;
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Kaneta T. PET and SPECT imaging of the brain: a review on the current status of nuclear medicine in Japan. Jpn J Radiol 2020; 38:343-357. [DOI: 10.1007/s11604-019-00901-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Accepted: 10/31/2019] [Indexed: 01/07/2023]
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Beyer L, Brendel M, Scheiwein F, Sauerbeck J, Hosakawa C, Alberts I, Shi K, Bartenstein P, Ishii K, Seibyl J, Cumming P, Rominger A. Improved Risk Stratification for Progression from Mild Cognitive Impairment to Alzheimer's Disease with a Multi-Analytical Evaluation of Amyloid-β Positron Emission Tomography. J Alzheimers Dis 2020; 74:101-112. [PMID: 31985461 DOI: 10.3233/jad-190818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Amyloid-β (Aβ) accumulation in brain of patients with suspected Alzheimer's disease (AD) can be assessed by positron emission tomography (PET) in vivo. While visual classification prevails in the clinical routine, semiquantitative PET analyses may enable more reliable evaluation of cases with a visually uncertain, borderline Aβ accumulation. OBJECTIVE We evaluated different analysis approaches (visual/semiquantitative) to find the most accurate and sensitive interpretation of Aβ-PET for predicting risk of progression from mild cognitive impairment (MCI) to AD. METHODS Based on standard uptake value (SUV) ratios of a cortical-composite volume of interest of 18F-AV45-PET from MCI subjects (n = 396, ADNI database), we compared three different reference region (cerebellar grey matter, CBL; brainstem, BST; white matter, WM) normalizations and the visual read by receiver operator characteristics for calculating a hazard ratio (HR) for progression to Alzheimer's disease dementia (ADD). RESULTS During a mean follow-up time of 45.6±13.0 months, 28% of the MCI cases (110/396) converted to ADD. Among the tested methods, the WM reference showed best discriminatory power and progression-risk stratification (HRWM of 4.4 [2.6-7.6]), but the combined results of the visual and semiquantitative analysis with all three reference regions showed an even higher discriminatory power. CONCLUSION A multi-analytical composite of visual and semiquantitative reference tissue analyses of 18F-AV45-PET gave improved risk stratification for progression from MCI to ADD relative to performance of single read-outs. This optimized approach is of special interest for prospective treatment trials, which demand a high accuracy.
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Affiliation(s)
- Leonie Beyer
- Department of Nuclear Medicine, University Hospital of Munich, LMU, Munich, Germany
| | - Matthias Brendel
- Department of Nuclear Medicine, University Hospital of Munich, LMU, Munich, Germany
| | - Franziska Scheiwein
- Department of Nuclear Medicine, University Hospital of Munich, LMU, Munich, Germany
| | - Julia Sauerbeck
- Department of Nuclear Medicine, University Hospital of Munich, LMU, Munich, Germany
| | - Chisa Hosakawa
- Department of Radiology, Kindai University, Osaka, Japan
| | - Ian Alberts
- Department of Nuclear Medicine, Inselspital, University Hospital Bern, Bern, Switzerland
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital, University Hospital Bern, Bern, Switzerland
| | - Peter Bartenstein
- Department of Nuclear Medicine, University Hospital of Munich, LMU, Munich, Germany
| | - Kazunari Ishii
- Department of Radiology, Kindai University, Osaka, Japan
| | | | - Paul Cumming
- Department of Nuclear Medicine, Inselspital, University Hospital Bern, Bern, Switzerland.,School of Psychology and Counseling and IHBI, Queensland University of Technology, Brisbane, Australia
| | - Axel Rominger
- Department of Nuclear Medicine, Inselspital, University Hospital Bern, Bern, Switzerland
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Haller S, Montandon ML, Rodriguez C, Garibotto V, Herrmann FR, Giannakopoulos P. Hippocampal Volume Loss, Brain Amyloid Accumulation, and APOE Status in Cognitively Intact Elderly Subjects. NEURODEGENER DIS 2019; 19:139-147. [PMID: 31846965 DOI: 10.1159/000504302] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Accepted: 10/21/2019] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Hippocampal volume loss (HVL), PET-documented brain amyloid accumulation, and APOE-ε4 status are predictive biomarkers of the transition from mild cognitive impairment to Alzheimer disease (AD). In asymptomatic cases, the role of these biomarkers remains ambiguous. In contrast to the idea that HVL occurs in late phases of neurodegeneration, recent contributions indicate that it might occur before abnormal amyloid PET occurrence in elderly subjects and that its severity could be only marginally related to APOE variants. Using a longitudinal design, we examined the determinants of HVL in our sample, i.e., brain amyloid burden and the presence of APOE-ε4, and made a longitudinal assessment of cognitive functions. METHODS We performed a 4.5-year longitudinal study on 81 elderly community dwellers (all right-handed;, 48 (59.3%) women; mean age 73.7 ± 3.7 years) including MRI at baseline and follow-up, PET amyloid during follow-up, neuropsychological assessment at 18 and 54 months, and APOE genotyping. All cases were assessed using a continuous cognitive score (CCS) that took into account the global evolution of neuropsychological performance. Linear regression models were used to identify predictors of HVL. RESULTS There was a negative association between the CCS and HVL bilaterally. In multivariate models adjusting for demographic variables, the presence of APOE-ε4 was related to increased HVL bilaterally. A trend of significance was observed with respect to the impact of amyloid positivity on HVL in the left hemisphere. No significant interaction was found between amyloid positivity and the APOE-ε4 allele. CONCLUSION The progressive decrement of neuropsychological performance is associated with HVL long before the emergence of clinically overt symptoms. In this cohort of healthy individuals, the presence of the APOE-ε4 allele was shown to be an independent predictor of worst hippocampal integrity in asymptomatic cases independently of amyloid positivity.
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Affiliation(s)
- Sven Haller
- CIRD Centre d'Imagerie Rive Droite, Geneva, Switzerland, .,Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden, .,Faculty of Medicine, University of Geneva, Geneva, Switzerland,
| | - Marie-Louise Montandon
- Department of Rehabilitation and Geriatrics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland.,Department of Psychiatry, University of Geneva, Geneva, Switzerland
| | - Cristelle Rodriguez
- Department of Psychiatry, University of Geneva, Geneva, Switzerland.,Division of Institutional Measures, Medical Direction, Geneva University Hospitals, Geneva, Switzerland
| | - Valentina Garibotto
- Faculty of Medicine, University of Geneva, Geneva, Switzerland.,Division of Nuclear Medicine and Molecular Imaging, Diagnostic Department, Geneva University Hospitals, Geneva, Switzerland
| | - François R Herrmann
- Department of Rehabilitation and Geriatrics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
| | - Panteleimon Giannakopoulos
- Department of Psychiatry, University of Geneva, Geneva, Switzerland.,Division of Institutional Measures, Medical Direction, Geneva University Hospitals, Geneva, Switzerland
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Park JC, Han SH, Lee H, Jeong H, Byun MS, Bae J, Kim H, Lee DY, Yi D, Shin SA, Kim YK, Hwang D, Lee SW, Mook-Jung I. Prognostic plasma protein panel for Aβ deposition in the brain in Alzheimer’s disease. Prog Neurobiol 2019; 183:101690. [DOI: 10.1016/j.pneurobio.2019.101690] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 07/02/2019] [Accepted: 08/28/2019] [Indexed: 12/15/2022]
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de Wilde A, van der Flier WM, Pelkmans W, Bouwman F, Verwer J, Groot C, van Buchem MM, Zwan M, Ossenkoppele R, Yaqub M, Kunneman M, Smets EMA, Barkhof F, Lammertsma AA, Stephens A, van Lier E, Biessels GJ, van Berckel BN, Scheltens P. Association of Amyloid Positron Emission Tomography With Changes in Diagnosis and Patient Treatment in an Unselected Memory Clinic Cohort: The ABIDE Project. JAMA Neurol 2019; 75:1062-1070. [PMID: 29889941 DOI: 10.1001/jamaneurol.2018.1346] [Citation(s) in RCA: 93] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Importance Previous studies have evaluated the diagnostic effect of amyloid positron emission tomography (PET) in selected research cohorts. However, these research populations do not reflect daily practice, thus hampering clinical implementation of amyloid imaging. Objective To evaluate the association of amyloid PET with changes in diagnosis, diagnostic confidence, treatment, and patients' experiences in an unselected memory clinic cohort. Design, Setting, and Participants Amyloid PET using fluoride-18 florbetaben was offered to 866 patients who visited the tertiary memory clinic at the VU University Medical Center between January 2015 and December 2016 as part of their routine diagnostic dementia workup. Of these patients, 476 (55%) were included, 32 (4%) were excluded, and 358 (41%) did not participate. To enrich this sample, 31 patients with mild cognitive impairment from the University Medical Center Utrecht memory clinic were included. For each patient, neurologists determined a preamyloid and postamyloid PET diagnosis that existed of both a clinical syndrome (dementia, mild cognitive impairment, or subjective cognitive decline) and a suspected etiology (Alzheimer disease [AD] or non-AD), with a confidence level ranging from 0% to 100%. In addition, the neurologist determined patient treatment in terms of ancillary investigations, medication, and care. Each patient received a clinical follow-up 1 year after being scanned. Main Outcomes and Measures Primary outcome measures were post-PET changes in diagnosis, diagnostic confidence, and patient treatment. Results Of the 507 patients (mean [SD] age, 65 (8) years; 201 women [39%]; mean [SD] Mini-Mental State Examination score, 25 [4]), 164 (32%) had AD dementia, 70 (14%) non-AD dementia, 114 (23%) mild cognitive impairment, and 159 (31%) subjective cognitive decline. Amyloid PET results were positive for 242 patients (48%). The suspected etiology changed for 125 patients (25%) after undergoing amyloid PET, more often due to a negative (82 of 265 [31%]) than a positive (43 of 242 [18%]) PET result (P < .01). Post-PET changes in suspected etiology occurred more frequently in patients older (>65 years) than younger (<65 years) than the typical age at onset of 65 years (74 of 257 [29%] vs 51 of 250 [20%]; P < .05). Mean diagnostic confidence (SD) increased from 80 (13) to 89 (13%) (P < .001). In 123 patients (24%), there was a change in patient treatment post-PET, mostly related to additional investigations and therapy. Conclusions and Relevance This prospective diagnostic study provides a bridge between validating amyloid PET in a research setting and implementing this diagnostic tool in daily clinical practice. Both amyloid-positive and amyloid-negative results had substantial associations with changes in diagnosis and treatment, both in patients with and without dementia.
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Affiliation(s)
- Arno de Wilde
- Department of Neurology & Alzheimer Center, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands
| | - Wiesje M van der Flier
- Department of Neurology & Alzheimer Center, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands.,Department of Epidemiology & Biostatistics, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands
| | - Wiesje Pelkmans
- Department of Neurology & Alzheimer Center, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands
| | - Femke Bouwman
- Department of Neurology & Alzheimer Center, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands
| | - Jurre Verwer
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Colin Groot
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands
| | - Marieke M van Buchem
- Department of Neurology & Alzheimer Center, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands
| | - Marissa Zwan
- Department of Neurology & Alzheimer Center, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands
| | - Rik Ossenkoppele
- Department of Neurology & Alzheimer Center, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands.,Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands
| | - Maqsood Yaqub
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands
| | - Marleen Kunneman
- Department of Medical Psychology, Amsterdam Neuroscience, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Ellen M A Smets
- Department of Medical Psychology, Amsterdam Neuroscience, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Frederik Barkhof
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands.,Institutes of Neurology and Healthcare Engineering, University College London, London, England
| | - Adriaan A Lammertsma
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands
| | | | | | - Geert Jan Biessels
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Bart N van Berckel
- Department of Neurology & Alzheimer Center, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands.,Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands
| | - Philip Scheltens
- Department of Neurology & Alzheimer Center, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands
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Piri R, Naghavi-Behzad M, Gerke O, Høilund-Carlsen PF, Vafaee MS. Investigations of possible links between Alzheimer’s disease and type 2 diabetes mellitus by positron emission tomography: a systematic review. Clin Transl Imaging 2019. [DOI: 10.1007/s40336-019-00339-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Ramusino MC, Garibotto V, Bacchin R, Altomare D, Dodich A, Assal F, Mendes A, Costa A, Tinazzi M, Morbelli SD, Bauckneht M, Picco A, Dottorini ME, Tranfaglia C, Farotti L, Salvadori N, Moretti D, Savelli G, Tarallo A, Nobili F, Parapini M, Cavaliere C, Salvatore E, Salvatore M, Boccardi M, Frisoni GB. Incremental value of amyloid-PET versus CSF in the diagnosis of Alzheimer's disease. Eur J Nucl Med Mol Imaging 2019; 47:270-280. [PMID: 31388720 DOI: 10.1007/s00259-019-04466-6] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2019] [Accepted: 07/26/2019] [Indexed: 10/26/2022]
Abstract
PURPOSE To compare the incremental diagnostic value of amyloid-PET and CSF (Aβ42, tau, and phospho-tau) in AD diagnosis in patients with mild cognitive impairment (MCI) or mild dementia, in order to improve the definition of diagnostic algorithm. METHODS Two independent dementia experts provided etiological diagnosis and relative diagnostic confidence in 71 patients on 3 rounds, based on (1) clinical, neuropsychological, and structural MRI information alone; (2) adding one biomarker (CSF amyloid and tau levels or amyloid-PET with a balanced randomized design); and (3) adding the other biomarker. RESULTS Among patients with a pre-biomarker diagnosis of AD, negative PET induced significantly more diagnostic changes than amyloid-negative CSF at both rounds 2 (CSF 67%, PET 100%, P = 0.028) and 3 (CSF 0%; PET 78%, P < 0.001); PET induced a diagnostic confidence increase significantly higher than CSF on both rounds 2 and 3. CONCLUSIONS Amyloid-PET should be prioritized over CSF biomarkers in the diagnostic workup of patients investigated for suspected AD, as it provides greater changes in diagnosis and diagnostic confidence. TRIAL REGISTRATION EudraCT no.: 2014-005389-31.
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Affiliation(s)
- Matteo Cotta Ramusino
- Memory Clinic and LANVIE -Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Chemin du Petit Bel-Air 2, Bâtiment Voirons, CH1225, Geneva, Switzerland. .,Center for Cognitive and Behavioral Disorders, IRCCS Mondino Foundation and Dept of Brain and Behavior, University of Pavia, 27100, Pavia, Italy.
| | - Valentina Garibotto
- NIMTlab, Neuroimaging and Innovative Molecular Tracers Laboratory, University of Geneva, CH1205, Geneva, Switzerland.,Division of Nuclear Medicine, Geneva University Hospitals, CH1205, Geneva, Switzerland
| | - Ruggero Bacchin
- Memory Clinic and LANVIE -Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Chemin du Petit Bel-Air 2, Bâtiment Voirons, CH1225, Geneva, Switzerland.,Dept of Neurosciences, Biomedicine and Movement Sciences, Section of Neurology, University of Verona, 34134, Verona, Italy
| | - Daniele Altomare
- Memory Clinic and LANVIE -Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Chemin du Petit Bel-Air 2, Bâtiment Voirons, CH1225, Geneva, Switzerland
| | - Alessandra Dodich
- NIMTlab, Neuroimaging and Innovative Molecular Tracers Laboratory, University of Geneva, CH1205, Geneva, Switzerland
| | - Frederic Assal
- Memory Clinic and LANVIE -Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Chemin du Petit Bel-Air 2, Bâtiment Voirons, CH1225, Geneva, Switzerland
| | - Aline Mendes
- Memory Clinic and LANVIE -Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Chemin du Petit Bel-Air 2, Bâtiment Voirons, CH1225, Geneva, Switzerland
| | - Alfredo Costa
- Center for Cognitive and Behavioral Disorders, IRCCS Mondino Foundation and Dept of Brain and Behavior, University of Pavia, 27100, Pavia, Italy
| | - Michele Tinazzi
- Dept of Neurosciences, Biomedicine and Movement Sciences, Section of Neurology, University of Verona, 34134, Verona, Italy
| | - Silvia D Morbelli
- Nuclear Medicine, Dept of Health Sciences (DISSAL), University of Genoa and IRCCS AOU San Martino-IST, 16132, Genoa, Italy
| | - Matteo Bauckneht
- Nuclear Medicine, Dept of Health Sciences (DISSAL), University of Genoa and IRCCS AOU San Martino-IST, 16132, Genoa, Italy
| | - Agnese Picco
- Clinical Neurology, Dept. of Neuroscience (DINOGMI), University of Genoa, 16126, Genoa, Italy
| | - Massimo E Dottorini
- Nuclear Medicine Division, "S. Maria della Misericordia" Hospital, 06129, Perugia, Italy
| | - Cristina Tranfaglia
- Nuclear Medicine Division, "S. Maria della Misericordia" Hospital, 06129, Perugia, Italy
| | - Lucia Farotti
- Center for Memory Disturbances, Laboratory of Clinical Neurochemistry, University of Perugia, 06123, Perugia, Italy
| | - Nicola Salvadori
- Center for Memory Disturbances, Laboratory of Clinical Neurochemistry, University of Perugia, 06123, Perugia, Italy
| | - Davide Moretti
- Alzheimer's Disease Operative Unit, IRCCS S, Giovanni di Dio Fatebenefratelli, 25125, Brescia, Italy
| | - Giordano Savelli
- Nuclear Medicine Division, Fondazione Poliambulanza Istituto Ospedaliero, 25124, Brescia, Italy
| | - Anna Tarallo
- LANE-Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS San Giovanni di Dio Fatebenefratelli, 25125, Brescia, Italy
| | - Flavio Nobili
- Clinical Neurology, Dept. of Neuroscience (DINOGMI), University of Genoa, 16126, Genoa, Italy.,IRCCS Ospedale Policlinico San Martino, 16132, Genoa, Italy
| | - Maura Parapini
- Memory Clinic and LANVIE -Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Chemin du Petit Bel-Air 2, Bâtiment Voirons, CH1225, Geneva, Switzerland
| | | | | | | | - Marina Boccardi
- Memory Clinic and LANVIE -Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Chemin du Petit Bel-Air 2, Bâtiment Voirons, CH1225, Geneva, Switzerland.,LANE-Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS San Giovanni di Dio Fatebenefratelli, 25125, Brescia, Italy
| | - Giovanni B Frisoni
- Memory Clinic and LANVIE -Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Chemin du Petit Bel-Air 2, Bâtiment Voirons, CH1225, Geneva, Switzerland
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van Maurik IS, van der Kall LM, de Wilde A, Bouwman FH, Scheltens P, van Berckel BNM, Berkhof J, van der Flier WM. Added value of amyloid PET in individualized risk predictions for MCI patients. ALZHEIMER'S & DEMENTIA: DIAGNOSIS, ASSESSMENT & DISEASE MONITORING 2019; 11:529-537. [PMID: 31388557 PMCID: PMC6667768 DOI: 10.1016/j.dadm.2019.04.011] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Introduction To construct a prognostic model based on amyloid positron emission tomography (PET) to predict clinical progression in individual patients with mild cognitive impairment (MCI). Methods We included 411 MCI patients from the Alzheimer's Disease Neuroimaging Initiative. Prognostic models were constructed with Cox regression with demographics, magnetic resonance imaging, and/or amyloid PET to predict progression to Alzheimer's disease dementia. The models were validated in the Amsterdam Dementia Cohort. Results The combined model (Harrell's C = 0.82 [0.78–0.86]) was significantly superior to demographics (β = 0.100, P < .001), magnetic resonance imaging (β = 0.037, P = .011), and PET only models (β = 0.053, P = .003).The models can be used to calculate individualized risk, for example, a female MCI patient (age = 60, APOE ε4 positive, Mini-Mental State Examination = 25, hippocampal volume = 5.8 cm3, amyloid PET positive) has 35% (19–57) risk in one year and 85% (64–97) risk in three years. Model performances in the Amsterdam Dementia Cohort were reasonable. Discussion The present study facilitates the interpretation of an amyloid PET result in the context of a patient's own characteristics and clinical assessment. Our models facilitate amyloid PET–based prognosis in light of patient characteristics. Amyloid PET is the key player in our prognostic models. Our models are easy to use and can guide clinical decision making.
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Affiliation(s)
- Ingrid S van Maurik
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.,Department of Epidemiology and Biostatistics, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Laura M van der Kall
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Arno de Wilde
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Femke H Bouwman
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Philip Scheltens
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Bart N M van Berckel
- Department of Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Johannes Berkhof
- Department of Epidemiology and Biostatistics, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Wiesje M van der Flier
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.,Department of Epidemiology and Biostatistics, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
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Lage C, Suarez AG, Pozueta A, Riancho J, Kazimierczak M, Bravo M, Jimenez Bonilla J, de Arcocha Torres M, Quirce R, Banzo I, Vazquez-Higuera JL, Rabinovici GD, Rodriguez-Rodriguez E, Sánchez-Juan P. Utility of Amyloid and FDG-PET in Clinical Practice: Differences Between Secondary and Tertiary Care Memory Units. J Alzheimers Dis 2019; 63:1025-1033. [PMID: 29710706 DOI: 10.3233/jad-170985] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The clinical utility of amyloid positron emission tomography (PET) has not been fully established. Our aim was to evaluate the effect of amyloid imaging on clinical decision making in a secondary care unit and compare our results with a previous study in a tertiary center following the same methods. We reviewed retrospectively 151 cognitively impaired patients who underwent amyloid (Pittsburgh compound B [PiB]) PET and were evaluated clinically before and after the scan in a secondary care unit. One hundred and fifty concurrently underwent fluorodeoxyglucose (FDG)-PET. We assessed changes between the pre- and post-PET clinical diagnosis and Alzheimer's disease treatment plan. The association between PiB/FDG results and changes in management was evaluated using χ2 and multivariate logistic regression. Concordance between classification based on scan readings and baseline diagnosis was 66% for PiB and 47% for FDG. The primary diagnosis changed after PET in 17.2% of cases. When examined independently, discordant PiB and discordant FDG were both associated with diagnostic change (p < 0.0001). However, when examined together in a multivariate logistic regression, only discordant PiB remained significant (p = 0.0002). Changes in treatment were associated with concordant PiB (p = 0.009) while FDG had no effect on treatment decisions. Based on our regression model, patients with diagnostic dilemmas, a suspected non-amyloid syndrome, and Clinical Dementia Rating <1 were more likely to benefit from amyloid PET due to a higher likelihood of diagnostic change. We found that changes in diagnosis after PET in our secondary center almost doubled those of our previous analysis of a tertiary unit (9% versus 17.2%). Our results offer some clues about the rational use of amyloid PET in a secondary care memory unit stressing its utility in mild cognitive impairment patients.
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Affiliation(s)
- Carmen Lage
- Neurology Service and Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), 'Marqués de Valdecilla' University Hospital, University of Cantabria, Institute for Research 'Marqués de Valdecilla' (IDIVAL), Santander, Spain
| | - Andrea Gonzalez Suarez
- Neurology Service and Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), 'Marqués de Valdecilla' University Hospital, University of Cantabria, Institute for Research 'Marqués de Valdecilla' (IDIVAL), Santander, Spain
| | - Ana Pozueta
- Neurology Service and Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), 'Marqués de Valdecilla' University Hospital, University of Cantabria, Institute for Research 'Marqués de Valdecilla' (IDIVAL), Santander, Spain
| | - Javier Riancho
- Neurology Service and Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), 'Marqués de Valdecilla' University Hospital, University of Cantabria, Institute for Research 'Marqués de Valdecilla' (IDIVAL), Santander, Spain
| | - Martha Kazimierczak
- Neurology Service and Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), 'Marqués de Valdecilla' University Hospital, University of Cantabria, Institute for Research 'Marqués de Valdecilla' (IDIVAL), Santander, Spain
| | - Maria Bravo
- Neurology Service and Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), 'Marqués de Valdecilla' University Hospital, University of Cantabria, Institute for Research 'Marqués de Valdecilla' (IDIVAL), Santander, Spain
| | - Julio Jimenez Bonilla
- Department of Nuclear Medicine, University Hospital Marqués de Valdecilla, University of Cantabria, Molecular Imaging Group - IDIVAL, Santander, Spain
| | - Marıa de Arcocha Torres
- Department of Nuclear Medicine, University Hospital Marqués de Valdecilla, University of Cantabria, Molecular Imaging Group - IDIVAL, Santander, Spain
| | - Remedios Quirce
- Department of Nuclear Medicine, University Hospital Marqués de Valdecilla, University of Cantabria, Molecular Imaging Group - IDIVAL, Santander, Spain
| | - Ignacio Banzo
- Department of Nuclear Medicine, University Hospital Marqués de Valdecilla, University of Cantabria, Molecular Imaging Group - IDIVAL, Santander, Spain
| | - Jose Luis Vazquez-Higuera
- Neurology Service and Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), 'Marqués de Valdecilla' University Hospital, University of Cantabria, Institute for Research 'Marqués de Valdecilla' (IDIVAL), Santander, Spain
| | - Gil D Rabinovici
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA; Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Lab, Berkeley, CA, USA; Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA
| | - Eloy Rodriguez-Rodriguez
- Neurology Service and Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), 'Marqués de Valdecilla' University Hospital, University of Cantabria, Institute for Research 'Marqués de Valdecilla' (IDIVAL), Santander, Spain
| | - Pascual Sánchez-Juan
- Neurology Service and Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), 'Marqués de Valdecilla' University Hospital, University of Cantabria, Institute for Research 'Marqués de Valdecilla' (IDIVAL), Santander, Spain
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Semi-quantification and grading of amyloid PET: A project of the European Alzheimer's Disease Consortium (EADC). NEUROIMAGE-CLINICAL 2019; 23:101846. [PMID: 31077984 PMCID: PMC6514268 DOI: 10.1016/j.nicl.2019.101846] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Revised: 03/25/2019] [Accepted: 04/30/2019] [Indexed: 02/08/2023]
Abstract
Background amyloid-PET reading has been classically implemented as a binary assessment, although the clinical experience has shown that the number of borderline cases is non negligible not only in epidemiological studies of asymptomatic subjects but also in naturalistic groups of symptomatic patients attending memory clinics. In this work we develop a model to compare and integrate visual reading with two independent semi-quantification methods in order to obtain a tracer-independent multi-parametric evaluation. Methods We retrospectively enrolled three cohorts of cognitively impaired patients submitted to 18F-florbetaben (53 subjects), 18F-flutemetamol (62 subjects), 18F-florbetapir (60 subjects) PET/CT respectively, in 6 European centres belonging to the EADC. The 175 scans were visually classified as positive/negative following approved criteria and further classified with a 5-step grading as negative, mild negative, borderline, mild positive, positive by 5 independent readers, blind to clinical data. Scan quality was also visually assessed and recorded. Semi-quantification was based on two quantifiers: the standardized uptake value (SUVr) and the ELBA method. We used a sigmoid model to relate the grading with the quantifiers. We measured the readers accord and inconsistencies in the visual assessment as well as the relationship between discrepancies on the grading and semi-quantifications. Conclusion It is possible to construct a map between different tracers and different quantification methods without resorting to ad-hoc acquired cases. We used a 5-level visual scale which, together with a mathematical model, delivered cut-offs and transition regions on tracers that are (largely) independent from the population. All fluorinated tracers appeared to have the same contrast and discrimination ability with respect to the negative-to-positive grading. We validated the integration of both visual reading and different quantifiers in a more robust framework thus bridging the gap between a binary and a user-independent continuous scale. Scans acquired with all commercial amyloid-PET fluorinated tracers are compared. 2 independent semi-quantification methods provided whole-brain amyloid load values. 5 readers independently evaluated all scans using a graded scale. A mathematical model is used to link visual grading to semi-quantification. Mapping between tracers and reader evaluation are given.
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Leuzy A, Savitcheva I, Chiotis K, Lilja J, Andersen P, Bogdanovic N, Jelic V, Nordberg A. Clinical impact of [ 18F]flutemetamol PET among memory clinic patients with an unclear diagnosis. Eur J Nucl Med Mol Imaging 2019; 46:1276-1286. [PMID: 30915522 PMCID: PMC6486908 DOI: 10.1007/s00259-019-04297-5] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Accepted: 02/25/2019] [Indexed: 12/11/2022]
Abstract
Purpose To investigate the impact of amyloid PET with [18F]flutemetamol on diagnosis and treatment management in a cohort of patients attending a tertiary memory clinic in whom, despite extensive cognitive assessment including neuropsychological testing, structural imaging, CSF biomarker analysis and in some cases [18F]FDG PET, the diagnosis remained unclear. Methods The study population consisted of 207 patients with a clinical diagnosis prior to [18F]flutemetamol PET including mild cognitive impairment (MCI; n = 131), Alzheimer’s disease (AD; n = 41), non-AD (n = 10), dementia not otherwise specified (dementia NOS; n = 20) and subjective cognitive decline (SCD; n = 5). Results Amyloid positivity was found in 53% of MCI, 68% of AD, 20% of non-AD, 20% of dementia NOS, and 60% of SCD patients. [18F]Flutemetamol PET led, overall, to a change in diagnosis in 92 of the 207 patients (44%). A high percentage of patients with a change in diagnosis was observed in the MCI group (n = 67, 51%) and in the dementia NOS group (n = 11; 55%), followed by the non-AD and AD (30% and 20%, respectively). A significant increase in cholinesterase inhibitor treatment was observed after [18F]flutemetamol PET (+218%, 34 patients before and 108 patients after). Conclusion The present study lends support to the clinical value of amyloid PET in patients with an uncertain diagnosis in the tertiary memory clinic setting.
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Affiliation(s)
- Antoine Leuzy
- Department of Neurobiology, Care Sciences and Society, Division of Clinical Geriatrics Center for Alzheimer Research, Karolinska Institutet, Neo, 7th floor, 141 83, Huddinge, Sweden
| | - Irina Savitcheva
- Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Konstantinos Chiotis
- Department of Neurobiology, Care Sciences and Society, Division of Clinical Geriatrics Center for Alzheimer Research, Karolinska Institutet, Neo, 7th floor, 141 83, Huddinge, Sweden
| | - Johan Lilja
- Department of Surgical Sciences, Radiology, Nuclear Medicine and PET, Uppsala University, Uppsala, Sweden.,Hermes Medical Solutions, Stockholm, Sweden
| | - Pia Andersen
- Clinic for Cognitive Disorders, Theme Aging, Karolinska University Hospital, Stockholm, Sweden
| | - Nenad Bogdanovic
- Clinic for Cognitive Disorders, Theme Aging, Karolinska University Hospital, Stockholm, Sweden
| | - Vesna Jelic
- Department of Neurobiology, Care Sciences and Society, Division of Clinical Geriatrics Center for Alzheimer Research, Karolinska Institutet, Neo, 7th floor, 141 83, Huddinge, Sweden.,Clinic for Cognitive Disorders, Theme Aging, Karolinska University Hospital, Stockholm, Sweden
| | - Agneta Nordberg
- Department of Neurobiology, Care Sciences and Society, Division of Clinical Geriatrics Center for Alzheimer Research, Karolinska Institutet, Neo, 7th floor, 141 83, Huddinge, Sweden. .,Clinic for Cognitive Disorders, Theme Aging, Karolinska University Hospital, Stockholm, Sweden.
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Hunter S, Smailagic N, Brayne C. Aβ and the dementia syndrome: Simple versus complex perspectives. Eur J Clin Invest 2018; 48:e13025. [PMID: 30246866 DOI: 10.1111/eci.13025] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2017] [Revised: 08/15/2018] [Accepted: 09/06/2018] [Indexed: 02/06/2023]
Abstract
BACKGROUND The amyloid cascade hypothesis (ACH) has dominated strategy in dementia research for decades despite evidence of its limitations including known heterogeneity of the dementia syndrome in the population and the narrow focus on a single molecule - the amyloid beta protein (Aβ) as causal for all Alzheimer-type dementia. Other hypotheses relevant to Aβ are the presenilin (PS) hypothesis (PSH) relating to the involvement of PS in the generation of Aβ, and the amyloid precursor protein (APP) matrix approach (AMA), relating to the complex and dynamic breakdown of APP, from which Aβ derives. MATERIALS AND METHODS In this article we explore perspectives relating to complex disorders occurring mainly in older populations through a detailed case study of the role of Aβ in AD. RESULTS Scrutiny of the evidence generated so far reveals and a lack of understanding of the wider APP proteolytic system and how narrow research into the dementia syndrome has been to date. Confounding factors add significant limitations to the understanding of the current evidence base. CONCLUSIONS A better characterisation of the entire APP proteolytic system in the human brain is urgently required to place Aβ in its complex physiological context. From a molecular perspective, a combination of the alternative hypotheses, the PSH and the AMA may better describe the complexity of the APP proteolytic system leading to new therapeutic approaches. The reductionist approach is widespread throughout biomedical research and this example highlights how neglect of complexity can undermine investigations of complex disorders, particularly those arising in the oldest in our populations.
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Affiliation(s)
- Sally Hunter
- Department of Public Health and Primary Care, Institute of Public Health, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Nadja Smailagic
- Department of Public Health and Primary Care, Institute of Public Health, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Carol Brayne
- Department of Public Health and Primary Care, Institute of Public Health, School of Clinical Medicine, University of Cambridge, Cambridge, UK
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Tau PET imaging evidence in patients with cognitive impairment: preparing for clinical use. Clin Transl Imaging 2018. [DOI: 10.1007/s40336-018-0297-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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