| 1. |
- Vandenberghe, Rik, et al.
(författare)
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F-18-Flutemetamol Amyloid Imaging in Alzheimer Disease and Mild Cognitive Impairment A Phase 2 Trial
- 2010
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Ingår i: Annals of Neurology. - : Wiley. - 1531-8249 .- 0364-5134. ; 68:3, s. 319-329
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Tidskriftsartikel (refereegranskat)abstract
- Objective: The most widely studied positron emission tomography ligand for in vivo P-amyloid imaging is C-11-Pittsburgh compound B (C-11-PIB). Its availability, however, is limited by the need for an on-site cyclotron. Validation of the F-18-labeled PIB derivative F-18-flutemetamol could significantly enhance access to this novel technology. Methods: Twenty-seven patients with early-stage clinically probable Alzheimer disease (AD), 20 with amnestic mild cognitive impairment (MCI), and 15 cognitively intact healthy volunteers (HVs) above and 10 HVs below 55 years of age participated. The primary endpoint was the efficacy of blinded visual assessments of F-18-flutemetamol scans in assigning subjects to a raised versus normal uptake category, with clinical diagnosis as the standard of truth (SOT). As secondary objectives, we determined the correlation between the regional standardized uptake value ratios (SUVRs) for F-18-flutemetamol and its parent molecule C-11-PIB in 20 of the AD subjects and 20 of the MCI patients. We also determined test-retest variability of F-18-flutemetamol SUVRs in 5 of the AD subjects. Results: Blinded visual assessments of F-18-flutemetamol scans assigned 25 of 27 scans from AD subjects and 1 of 15 scans from the elderly HVs to the raised category, corresponding to a sensitivity of 93.1% and a specificity of 93.3% against the SOT. Correlation coefficients between cortical F-18-flutemetamol SUVRs and C-11-PIB SUVRs ranged from 0.89 to 0.92. Test-retest variabilities of regional SUVRs were 1 to 4%. Interpretation: F-18-Flutemetamol performs similarly to the C-11-PIB parent molecule within the same subjects and provides high test-retest replicability and potentially much wider accessibility for clinical and research use. ANN NEUROL 2010;68:319-329
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| 2. |
- Antila, Kari, et al.
(författare)
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The PredictAD project : development of novel biomarkers and analysis software for early diagnosis of the Alzheimer's disease
- 2013
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Ingår i: Interface Focus. - : The Royal Society Publishing. - 2042-8898 .- 2042-8901. ; 3:2
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Tidskriftsartikel (refereegranskat)abstract
- Alzheimer's disease (AD) is the most common cause of dementia affecting 36 million people worldwide. As the demographic transition in the developed countries progresses towards older population, the worsening ratio of workers per retirees and the growing number of patients with age-related illnesses such as AD will challenge the current healthcare systems and national economies. For these reasons AD has been identified as a health priority, and various methods for diagnosis and many candidates for therapies are under intense research. Even though there is currently no cure for AD, its effects can be managed. Today the significance of early and precise diagnosis of AD is emphasized in order to minimize its irreversible effects on the nervous system. When new drugs and therapies enter the market it is also vital to effectively identify the right candidates to benefit from these. The main objective of the PredictAD project was to find and integrate efficient biomarkers from heterogeneous patient data to make early diagnosis and to monitor the progress of AD in a more efficient, reliable and objective manner. The project focused on discovering biomarkers from biomolecular data, electrophysiological measurements of the brain and structural, functional and molecular brain images. We also designed and built a statistical model and a framework for exploiting these biomarkers with other available patient history and background data. We were able to discover several potential novel biomarker candidates and implement the framework in software. The results are currently used in several research projects, licensed to commercial use and being tested for clinical use in several trials.
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| 3. |
- Lundqvist, Roger, et al.
(författare)
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Implementation and validation of an adaptive template registration method for 18F-flutemetamol imaging data.
- 2013
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Ingår i: Journal of Nuclear Medicine. - : Society of Nuclear Medicine. - 0161-5505 .- 1535-5667 .- 2159-662X. ; 54:8, s. 1472-8
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Tidskriftsartikel (refereegranskat)abstract
- UNLABELLED: The spatial normalization of PET amyloid imaging data is challenging because different white and gray matter patterns of negative (Aβ-) and positive (Aβ+) uptake could lead to systematic bias if a standard method is used. In this study, we propose the use of an adaptive template registration method to overcome this problem.METHODS: Data from a phase II study (n = 72) were used to model amyloid deposition with the investigational PET imaging agent (18)F-flutemetamol. Linear regression of voxel intensities on the standardized uptake value ratio (SUVR) in a neocortical composite region for all scans gave an intercept image and a slope image. We devised a method where an adaptive template image spanning the uptake range (the most Aβ- to the most Aβ+ image) can be generated through a linear combination of these 2 images and where the optimal template is selected as part of the registration process. We applied the method to the (18)F-flutemetamol phase II data using a fixed volume of interest atlas to compute SUVRs. Validation was performed in several steps. The PET-only adaptive template registration method and the MR imaging-based method used in statistical parametric mapping were applied to spatially normalize PET and MR scans, respectively. Resulting transformations were applied to coregistered gray matter probability maps, and the quality of the registrations was assessed visually and quantitatively. For comparison of quantification results with an independent patient-space method, FreeSurfer was used to segment each subject's MR scan and the parcellations were applied to the coregistered PET scans. We then correlated SUVRs for a composite neocortical region obtained with both methods. Furthermore, to investigate whether the (18)F-flutemetamol model could be generalized to (11)C-Pittsburgh compound B ((11)C-PIB), we applied the method to Australian Imaging, Biomarkers and Lifestyle (AIBL) (11)C-PIB scans (n = 285) and compared the PET-only neocortical composite score with the corresponding score obtained with a semimanual method that made use of the subject's MR images for the positioning of regions.RESULTS: Spatial normalization was successful on all scans. Visual and quantitative comparison of the new PET-only method with the MR imaging-based method of statistical parametric mapping indicated that performance was similar in the cortical regions although the new PET-only method showed better registration in the cerebellum and pons reference region area. For the (18)F-flutemetamol quantification, there was a strong correlation between the PET-only and FreeSurfer SUVRs (Pearson r = 0.96). We obtained a similar correlation for the AIBL (11)C-PIB data (Pearson r = 0.94).CONCLUSION: The derived adaptive template registration method allows for robust, accurate, and fully automated quantification of uptake for (18)F-flutemetamol and (11)C-PIB scans without the use of MR imaging data.
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| 4. |
- Thurfjell, Lennart, et al.
(författare)
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Automated Quantification of F-18-Flutemetamol PET Activity for Categorizing Scans as Negative or Positive for Brain Amyloid: Concordance with Visual Image Reads
- 2014
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Ingår i: Journal of Nuclear Medicine. - : Society of Nuclear Medicine. - 0161-5505 .- 2159-662X .- 1535-5667. ; 55:10, s. 1623-1628
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Tidskriftsartikel (refereegranskat)abstract
- Clinical trials of the PET amyloid imaging agent F-18-flutemetamol have used visual assessment to classify PET scans as negative or positive for brain amyloid. However, quantification provides additional information about regional and global tracer uptake and may have utility for image assessment over time and across different centers. Using postmortem brain neuritic plaque density data as a truth standard to derive a standardized uptake value ratio (SUVR) threshold, we assessed a fully automated quantification method comparing visual and quantitative scan categorizations. We also compared the histopathology-derived SUVR threshold with one derived from healthy controls. Methods: Data from 345 consenting subjects enrolled in 8 prior clinical trials of F-18-flutemetamol injection were used. We grouped subjects into 3 cohorts: an autopsy cohort (n = 68) comprising terminally ill patients with postmortem confirmation of brain amyloid status; a test cohort (n = 172) comprising 33 patients with clinically probable Alzheimer disease, 80 patients with mild cognitive impairment, and 59 healthy volunteers; and a healthy cohort of 105 volunteers, used to define a reference range for SUVR. Visual image categorizations for comparison were from a previous study. A fully automated PET-only quantification method was used to compute regional neocortical SUVRs that were combined into a single composite SUVR. An SUVR threshold for classifying scans as positive or negative was derived by ranking the PET scans from the autopsy cohort based on their composite SUVR and comparing data with the standard of truth based on postmortem brain amyloid status for subjects in the autopsy cohort. The derived threshold was used to categorize the 172 scans in the test cohort as negative or positive, and results were compared with categorization using visual assessment. Different reference and composite region definitions were assessed. Threshold levels were also compared with corresponding thresholds derived from the healthy group. Results: Automated quantification (using pons as the reference region) demonstrated 91% sensitivity and 88% specificity and gave 3 false-positive and 4 false-negative scans. All 3 false-positive cases were either borderline-normal by standard of truth or had moderate to heavy cortical diffuse plaque burden. In the test cohort, the concordance between quantitative and visual read categorization ranged from 97.1% to 99.4% depending on the selection of reference and composite regions. The threshold derived from the healthy group was close to the histopathology-derived threshold. Conclusion: Categorization of F-18-flutemetamol amyloid imaging data using an automated PET-only quantification method showed good agreement with histopathologic classification of neuritic plaque density and a strong concordance with visual read results.
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