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Automated quantification of 18F-flutemetamol PET activity for categorizing scans as negative or positive for brain amyloid: concordance with visual image reads.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Radiology.
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2014 (English)In: Journal of Nuclear Medicine, ISSN 0161-5505, E-ISSN 1535-5667, Vol. 55, no 10, 1623-8 p.Article in journal (Refereed) Published
Abstract [en]

UNLABELLED: Clinical trials of the PET amyloid imaging agent (18)F-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 (18)F-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 (18)F-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.

Place, publisher, year, edition, pages
2014. Vol. 55, no 10, 1623-8 p.
Keyword [en]
18F-flutemetamol, amyloid imaging, quantification
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:uu:diva-316801DOI: 10.2967/jnumed.114.142109PubMedID: 25146124OAI: oai:DiVA.org:uu-316801DiVA: diva2:1079045
Available from: 2017-03-07 Created: 2017-03-07 Last updated: 2017-03-18
In thesis
1. [18F]Flutemetamol PET image processing, visualization and quantification targeting clinical routine
Open this publication in new window or tab >>[18F]Flutemetamol PET image processing, visualization and quantification targeting clinical routine
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Alzheimer’s disease (AD) is the leading cause of dementia and is alone responsible for 60-70% of all cases of dementia. Though sharing clinical symptoms with other types of dementia, the hallmarks of AD are the abundance of extracellular depositions of β-amyloid (Aβ) plaques, intracellular neurofibrillary tangles of hyper phosphorylated tau proteins and synaptic depletion. The onset of the physiological hallmarks may precede clinical symptoms with a decade or more, and once clinical symptoms occur it may be difficult to separate AD from other types of dementia based on clinical symptoms alone. Since the introduction of radiolabeled Aβ tracer substances for positron emission tomography (PET) imaging it is possible to image the Aβ depositions in-vivo, strengthening the confidence in the diagnosis. Because the accumulation of Aβ may occur years before the first clinical symptoms are shown and even reach a plateau, Aβ PET imaging may not be feasible for disease progress monitoring. However, a negative scan may be used to rule out AD as the underlying cause to the clinical symptoms. It may also be used as a predictor to evaluate the risk of developing AD in patients with mild cognitive impairment (MCI) as well as monitoring potential effects of anti-amyloid drugs.Though currently validated for dichotomous visual assessment only, there is evidence to suggest that quantification of Aβ PET images may reduce inter-reader variability and aid in the monitoring of treatment effects from anti-amyloid drugs.The aim of this thesis was to refine existing methods and develop new ones for processing, quantification and visualization of Aβ PET images to aid in the diagnosis and monitoring of potential treatment of AD in clinical routine. Specifically, the focus for this thesis has been to find a way to fully automatically quantify and visualize a patient’s Aβ PET image in such way that it is presented in a uniform way and show how it relates to what is considered normal. To achieve the aim of the thesis registration algorithms, providing the means to register a patient’s Aβ PET image to a common stereotactic space avoiding the bias of different uptake patterns for Aβ- and Aβ+ images, a suitable region atlas and a 3-dimensional stereotactic surface projections (3D SSP) method, capable of projecting cortical activity onto the surface of a 3D model of the brain without sampling white matter, were developed and evaluated.The material for development and testing comprised 724 individual amyloid PET brain images from six distinct cohorts, ranging from healthy volunteers to definite AD. The new methods could be implemented in a fully automated workflow and were found to be highly accurate, when tested by comparisons to Standards of Truth, such as defining regional uptake from PET images co-registered to magnetic resonance images, post-mortem histopathology and the visual consensus diagnosis of imaging experts.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2017. 42 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine, ISSN 1651-6206 ; 1322
Keyword
quantification; flutemetamol; amyloid imaging; Alzheimer’s disease; positron emission tomography; brain mapping; stereotactic surface projections;image registration
National Category
Radiology, Nuclear Medicine and Medical Imaging Neurology
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-317688 (URN)978-91-554-9873-3 (ISBN)
Public defence
2017-05-05, Skoogsalen, Akademiska Sjukhuset, Ing 78/79 1tr, Uppsala, 13:15 (English)
Opponent
Supervisors
Available from: 2017-04-12 Created: 2017-03-18 Last updated: 2017-04-12

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