Dataset Information

Optimized dual-time-window protocols for quantitative [18F]flutemetamol and [18F]florbetaben PET studies.

ABSTRACT: BACKGROUND:A long dynamic scanning protocol may be required to accurately measure longitudinal changes in amyloid load. However, such a protocol results in a lower patient comfort and scanning efficiency compared to static scans. A compromise can be achieved by implementing dual-time-window protocols. This study aimed to optimize these protocols for quantitative [18F]flutemetamol and [18F]florbetaben studies. METHODS:Rate constants for subjects across the Alzheimer's disease spectrum (i.e., non-displaceable binding potential (BPND) in the range 0.02-0.77 and 0.02-1.04 for [18F]flutemetamol and [18F]florbetaben, respectively) were established based on clinical [18F]flutemetamol (N?=?6) and [18F]florbetaben (N?=?20) data, and used to simulate tissue time-activity curves (TACs) of 110?min using a reference tissue and plasma input model. Next, noise was added (N?=?50) and data points corresponding to different intervals were removed from the TACs, ranging from 0 (i.e., 90-90?=?full-kinetic curve) to 80 (i.e., 10-90) minutes, creating a dual-time-window. Resulting TACs were fitted using the simplified reference tissue method (SRTM) to estimate the BPND, outliers (??1.5?×?BPND max) were removed and the bias was assessed using the distribution volume ratio (DVR?=?BPND?+?1). To this end, acceptability curves, which display the fraction of data below a certain bias threshold, were generated and the area under those curves were calculated. RESULTS:[18F]Flutemetamol and [18F]florbetaben data demonstrated an increased bias in amyloid estimate for larger intervals and higher noise levels. An acceptable bias (??3.1%) in DVR could be obtained with all except the 10-90 and 20-90-min intervals. Furthermore, a reduced fraction of acceptable data and most outliers were present for these two largest intervals (maximum percentage outliers 48 and 32 for [18F]flutemetamol and [18F]florbetaben, respectively). CONCLUSIONS:The length of the interval inversely correlates with the accuracy of the BPND estimates. Consequently, a dual-time-window protocol of 0-30 and 90-110?min (=maximum of 60?min interval) allows for accurate estimation of BPND values for both tracers. [18F]flutemetamol: EudraCT 2007-000784-19, registered 8 February 2007, [18F]florbetaben: EudraCT 2006-003882-15, registered 2006.

SUBMITTER: Heeman F

PROVIDER: S-EPMC6437225 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Optimized dual-time-window protocols for quantitative [<sup>18</sup>F]flutemetamol and [<sup>18</sup>F]florbetaben PET studies.

Heeman Fiona F Yaqub Maqsood M Lopes Alves Isadora I Heurling Kerstin K Berkhof Johannes J Gispert Juan Domingo JD Bullich Santiago S Foley Christopher C Lammertsma Adriaan A AA

EJNMMI research 20190327 1

<h4>Background</h4>A long dynamic scanning protocol may be required to accurately measure longitudinal changes in amyloid load. However, such a protocol results in a lower patient comfort and scanning efficiency compared to static scans. A compromise can be achieved by implementing dual-time-window protocols. This study aimed to optimize these protocols for quantitative [<sup>18</sup>F]flutemetamol and [<sup>18</sup>F]florbetaben studies.<h4>Methods</h4>Rate constants for subjects across the Alz ...[more]

PMID: 30919133

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Project description:INTRODUCTION:Standardized uptake value ratios (SUVRs) calculated from cerebral cortical areas can be used to categorize 18F-Florbetaben (FBB) PET scans by applying appropriate cutoffs. The objective of this work was first to generate FBB SUVR cutoffs using visual assessment (VA) as standard of truth (SoT) for a number of reference regions (RR) (cerebellar gray matter (GCER), whole cerebellum (WCER), pons (PONS), and subcortical white matter (SWM)). Secondly, to validate the FBB PET scan categorization performed by SUVR cutoffs against the categorization made by post-mortem histopathological confirmation of the A? presence. Finally, to evaluate the added value of SUVR cutoff categorization to VA. METHODS:SUVR cutoffs were generated for each RR using FBB scans from 143 subjects who were visually assessed by 3 readers. SUVR cutoffs were validated in 78 end-of life subjects using VA from 8 independent blinded readers (3 expert readers and 5 non-expert readers) and histopathological confirmation of the presence of neuritic beta-amyloid plaques as SoT. Finally, the number of correctly or incorrectly classified scans according to pathology results using VA and SUVR cutoffs was compared. RESULTS:Composite SUVR cutoffs generated were 1.43 (GCER), 0.96 (WCER), 0.78 (PONS) and 0.71 (SWM). Accuracy values were high and consistent across RR (range 83-94% for histopathology, and 85-94% for VA). SUVR cutoff performed similarly as VA but did not improve VA classification of FBB scans read either by expert readers or the majority read but provided higher accuracy than some non-expert readers. CONCLUSION:The accurate scan classification obtained in this study supports the use of VA as SoT to generate site-specific SUVR cutoffs. For an elderly end of life population, VA and SUVR cutoff categorization perform similarly in classifying FBB scans as A?-positive or A?-negative. These results emphasize the additional contribution that SUVR cutoff classification may have compared with VA performed by non-expert readers.

Project description:High accuracy has been reported in deep learning classification for amyloid brain scans, an important factor in Alzheimer's disease diagnosis. However, the possibility of overfitting should be considered, as this model is fitted with sample data. Therefore, we created and evaluated an [18F]Florbetaben amyloid brain positron emission tomography (PET) scan classification model with a Dong-A University Hospital (DAUH) dataset based on a convolutional neural network (CNN), and performed external validation with the Alzheimer's Disease Neuroimaging Initiative dataset. Spatial normalization, count normalization, and skull stripping preprocessing were performed on the DAUH and external datasets. However, smoothing was only performed on the external dataset. Three types of models were used, depending on their structure: Inception3D, ResNet3D, and VGG3D. After training with 80% of the DAUH dataset, an appropriate model was selected, and the rest of the DAUH dataset was used for model evaluation. The generalization potential of the selected model was then validated using the external dataset. The accuracy of the model evaluation for Inception3D, ResNet3D, and VGG3D was 95.4%, 92.0%, and 97.7%, and the accuracy of the external validation was 76.7%, 67.1%, and 85.3%, respectively. Inception3D and ResNet3D were retrained with the external dataset; then, the area under the curve was compared to determine the binary classification performance with a significance level of less than 0.05. When external validation was performed again after fine tuning, the performance improved to 15.3%p for Inception3D and 16.9%p for ResNet3D. In [18F]Florbetaben amyloid brain PET scan classification using CNN, the generalization potential can be seen through external validation. When there is a significant difference between the model classification performance and the external validation, changing the model structure or fine tuning the model can help improve the classification performance, and the optimal model can also be found by collaborating through a web-based open platform.

Project description:The deposition of the amyloid ?-protein (A?) in senile plaques is one of the histopathological hallmarks of Alzheimer's disease (AD). A?-plaques arise first in neocortical areas and, then, expand into further brain regions in a process described by 5 phases. Since it is possible to identify amyloid pathology with radioactive-labeled tracers by positron emission tomography (PET) the question arises whether it is possible to distinguish the neuropathological A?-phases with amyloid PET imaging. To address this question we reassessed 97 cases of the end-of-life study cohort of the phase 3 [18F]flutemetamol trial (ClinicalTrials.gov identifiers NCT01165554, and NCT02090855) by combining the standardized uptake value ratios (SUVRs) with pons as reference region for cortical and caudate nucleus-related [18F]flutemetamol-retention. We tested them for their prediction of the neuropathological pattern found at autopsy. By defining threshold levels for cortical and caudate nucleus SUVRs we could distinguish different levels of [18F]flutemetamol uptake termed PET-A? phase estimates. When comparing these PET-A? phase estimates with the neuropathological A?-phases we found that PET-A? phase estimate 0 corresponded with A?-phases 0-2, 1 with A?-phase 3, 2 with A?-phase 4, and 3 with A?-phase 5. Classification using the PET-A? phase estimates predicted the correct A?-phase in 72.16% of the cases studied here. Bootstrap analysis was used to confirm the robustness of the estimates around this association. When allowing a range of?±?1 phase for a given A?-phase correct classification was given in 96.91% of the cases. In doing so, we provide a novel method to convert SUVR-levels into PET-A? phase estimates that can be easily translated into neuropathological phases of A?-deposition. This method allows direct conclusions about the pathological distribution of amyloid plaques (A?-phases) in vivo. Accordingly, this method may be ideally suited to detect early preclinical AD-patients, to follow them with disease progression, and to provide a more precise prognosis for them based on the knowledge about the underlying pathological phase of the disease.

Project description:Background: Blood biomarkers may aid in recruitment to clinical trials of Alzheimer's disease (AD) modifying therapeutics by triaging potential trials participants for amyloid positron emission tomography (PET) or cerebrospinal fluid (CSF) A? and tau tests. Objective: To discover a plasma proteomic signature associated with CSF and PET measures of AD pathology. Methods: Liquid chromatography-tandem mass spectrometry (LC-MS/MS) based proteomics were performed in plasma from participants with subjective cognitive decline (SCD), mild cognitive impairment (MCI), and AD, recruited to the Amsterdam Dementia Cohort, stratified by CSF Tau/A?42 (n = 50). Technical replication and independent validation were performed by immunoassay in plasma from SCD, MCI, and AD participants recruited to the Amsterdam Dementia Cohort with CSF measures (n = 100), MCI participants enrolled in the GE067-005 study with [18F]-Flutemetamol PET amyloid measures (n = 173), and AD, MCI and cognitively healthy participants from the EMIF 500 study with CSF A?42 measurements (n = 494). Results: 25 discovery proteins were nominally associated with CSF Tau/A?42 (P < 0.05) with associations of ficolin-2 (FCN2), apolipoprotein C-IV and fibrinogen ? chain confirmed by immunoassay (P < 0.05). In the GE067-005 cohort, FCN2 was nominally associated with PET amyloid (P < 0.05) replicating the association with CSF Tau/A?42. There were nominally significant associations of complement component 3 with PET amyloid, and apolipoprotein(a), apolipoprotein A-I, ceruloplasmin, and PPY with MCI conversion to AD (all P < 0.05). In the EMIF 500 cohort FCN2 was trending toward a significant relationship with CSF A?42 (P ? 0.05), while both A1AT and clusterin were nominally significantly associated with CSF A?42 (both P < 0.05). Conclusion: Associations of plasma proteins with multiple measures of AD pathology and progression are demonstrated. To our knowledge this is the first study to report an association of FCN2 with AD pathology. Further testing of the proteins in larger independent cohorts will be important.

Dataset Information

Optimized dual-time-window protocols for quantitative [18F]flutemetamol and [18F]florbetaben PET studies.

Publications

Optimized dual-time-window protocols for quantitative [<sup>18</sup>F]flutemetamol and [<sup>18</sup>F]florbetaben PET studies.

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