Dataset Information

Amyloid Load: A More Sensitive Biomarker for Amyloid Imaging.

ABSTRACT: Amyloid-? (A?) plays a key role in the pathogenesis of Alzheimer disease (AD) and can be imaged in vivo using ¹⁸F-florbetapir PET. A composite SUV ratio (SUVr) is a commonly used outcome measure for quantifying the global A? burden; however, sensitivity is suboptimal and can lead to low power in clinical trials. We introduce amyloid load, A?_L, as a novel biomarker to quantify the global A? burden along with an automated algorithm for its calculation (Amyloid^IQ). A?_L is evaluated on cross-sectional and longitudinal data obtained from the Alzheimer's Disease Neuroimaging Initiative. The cross-sectional data consisted of 769 subjects across the disease spectrum (211 healthy controls, 223 patients with early mild cognitive impairment, 204 with late mild cognitive impairment, and 132 with AD). The distributions of A?_L in the 4 different classifications were compared, and the same analyses were applied to a composite SUVr outcome measure. The effect sizes (Hedges g) between all but one classification were higher for A?_L than for composite SUVr, with the mean difference being 46%. Of the patients with early mild cognitive impairment, 147 had a 2-y follow-up scan, and the effect size between baseline and follow-up for A?_L was 0.49, compared with 0.36 for a composite SUVr, demonstrating an equivalent increase in power for longitudinal data. These results offer evidence that A?_L will be a valuable outcome measure in future A? imaging studies, providing a substantial increase in power over currently used SUVr methods.

SUBMITTER: Whittington A

PROVIDER: S-EPMC6448463 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Amyloid Load: A More Sensitive Biomarker for Amyloid Imaging.

Whittington Alex A Gunn Roger N RN

Journal of nuclear medicine : official publication, Society of Nuclear Medicine 20180906 4

Amyloid-β (Aβ) plays a key role in the pathogenesis of Alzheimer disease (AD) and can be imaged in vivo using 18F-florbetapir PET. A composite SUV ratio (SUVr) is a commonly used outcome measure for quantifying the global Aβ burden; however, sensitivity is suboptimal and can lead to low power in clinical trials. We introduce amyloid load, AβL, as a novel biomarker to quantify the global Aβ burden along with an automated algorithm for its calculation (AmyloidIQ). ...[more]

PMID: 30190305

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Project description:Qualitative visual assessment of MRI scans is a key mechanism by which inflammation is assessed in clinical practice. For example, in axial spondyloarthritis (axSpA), visual assessment focuses on the identification of regions with increased signal in the bone marrow, known as bone marrow oedema (BMO), on water-sensitive images. The identification of BMO has an important role in the diagnosis, quantification and monitoring of disease in axSpA. However, BMO evaluation depends heavily on the experience and expertise of the image reader, creating substantial imprecision. Deep learning-based segmentation is a natural approach to addressing this imprecision, but purely automated solutions require large training sets that are not currently available, and deep learning solutions with limited data may not be sufficiently trustworthy for use in clinical practice. To address this, we propose a workflow for inflammation segmentation incorporating both deep learning and human input. With this 'human-machine cooperation' workflow, a preliminary segmentation is generated automatically by deep learning; a human reader then 'cleans' the segmentation by removing extraneous segmented voxels. The final cleaned segmentation defines the volume of hyperintense inflammation (VHI), which is proposed as a quantitative imaging biomarker (QIB) of inflammation load in axSpA. We implemented and evaluated the proposed human-machine workflow in a cohort of 29 patients with axSpA who had undergone prospective MRI scans before and after starting biologic therapy. The performance of the workflow was compared against purely visual assessment in terms of inter-observer/inter-method segmentation overlap, inter-observer agreement and assessment of response to biologic therapy. The human-machine workflow showed superior inter-observer segmentation overlap than purely manual segmentation (Dice score 0.84 versus 0.56). VHI measurements produced by the workflow showed similar or better inter-observer agreement than visual scoring, with similar response assessments. We conclude that the proposed human-machine workflow offers a mechanism to improve the consistency of inflammation assessment, and that VHI could be a valuable QIB of inflammation load in axSpA, as well as offering an exemplar of human-machine cooperation more broadly.

Dataset Information

Amyloid Load: A More Sensitive Biomarker for Amyloid Imaging.

Publications

Amyloid Load: A More Sensitive Biomarker for Amyloid Imaging.

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