Boosting the diagnostic power of amyloid-β PET using a data-driven spatially informed classifier for decision support
File(s)
Author(s)
Type
Journal Article
Abstract
Background
Amyloid-β (Aβ) PET has emerged as clinically useful for more accurate diagnosis of patients with cognitive decline. Aβ deposition is a necessary cause or response to the cellular pathology of Alzheimer’s disease (AD). Usual clinical and research interpretation of amyloid PET does not fully utilise all information regarding the spatial distribution of signal. We present a data-driven, spatially informed classifier to boost the diagnostic power of amyloid PET in AD.
Methods
Voxel-wise k-means clustering of amyloid-positive voxels was performed; clusters were mapped to brain anatomy and tested for their associations by diagnostic category and disease severity with 758 amyloid PET scans from volunteers in the AD continuum from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). A machine learning approach based on this spatially constrained model using an optimised quadratic support vector machine was developed for automatic classification of scans for AD vs non-AD pathology.
Results
This classifier boosted the accuracy of classification of AD scans to 81% using the amyloid PET alone with an area under the curve (AUC) of 0.91 compared to other spatial methods. This increased sensitivity to detect AD by 15% and the AUC by 9% compared to the use of a composite region of interest SUVr.
Conclusions
The diagnostic classification accuracy of amyloid PET was improved using an automated data-driven spatial classifier. Our classifier highlights the importance of considering the spatial variation in Aβ PET signal for optimal interpretation of scans. The algorithm now is available to be evaluated prospectively as a tool for automated clinical decision support in research settings.
Amyloid-β (Aβ) PET has emerged as clinically useful for more accurate diagnosis of patients with cognitive decline. Aβ deposition is a necessary cause or response to the cellular pathology of Alzheimer’s disease (AD). Usual clinical and research interpretation of amyloid PET does not fully utilise all information regarding the spatial distribution of signal. We present a data-driven, spatially informed classifier to boost the diagnostic power of amyloid PET in AD.
Methods
Voxel-wise k-means clustering of amyloid-positive voxels was performed; clusters were mapped to brain anatomy and tested for their associations by diagnostic category and disease severity with 758 amyloid PET scans from volunteers in the AD continuum from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). A machine learning approach based on this spatially constrained model using an optimised quadratic support vector machine was developed for automatic classification of scans for AD vs non-AD pathology.
Results
This classifier boosted the accuracy of classification of AD scans to 81% using the amyloid PET alone with an area under the curve (AUC) of 0.91 compared to other spatial methods. This increased sensitivity to detect AD by 15% and the AUC by 9% compared to the use of a composite region of interest SUVr.
Conclusions
The diagnostic classification accuracy of amyloid PET was improved using an automated data-driven spatial classifier. Our classifier highlights the importance of considering the spatial variation in Aβ PET signal for optimal interpretation of scans. The algorithm now is available to be evaluated prospectively as a tool for automated clinical decision support in research settings.
Date Issued
2021-12
Date Acceptance
2021-10-02
Citation
Alzheimer's Research and Therapy, 2021, 13 (1), pp.1-12
ISSN
1758-9193
Publisher
BioMed Central
Start Page
1
End Page
12
Journal / Book Title
Alzheimer's Research and Therapy
Volume
13
Issue
1
Copyright Statement
© The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the
original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or
other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this
licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativeco
mmons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the
original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or
other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this
licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativeco
mmons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
License URL
Sponsor
Medical Research Council (MRC)
UK DRI Ltd
UK DRI Ltd
Medical Research Council (MRC)
UK DRI Ltd
Engineering & Physical Science Research Council (EPSRC)
Identifier
https://alzres.biomedcentral.com/articles/10.1186/s13195-021-00910-8
Grant Number
HQR00720
N/A
DRI-CORE2020-IMP
4050641385
DRI-DSI2020-ATLAS
EP/N014529/1
Subjects
Science & Technology
Life Sciences & Biomedicine
Clinical Neurology
Neurosciences
Neurosciences & Neurology
Alzheimer's
Amyloid clusters
Amyloid PET
Machine learning
Clustering
Automated decision
MILD COGNITIVE IMPAIRMENT
ALZHEIMERS-DISEASE
DEPOSITION
BRAIN
PREVALENCE
MANAGEMENT
PATHOLOGY
DEMENTIA
DECLINE
IMPACT
Alzheimer’s
Amyloid PET
Amyloid clusters
Automated decision
Clustering
Machine learning
Alzheimer Disease
Amyloid beta-Peptides
Cognitive Dysfunction
Humans
Neuroimaging
Positron-Emission Tomography
Alzheimer’s Disease Neuroimaging Initiative
Humans
Alzheimer Disease
Positron-Emission Tomography
Amyloid beta-Peptides
Neuroimaging
Cognitive Dysfunction
11 Medical and Health Sciences
Publication Status
Published
Article Number
185
Date Publish Online
2021-11-10