Boosting the diagnostic power of amyloid-β PET using a data-driven spatially informed classifier for decision support

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...

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Autores principales:	Ashwin V. Venkataraman, Wenjia Bai, Alex Whittington, James F. Myers, Eugenii A. Rabiner, Anne Lingford-Hughes, Paul M. Matthews, for the Alzheimer’s Disease Neuroimaging Initiative
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Alzheimer’s Amyloid clusters Amyloid PET Machine learning Clustering Automated decision Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429
Acceso en línea:	https://doaj.org/article/232b9cbd125145b38e07fa18b48eaefd
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spelling	oai:doaj.org-article:232b9cbd125145b38e07fa18b48eaefd2021-11-14T12:39:12ZBoosting the diagnostic power of amyloid-β PET using a data-driven spatially informed classifier for decision support10.1186/s13195-021-00910-81758-9193https://doaj.org/article/232b9cbd125145b38e07fa18b48eaefd2021-11-01T00:00:00Zhttps://doi.org/10.1186/s13195-021-00910-8https://doaj.org/toc/1758-9193Abstract 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.Ashwin V. VenkataramanWenjia BaiAlex WhittingtonJames F. MyersEugenii A. RabinerAnne Lingford-HughesPaul M. Matthewsfor the Alzheimer’s Disease Neuroimaging InitiativeBMCarticleAlzheimer’sAmyloid clustersAmyloid PETMachine learningClusteringAutomated decisionNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571Neurology. Diseases of the nervous systemRC346-429ENAlzheimer’s Research & Therapy, Vol 13, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Alzheimer’s Amyloid clusters Amyloid PET Machine learning Clustering Automated decision Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429
spellingShingle	Alzheimer’s Amyloid clusters Amyloid PET Machine learning Clustering Automated decision Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429 Ashwin V. Venkataraman Wenjia Bai Alex Whittington James F. Myers Eugenii A. Rabiner Anne Lingford-Hughes Paul M. Matthews for the Alzheimer’s Disease Neuroimaging Initiative Boosting the diagnostic power of amyloid-β PET using a data-driven spatially informed classifier for decision support
description	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.
format	article
author	Ashwin V. Venkataraman Wenjia Bai Alex Whittington James F. Myers Eugenii A. Rabiner Anne Lingford-Hughes Paul M. Matthews for the Alzheimer’s Disease Neuroimaging Initiative
author_facet	Ashwin V. Venkataraman Wenjia Bai Alex Whittington James F. Myers Eugenii A. Rabiner Anne Lingford-Hughes Paul M. Matthews for the Alzheimer’s Disease Neuroimaging Initiative
author_sort	Ashwin V. Venkataraman
title	Boosting the diagnostic power of amyloid-β PET using a data-driven spatially informed classifier for decision support
title_short	Boosting the diagnostic power of amyloid-β PET using a data-driven spatially informed classifier for decision support
title_full	Boosting the diagnostic power of amyloid-β PET using a data-driven spatially informed classifier for decision support
title_fullStr	Boosting the diagnostic power of amyloid-β PET using a data-driven spatially informed classifier for decision support
title_full_unstemmed	Boosting the diagnostic power of amyloid-β PET using a data-driven spatially informed classifier for decision support
title_sort	boosting the diagnostic power of amyloid-β pet using a data-driven spatially informed classifier for decision support
publisher	BMC
publishDate	2021
url	https://doaj.org/article/232b9cbd125145b38e07fa18b48eaefd
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Boosting the diagnostic power of amyloid-β PET using a data-driven spatially informed classifier for decision support

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