Imperial College London
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DrWenjiaBai

Faculty of Medicine, Department of Brain Sciences

Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 8291w.bai Website

 
 
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Location

 

Room 212, Data Science InstituteWilliam Penney LaboratorySouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Venkataraman:2021:10.1186/s13195-021-00910-8,
author = {Venkataraman, AV and Bai, W and Whittington, A and Myers, JF and Rabiner, EA and Lingford-Hughes, A and Matthews, PM},
doi = {10.1186/s13195-021-00910-8},
journal = {Alzheimer's Research and Therapy},
pages = {1--12},
title = {Boosting the diagnostic power of amyloid-β PET using a data-driven spatially informed classifier for decision support},
url = {http://dx.doi.org/10.1186/s13195-021-00910-8},
volume = {13},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - BackgroundAmyloid-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.MethodsVoxel-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.ResultsThis 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.ConclusionsThe 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.
AU  - Venkataraman,AV
AU  - Bai,W
AU  - Whittington,A
AU  - Myers,JF
AU  - Rabiner,EA
AU  - Lingford-Hughes,A
AU  - Matthews,PM
DO  - 10.1186/s13195-021-00910-8
EP  - 12
PY  - 2021///
SN  - 1758-9193
SP  - 1
TI  - Boosting the diagnostic power of amyloid-β PET using a data-driven spatially informed classifier for decision support
T2  - Alzheimer's Research and Therapy
UR  - http://dx.doi.org/10.1186/s13195-021-00910-8
UR  - https://alzres.biomedcentral.com/articles/10.1186/s13195-021-00910-8
UR  - http://hdl.handle.net/10044/1/92947
VL  - 13
ER  -
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