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

• Published in: Alzheimer's research & therapy Vol. 13; no. 1
• Main Authors: Venkataraman, Ashwin V, Bai, Wenjia, Whittington, Alex, Myers, James F, Rabiner, Eugenii A, Lingford-Hughes, Anne, Matthews, Paul M
• Format: Journal Article
• Language: English
• Published: BioMed Central Ltd 10-11-2021
• Subjects: Alzheimer's disease; Amyloid beta-protein; Computer-aided medical diagnosis; Decision support systems; Diagnosis; Health aspects; Machine learning; Methods; PET imaging; United Kingdom
• ISSN: 1758-9193; 1758-9193
• DOI: 10.1186/s13195-021-00910-8

Background Amyloid-[beta] (A[beta]) PET has emerged as clinically useful for more accurate diagnosis of patients with cognitive decline. A[beta] 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 A[beta] 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. Keywords: Alzheimer's, Amyloid clusters, Amyloid PET, Machine learning, Clustering, Automated decision