Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

Evaluation of novel data-driven metrics of amyloid β deposition for longitudinal PET studies

Bollack, Ariane ; Markiewicz, Pawel J. ; Wink, Alle Meije ; Prosser, Lloyd ; Lilja, Johan LU ; Bourgeat, Pierrick ; Schott, Jonathan M. ; Coath, William ; Collij, Lyduine E. LU and Pemberton, Hugh G. , et al. (2023) In NeuroImage 280.
Abstract

Purpose: Positron emission tomography (PET) provides in vivo quantification of amyloid-β (Aβ) pathology. Established methods for assessing Aβ burden can be affected by physiological and technical factors. Novel, data-driven metrics have been developed to account for these sources of variability. We aimed to evaluate the performance of four of these amyloid PET metrics against conventional techniques, using a common set of criteria. Methods: Three cohorts were used for evaluation: Insight 46 (N=464, [18F]florbetapir), AIBL (N=277, [18F]flutemetamol), and an independent test-retest data (N=10, [18F]flutemetamol). Established metrics of amyloid tracer uptake included the Centiloid (CL) and where dynamic... (More)

Purpose: Positron emission tomography (PET) provides in vivo quantification of amyloid-β (Aβ) pathology. Established methods for assessing Aβ burden can be affected by physiological and technical factors. Novel, data-driven metrics have been developed to account for these sources of variability. We aimed to evaluate the performance of four of these amyloid PET metrics against conventional techniques, using a common set of criteria. Methods: Three cohorts were used for evaluation: Insight 46 (N=464, [18F]florbetapir), AIBL (N=277, [18F]flutemetamol), and an independent test-retest data (N=10, [18F]flutemetamol). Established metrics of amyloid tracer uptake included the Centiloid (CL) and where dynamic data was available, the non-displaceable binding potential (BPND). The four data-driven metrics computed were the amyloid load (Aβ load), the Aβ-PET pathology accumulation index (Aβ index), the Centiloid derived from non-negative matrix factorisation (CLNMF), and the amyloid pattern similarity score (AMPSS). These metrics were evaluated using reliability and repeatability in test-retest data, associations with BPND and CL, variability of the rate of change and sample size estimates to detect a 25% slowing in Aβ accumulation. Results: All metrics showed good reliability. Aβ load, Aβ index and CLNMF were strong associated with the BPND. The associations with CL suggest that cross-sectional measures of CLNMF, Aβ index and Aβ load are robust across studies. Sample size estimates for secondary prevention trial scenarios were the lowest for CLNMF and Aβ load compared to the CL. Conclusion: Among the novel data-driven metrics evaluated, the Aβ load, the Aβ index and the CLNMF can provide comparable performance to more established quantification methods of Aβ PET tracer uptake. The CLNMF and Aβ load could offer a more precise alternative to CL, although further studies in larger cohorts should be conducted.

(Less)
Please use this url to cite or link to this publication:
author
; ; ; ; ; ; ; ; and , et al. (More)
; ; ; ; ; ; ; ; ; ; ; and (Less)
author collaboration
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Alzheimer's, Amyloid, Longitudinal, Machine learning, PET, Quantification
in
NeuroImage
volume
280
article number
120313
publisher
Elsevier
external identifiers
  • pmid:37595816
  • scopus:85170410324
ISSN
1053-8119
DOI
10.1016/j.neuroimage.2023.120313
language
English
LU publication?
yes
id
89194749-5a10-49ee-9a15-b4d42ad756c4
date added to LUP
2024-01-12 15:18:44
date last changed
2024-03-30 06:40:17
@article{89194749-5a10-49ee-9a15-b4d42ad756c4,
  abstract     = {{<p>Purpose: Positron emission tomography (PET) provides in vivo quantification of amyloid-β (Aβ) pathology. Established methods for assessing Aβ burden can be affected by physiological and technical factors. Novel, data-driven metrics have been developed to account for these sources of variability. We aimed to evaluate the performance of four of these amyloid PET metrics against conventional techniques, using a common set of criteria. Methods: Three cohorts were used for evaluation: Insight 46 (N=464, [<sup>18</sup>F]florbetapir), AIBL (N=277, [<sup>18</sup>F]flutemetamol), and an independent test-retest data (N=10, [<sup>18</sup>F]flutemetamol). Established metrics of amyloid tracer uptake included the Centiloid (CL) and where dynamic data was available, the non-displaceable binding potential (BP<sub>ND</sub>). The four data-driven metrics computed were the amyloid load (Aβ load), the Aβ-PET pathology accumulation index (Aβ index), the Centiloid derived from non-negative matrix factorisation (CL<sub>NMF</sub>), and the amyloid pattern similarity score (AMPSS). These metrics were evaluated using reliability and repeatability in test-retest data, associations with BP<sub>ND</sub> and CL, variability of the rate of change and sample size estimates to detect a 25% slowing in Aβ accumulation. Results: All metrics showed good reliability. Aβ load, Aβ index and CL<sub>NMF</sub> were strong associated with the BP<sub>ND</sub>. The associations with CL suggest that cross-sectional measures of CL<sub>NMF</sub>, Aβ index and Aβ load are robust across studies. Sample size estimates for secondary prevention trial scenarios were the lowest for CL<sub>NMF</sub> and Aβ load compared to the CL. Conclusion: Among the novel data-driven metrics evaluated, the Aβ load, the Aβ index and the CL<sub>NMF</sub> can provide comparable performance to more established quantification methods of Aβ PET tracer uptake. The CL<sub>NMF</sub> and Aβ load could offer a more precise alternative to CL, although further studies in larger cohorts should be conducted.</p>}},
  author       = {{Bollack, Ariane and Markiewicz, Pawel J. and Wink, Alle Meije and Prosser, Lloyd and Lilja, Johan and Bourgeat, Pierrick and Schott, Jonathan M. and Coath, William and Collij, Lyduine E. and Pemberton, Hugh G. and Farrar, Gill and Barkhof, Frederik and Cash, David M.}},
  issn         = {{1053-8119}},
  keywords     = {{Alzheimer's; Amyloid; Longitudinal; Machine learning; PET; Quantification}},
  language     = {{eng}},
  publisher    = {{Elsevier}},
  series       = {{NeuroImage}},
  title        = {{Evaluation of novel data-driven metrics of amyloid β deposition for longitudinal PET studies}},
  url          = {{http://dx.doi.org/10.1016/j.neuroimage.2023.120313}},
  doi          = {{10.1016/j.neuroimage.2023.120313}},
  volume       = {{280}},
  year         = {{2023}},
}