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European Journal of Nuclear Medicine and Molecular Imaging

Erschienen in:

22.09.2023 | Original Article

Distinct subtypes of spatial brain metabolism patterns in Alzheimer’s disease identified by deep learning-based FDG PET clusters

verfasst von: Hyun Gee Ryoo, Hongyoon Choi, Kuangyu Shi, Axel Rominger, Dong Young Lee, Dong Soo Lee, for the Alzheimer’s Disease Neuroimaging Initiative

Erschienen in: European Journal of Nuclear Medicine and Molecular Imaging | Ausgabe 2/2024

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Abstract

Purpose

Alzheimer’s disease (AD) is a heterogeneous disease that presents a broad spectrum of clinicopathologic profiles. To date, objective subtyping of AD independent of disease progression using brain imaging has been required. Our study aimed to extract representations of unique brain metabolism patterns different from disease progression to identify objective subtypes of AD.

Methods

A total of 3620 FDG brain PET images with AD, mild cognitive impairment (MCI), and cognitively normal (CN) were obtained from the ADNI database from 1607 participants at enrollment and follow-up visits. A conditional variational autoencoder model was trained on FDG brain PET images of AD patients with the corresponding condition of AD severity score. The k-means algorithm was applied to generate clusters from the encoded representations. The trained deep learning-based cluster model was also transferred to FDG PET of MCI patients and predicted the prognosis of subtypes for conversion from MCI to AD. Spatial metabolism patterns, clinical and biological characteristics, and conversion rate from MCI to AD were compared across the subtypes.

Results

Four distinct subtypes of spatial metabolism patterns in AD with different brain pathologies and clinical profiles were identified: (i) angular, (ii) occipital, (iii) orbitofrontal, and (iv) minimal hypometabolic patterns. The deep learning model was also successfully transferred for subtyping MCI, and significant differences in frequency (P < 0.001) and risk of conversion (log-rank P < 0.0001) from MCI to AD were observed across the subtypes, highest in S2 (35.7%) followed by S1 (23.4%).

Conclusion

We identified distinct subtypes of AD with different clinicopathologic features. The deep learning-based approach to distinguish AD subtypes on FDG PET could have implications for predicting individual outcomes and provide a clue to understanding the heterogeneous pathophysiology of AD.

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Titel: Distinct subtypes of spatial brain metabolism patterns in Alzheimer’s disease identified by deep learning-based FDG PET clusters
verfasst von: Hyun Gee Ryoo
Hongyoon Choi
Kuangyu Shi
Axel Rominger
Dong Young Lee
Dong Soo Lee
for the Alzheimer’s Disease Neuroimaging Initiative
Publikationsdatum: 22.09.2023
Verlag: Springer Berlin Heidelberg
Erschienen in: European Journal of Nuclear Medicine and Molecular Imaging / Ausgabe 2/2024
Print ISSN: 1619-7070
Elektronische ISSN: 1619-7089
DOI: https://doi.org/10.1007/s00259-023-06440-9

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Distinct subtypes of spatial brain metabolism patterns in Alzheimer’s disease identified by deep learning-based FDG PET clusters

Abstract

Purpose

Methods

Results

Conclusion

Live-Webinar "Urologie und Sexualmedizin in der Praxis"

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Abstract

Purpose

Methods

Results

Conclusion

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