Evidence map›Paper›PMID 37735259›Full record

ArticleEuropean journal of nuclear medicine and molecular imaging2024

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

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

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Article in European journal of nuclear medicine and molecular imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

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6citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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6 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Hyun Gee RyooDepartment of Nuclear Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
Hongyoon ChoiDepartment of Nuclear Medicine, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea. chy1000@snu.ac.kr.
Kuangyu ShiDepartment of Nuclear Medicine, Inselspital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland.
Axel RomingerDepartment of Nuclear Medicine, Inselspital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland.
Dong Young LeeDepartment of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea.
Dong Soo LeeDepartment of Nuclear Medicine, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
Alzheimer’s Disease Neuroimaging Initiative

Funding

Korea Medical Device Development Fund Project Number: 1711137868Korea Medical Device Development Fund RS-2020-KD000006National Research Foundation of Korea NRF-2019K1A3A1A14065446
6 · The paper itself

Abstract

purposeAlzheimer'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.

methodsA 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.

resultsFour 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%).

conclusionWe 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.

Indexed as

Alzheimer DiseaseCognitive DysfunctionDeep LearningBrainDisease ProgressionFluorodeoxyglucose F18HumansPositron-Emission TomographyFluorodeoxyglucose F18Alzheimer’s diseaseDeep learningFDG PETMild cognitive impairmentSubtypes

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PMID37735259

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