Deep learning-based amyloid PET positivity classification model in the Alzheimer’s disease continuum by using 2-[18F]FDG PET

EJNMMI Research

Kyeong Taek Oh¹  Byoung Seok Ye²  Jun Ho Lee³  Dong Young Lee⁴  Min Soo Byun⁵  Yu Kyeong Kim⁶  Mi Jin Yun⁷  Yong Jeong⁸  Suhong Kim⁹  Dahyun Yi^1,10  Peter Lee^1,11 

[1] Department of Medical Engineering, Yonsei University College of Medicine, Seoul, Republic of Korea;Department of Neurology, Yonsei University College of Medicine, Seoul, Republic of Korea;Department of Neuropsychiatry, National Center for Mental Health, Seoul, Republic of Korea;Department of Neuropsychiatry, National Center for Mental Health, Seoul, Republic of Korea;Department of Psychiatry, Seoul National University College of Medicine, 101 Daehak-ro, Joungno-gu, 03080, Seoul, Republic of Korea;Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea;Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Republic of Korea;Department of Nuclear Medicine, SMG-SNU Boramae Medical Center, Seoul, Republic of Korea;Department of Nuclear Medicine, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, 03722, Seoul, Republic of Korea;Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea;Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, 34141, Daejeon, Republic of Korea;Korea Advanced Institute of Science and Technology (KAIST), KI for Health Science Technology, Daejeon, Republic of Korea;Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea;Korea Advanced Institute of Science and Technology (KAIST), KI for Health Science Technology, Daejeon, Republic of Korea;Institute of Human Behavioral Medicine, Medical Research Center, Seoul National University, Seoul, Republic of Korea;Korea Advanced Institute of Science and Technology (KAIST), KI for Health Science Technology, Daejeon, Republic of Korea;

关键词: Alzheimer’s disease;    Amyloid;    Dementia;    2-[F]FDG PET;    Deep learning;    Classification model;   

DOI  :  10.1186/s13550-021-00798-3

来源: Springer

【 摘 要 】

BackgroundConsidering the limited accessibility of amyloid position emission tomography (PET) in patients with dementia, we proposed a deep learning (DL)-based amyloid PET positivity classification model from PET images with 2-deoxy-2-[fluorine-18]fluoro-D-glucose (2-[18F]FDG).MethodsWe used 2-[18F]FDG PET datasets from the Alzheimer's Disease Neuroimaging Initiative and Korean Brain Aging Study for the Early diagnosis and prediction of Alzheimer’s disease for model development. Moreover, we used an independent dataset from another hospital. A 2.5-D deep learning architecture was constructed using 291 submodules and three axes images as the input. We conducted the voxel-wise analysis to assess the regions with substantial differences in glucose metabolism between the amyloid PET-positive and PET-negative participants. This facilitated an understanding of the deep model classification. In addition, we compared these regions with the classification probability from the submodules.ResultsThere were 686 out of 1433 (47.9%) and 50 out of 100 (50%) amyloid PET-positive participants in the training and internal validation datasets and the external validation datasets, respectively. With 50 times iterations of model training and validation, the model achieved an AUC of 0.811 (95% confidence interval (CI) of 0.803–0.819) and 0.798 (95% CI, 0.789–0.807) on the internal and external validation datasets, respectively. The area under the curve (AUC) was 0.860 when tested with the model with the highest value (0.864) on the external validation dataset. Moreover, it had 75.0% accuracy, 76.0% sensitivity, 74.0% specificity, and 75.0% F1-score. We found an overlap between the regions within the default mode network, thus generating high classification values.ConclusionThe proposed model based on the 2-[18F]FDG PET imaging data and a DL framework might successfully classify amyloid PET positivity in clinical practice, without performing amyloid PET, which have limited accessibility.

【 授权许可】

CC BY