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

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dc.contributor.authorKim, Suhong-
dc.contributor.authorLee, Peter-
dc.contributor.authorOh, Kyeong Taek-
dc.contributor.authorByun, Min Soo-
dc.contributor.authorYi, Dahyun-
dc.contributor.authorLee, Jun Ho-
dc.contributor.authorKim, Yu Kyeong-
dc.contributor.authorYe, Byoung Seok-
dc.contributor.authorYun, Mi Jin-
dc.contributor.authorLee, Dong Young-
dc.contributor.authorJeong, Yong-
dc.date.accessioned2021-08-13T05:55:56Z-
dc.date.available2021-08-13T14:57:50Z-
dc.date.issued2021-06-10-
dc.identifier.citationEJNMMI Research. 2021 Jun 10;11(1):56ko_KR
dc.identifier.issn2191-219X-
dc.identifier.urihttps://hdl.handle.net/10371/174793-
dc.description.abstractBackground
Considering 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).

Methods
We 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 Alzheimers 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.

Results
There 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.

Conclusion
The 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.
ko_KR
dc.description.sponsorshipThis research was supported by the Bio & Medical Technology Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (2016941946). This research was supported by the Brain Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT 2016M3C7A1913844). This research was supported by the Ministry of Science, ICT, and Future Planning, Republic of Korea (NRF-2014M3C7A1046042). This research was supported by the Basic Research Lab (BRL) Program ( NRF-2020R1A4A1018714 ) funded by the Korea Government (MSIP) through the National Research Foundation (NRF).ko_KR
dc.language.isoenko_KR
dc.publisherSpringer Openko_KR
dc.subjectAlzheimer’s disease-
dc.subjectAmyloid-
dc.subjectDementia-
dc.subject2-[18F]FDG PET-
dc.subjectDeep learning-
dc.subjectClassifcation model-
dc.titleDeep learning-based amyloid PET positivity classification model in the Alzheimers disease continuum by using 2-[18F]FDG PETko_KR
dc.typeArticleko_KR
dc.contributor.AlternativeAuthor김수홍-
dc.contributor.AlternativeAuthor오경택-
dc.contributor.AlternativeAuthor변민수-
dc.contributor.AlternativeAuthor이다훈-
dc.contributor.AlternativeAuthor이준호-
dc.contributor.AlternativeAuthor김유경-
dc.contributor.AlternativeAuthor예병석-
dc.contributor.AlternativeAuthor윤미진-
dc.contributor.AlternativeAuthor이동영-
dc.contributor.AlternativeAuthor정용-
dc.identifier.doi10.1186/s13550-021-00798-3-
dc.citation.journaltitleEJNMMI Researchko_KR
dc.language.rfc3066en-
dc.rights.holderThe Author(s)-
dc.date.updated2021-06-13T03:16:43Z-
dc.citation.number1ko_KR
dc.citation.startpage56ko_KR
dc.citation.volume11ko_KR
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College of Medicine/School of Medicine (의과대학/대학원)Psychiatry (정신과학전공)Journal Papers (저널논문_정신과학전공)
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