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Abstract
Purpose: We developed a machine learning-based classifier for in vivo amyloid positron emission tomography (PET) staging, quantified cortical uptake of the PET tracer by using a machine learning method, and investigated the impact of these amyloid PET parameters on clinical and structural outcomes.

Methods: A total of 337 18F-florbetaben PET scans obtained at Samsung Medical Center were assessed. We defined a feature vector representing the change in PET tracer uptake from grey to white matter. Using support vector machine (SVM) regression and SVM classification, we quantified the cortical uptake as predicted regional cortical tracer uptake (pRCTU) and categorised the scans as positive and negative. Positive scans were further classified into two stages according to the striatal uptake. We compared outcome parameters among stages and further assessed the association between the pRCTU and outcome variables. Finally, we performed path analysis to determine mediation effects between PET variables.

Results: The classification accuracy was 97.3% for cortical amyloid positivity and 91.1% for striatal positivity. The left frontal and precuneus/posterior cingulate regions, as well as the anterior portion of the striatum, were important in determination of stages. The clinical scores and magnetic resonance imaging parameters showed negative associations with PET stage. However, except for the hippocampal volume, most outcomes were associated with the stage through the complete mediation effect of pRCTU.

Conclusion: Using a machine learning algorithm, we achieved high accuracy for in vivo amyloid PET staging. The in vivo amyloid stage was associated with cognitive function and cerebral atrophy mostly through the mediation effect of cortical amyloid.

Affiliations

Jun Pyo Kim  1   2   3 , Jeonghun Kim  4 , Yeshin Kim  5 , Seung Hwan Moon  6 , Yu Hyun Park  1   2 , Sole Yoo  7 , Hyemin Jang  1   2   3 , Hee Jin Kim  1   2   3 , Duk L Na  1   2   3   8 , Sang Won Seo  9   10   11   12   13 , Joon-Kyung Seong  14   15   16
1 Department of Neurology, Samsung Medical Center, Seoul, South Korea.
2 Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, South Korea.
3 Neuroscience Center, Samsung Medical Center, Seoul, South Korea.
4 Department of Bio-convergence Engineering, Korea University, Seoul, South Korea.
5 Department of Neurology, Kangwon National University Hospital, Chuncheon, South Korea.
6 Department of Nuclear Medicine, Samsung Medical Center, Seoul, South Korea.
7 Department of Cognitive Science, Yonsei University, Seoul, South Korea.
8 Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, South Korea.
9 Department of Neurology, Samsung Medical Center, Seoul, South Korea. sangwonseo@empal.com.
10 Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, South Korea. sangwonseo@empal.com.
11 Neuroscience Center, Samsung Medical Center, Seoul, South Korea. sangwonseo@empal.com.
12 Department of Clinical Research Design & Evaluation, SAIHST, Sungkyunkwan University, Seoul, South Korea. sangwonseo@empal.com.
13 Center for Clinical Epidemiology, Samsung Medical Center, Seoul, South Korea. sangwonseo@empal.com.
14 Department of Bio-convergence Engineering, Korea University, Seoul, South Korea. jkseong@korea.ac.kr.
15 School of Biomedical Engineering, Korea University, Seoul, South Korea. jkseong@korea.ac.kr.
16 Department of Artificial Intelligence, Korea University, Seoul, South Korea. jkseong@korea.ac.kr.