Staging and quantification of florbetaben PET images using machine learning: impact of predicted regional cortical tracer uptake and amyloid stage on clinical outcomes

Jun Pyo Kim, Jeonghun Kim, Yeshin Kim, Seung Hwan Moon, Yu Hyun Park, Sole Yoo, Hyemin Jang, Hee Jin Kim, Duk L. Na, Sang Won Seo, Joon Kyung Seong

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1971-1983
Number of pages13
JournalEuropean Journal of Nuclear Medicine and Molecular Imaging
Volume47
Issue number8
DOIs
Publication statusPublished - 2020 Jul 1

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) [No. NRF-2017R1A2B2005081 and NRF-2019R1A5A2027340]; a grant of the Korean Health Technology R&D Project, Ministry of Health & Welfare, Republic of Korea?[HI19C1132]; a fund by Research of Korea Centers for Disease Control and Prevention [2019-ER6203-01]; and the Original Technology Research Program for Brain Science through the National Research Foundation of Korea(NRF) funded by the Ministry of Science ICT and Future Planning [2015M3C7A1029034].

Funding Information:
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) [No. NRF-2017R1A2B2005081 and NRF-2019R1A5A2027340]; a grant of the Korean Health Technology R&D Project, Ministry of Health & Welfare, Republic of Korea [HI19C1132]; a fund by Research of Korea Centers for Disease Control and Prevention [2019-ER6203-01]; and the Original Technology Research Program for Brain Science through the National Research Foundation of Korea(NRF) funded by the Ministry of Science ICT and Future Planning [2015M3C7A1029034].

Publisher Copyright:
© 2019, The Author(s).

Keywords

  • Alzheimer’s disease
  • Amyloid PET
  • Machine learning
  • Quantification
  • Staging

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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