Harmonisation of PET imaging features with different amyloid ligands using machine learning-based classifier

Sung Hoon Kang, Jeonghun Kim, Jun Pyo Kim, Soo Hyun Cho, Yeong Sim Choe, Hyemin Jang, Hee Jin Kim, Seong Beom Koh, Duk L. Na, Joon Kyung Seong, Sang Won Seo

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Purpose: In this study, we used machine learning to develop a new method derived from a ligand-independent amyloid (Aβ) positron emission tomography (PET) classifier to harmonise different Aβ ligands. Methods: We obtained 107 paired 18F-florbetaben (FBB) and 18F-flutemetamol (FMM) PET images at the Samsung Medical Centre. To apply the method to FMM ligand, we transferred the previously developed FBB PET classifier to test similar features from the FMM PET images for application to FMM, which in turn developed a ligand-independent Aβ PET classifier. We explored the concordance rates of our classifier in detecting cortical and striatal Aβ positivity. We investigated the correlation of machine learning-based cortical tracer uptake (ML-CTU) values quantified by the classifier between FBB and FMM. Results: This classifier achieved high classification accuracy (area under the curve = 0.958) even with different Aβ PET ligands. In addition, the concordance rate of FBB and FMM using the classifier (87.5%) was good to excellent, which seemed to be higher than that in visual assessment (82.7%) and lower than that in standardised uptake value ratio cut-off categorisation (93.3%). FBB and FMM ML-CTU values were highly correlated with each other (R = 0.903). Conclusion: Our findings suggested that our novel classifier may harmonise FBB and FMM ligands in the clinical setting which in turn facilitate the biomarker-guided diagnosis and trials of anti-Aβ treatment in the research field.

Original languageEnglish
Pages (from-to)321-330
Number of pages10
JournalEuropean Journal of Nuclear Medicine and Molecular Imaging
Volume49
Issue number1
DOIs
Publication statusPublished - 2021 Dec

Bibliographical note

Funding Information:
This work was supported by the Fourth Stage of Brain Korea 21 Project in Division of Intelligent Precision Healthcare, a grant of the Korean Health Technology R&D Project, Ministry of Health & Welfare, Republic of Korea (HI19C1132), a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare and Ministry of science and ICT, Republic of Korea (grant number : HU20C0111), a fund (2021-ER1006-00) by Research of Korea Disease Control and Prevention Agency, and Korea University Guro Hospital (KOREA RESEARCH-DRIVEN HOSPITAL) and grant funded by Korea University Medicine (K2107411). We would like to thank Younghoon Seo (Samsung Medical Center) for English language editing.

Funding Information:
This work was supported by the Fourth Stage of Brain Korea 21 Project in Division of Intelligent Precision Healthcare, a grant of the Korean Health Technology R&D Project, Ministry of Health & Welfare, Republic of Korea (HI19C1132), a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare and Ministry of science and ICT, Republic of Korea (grant number : HU20C0111), a fund (2021-ER1006-00) by Research of Korea Disease Control and Prevention Agency, and Korea University Guro Hospital (KOREA RESEARCH-DRIVEN HOSPITAL) and grant funded by Korea University Medicine (K2107411).

Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Keywords

  • Aβ positivity
  • Concordance
  • Harmonisation
  • PET classifier

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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