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

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European Journal of Nuclear Medicine and Molecular Imaging Aims and scope Submit manuscript

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.

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Data and materials availability

Anonymized data for our analyses presented in this report are available upon request from the corresponding authors.

Code availability

Not applicable.

References

  1. Jack CR Jr, Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB, et al. NIA-AA Research Framework: toward a biological definition of Alzheimer’s disease. Alzheimer’s Dementia. 2018;14(4):535–62. https://doi.org/10.1016/j.jalz.2018.02.018.

    Article  PubMed  Google Scholar 

  2. Ikonomovic MD, Buckley CJ, Heurling K, Sherwin P, Jones PA, Zanette M, et al. Post-mortem histopathology underlying beta-amyloid PET imaging following flutemetamol F 18 injection. Acta Neuropathol Commun. 2016;4(1):130. https://doi.org/10.1186/s40478-016-0399-z.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  3. Kim JP, Kim J, Kim Y, Moon SH, Park YH, Yoo S, et al. Staging and quantification of florbetaben PET images using machine learning: impact of predicted regional cortical tracer uptake and amyloid stage on clinical outcomes. Eur J Nucl Med Mol Imaging. 2019. https://doi.org/10.1007/s00259-019-04663-3.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Klunk WE, Engler H, Nordberg A, Wang Y, Blomqvist G, Holt DP, et al. Imaging brain amyloid in Alzheimer’s disease with Pittsburgh Compound-B. Ann Neurol. 2004;55(3):306–19. https://doi.org/10.1002/ana.20009.

    Article  PubMed  CAS  Google Scholar 

  5. Rinne JO, Wong DF, Wolk DA, Leinonen V, Arnold SE, Buckley C, et al. [(18)F]Flutemetamol PET imaging and cortical biopsy histopathology for fibrillar amyloid beta detection in living subjects with normal pressure hydrocephalus: pooled analysis of four studies. Acta Neuropathol. 2012;124(6):833–45. https://doi.org/10.1007/s00401-012-1051-z.

    Article  PubMed  CAS  Google Scholar 

  6. Wong DF, Rosenberg PB, Zhou Y, Kumar A, Raymont V, Ravert HT, et al. In vivo imaging of amyloid deposition in Alzheimer disease using the radioligand 18F-AV-45 (florbetapir [corrected] F 18). J Nucl Med Off Publ Soc Nucl Med. 2010;51(6):913–20. https://doi.org/10.2967/jnumed.109.069088.

    Article  CAS  Google Scholar 

  7. Barthel H, Gertz HJ, Dresel S, Peters O, Bartenstein P, Buerger K, et al. Cerebral amyloid-beta PET with florbetaben (18F) in patients with Alzheimer’s disease and healthy controls: a multicentre phase 2 diagnostic study. Lancet Neurol. 2011;10(5):424–35. https://doi.org/10.1016/s1474-4422(11)70077-1.

    Article  PubMed  CAS  Google Scholar 

  8. Klunk WE, Koeppe RA, Price JC, Benzinger TL, Devous MD Sr, Jagust WJ, et al. The Centiloid Project: standardizing quantitative amyloid plaque estimation by PET. Alzheimers Dement J Alzheimers Assoc. 2015;11(1):1-15.e1-4. https://doi.org/10.1016/j.jalz.2014.07.003.

    Article  Google Scholar 

  9. Cho SH, Shin JH, Jang H, Park S, Kim HJ, Kim SE, et al. Amyloid involvement in subcortical regions predicts cognitive decline. Eur J Nucl Med Mol Imaging. 2018;45(13):2368–76. https://doi.org/10.1007/s00259-018-4081-5.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. Hanseeuw BJ, Betensky RA, Mormino EC, Schultz AP, Sepulcre J, Becker JA, et al. PET staging of amyloidosis using striatum. Alzheimers Dement. 2018;14(10):1281–92. https://doi.org/10.1016/j.jalz.2018.04.011.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Cho SH, Choe YS, Kim YJ, Kim HJ, Jang H, Kim Y, et al. Head-to-head comparison of 18F-Florbetaben and 18F-Flutemetamol in the cortical and striatal regions. J Alzheimers Dis: JAD. 2020;76(1):281–90. https://doi.org/10.3233/jad-200079.

    Article  PubMed  Google Scholar 

  12. Cho SH, Choe YS, Park S, Kim YJ, Kim HJ, Jang H, et al. Appropriate reference region selection of (18)F-florbetaben and (18)F-flutemetamol beta-amyloid PET expressed in Centiloid. Sci Rep. 2020;10(1):14950. https://doi.org/10.1038/s41598-020-70978-z.

    Article  PubMed  PubMed Central  Google Scholar 

  13. McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR Jr, Kawas CH, et al. The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011;7(3):263–9. https://doi.org/10.1016/j.jalz.2011.03.005.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Petersen RC, Smith GE, Waring SC, Ivnik RJ, Tangalos EG, Kokmen E. Mild cognitive impairment: clinical characterization and outcome. Arch Neurol. 1999;56(3):303–8. https://doi.org/10.1001/archneur.56.3.303.

    Article  PubMed  CAS  Google Scholar 

  15. Bell CC. DSM-IV: Diagnostic and Statistical Manual of Mental Disorders. JAMA. 1994;272(10):828–9. https://doi.org/10.1001/jama.1994.03520100096046%JJAMA.

    Article  Google Scholar 

  16. Kim CH, Seo SW, Kim GH, Shin JS, Cho H, Noh Y, et al. Cortical thinning in subcortical vascular dementia with negative 11C-PiB PET. J Alzheimers Dis: JAD. 2012;31(2):315–23. https://doi.org/10.3233/jad-2012-111832.

    Article  PubMed  CAS  Google Scholar 

  17. Kim HJ, Yang JJ, Kwon H, Kim C, Lee JM, Chun P, et al. Relative impact of amyloid-beta, lacunes, and downstream imaging markers on cognitive trajectories. Brain J Neurol. 2016;139(Pt 9):2516–27. https://doi.org/10.1093/brain/aww148.

    Article  Google Scholar 

  18. Farrar G, Molinuevo JL, Zanette M. Is there a difference in regional read [(18)F]flutemetamol amyloid patterns between end-of-life subjects and those with amnestic mild cognitive impairment? Eur J Nucl Med Mol Imaging. 2019;46(6):1299–308. https://doi.org/10.1007/s00259-019-04282-y.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Buckley CJ, Sherwin PF, Smith AP, Wolber J, Weick SM, Brooks DJ. Validation of an electronic image reader training programme for interpretation of [18F]flutemetamol β-amyloid PET brain images. Nucl Med Commun. 2017;38(3):234–41. https://doi.org/10.1097/mnm.0000000000000633.

    Article  PubMed  CAS  Google Scholar 

  20. Mormino EC, Brandel MG, Madison CM, Rabinovici GD, Marks S, Baker SL, et al. Not quite PIB-positive, not quite PIB-negative: slight PIB elevations in elderly normal control subjects are biologically relevant. Neuroimage. 2012;59(2):1152–60. https://doi.org/10.1016/j.neuroimage.2011.07.098.

    Article  PubMed  Google Scholar 

  21. Ikonomovic MD, Buckley CJ, Heurling K, Sherwin P, Jones PA, Zanette M, et al. Post-mortem histopathology underlying β-amyloid PET imaging following flutemetamol F 18 injection. Acta Neuropathol Commun. 2016;4(1):130. https://doi.org/10.1186/s40478-016-0399-z.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Bullich S, Seibyl J, Catafau AM, Jovalekic A, Koglin N, Barthel H, et al. Optimized classification of (18)F-Florbetaben PET scans as positive and negative using an SUVR quantitative approach and comparison to visual assessment. NeuroImage Clinical. 2017;15:325–32. https://doi.org/10.1016/j.nicl.2017.04.025.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Landau SM, Fero A, Baker SL, Koeppe R, Mintun M, Chen K, et al. Measurement of longitudinal β-amyloid change with 18F-florbetapir PET and standardized uptake value ratios. J Nucl Med Off Publ Soc Nucl Med. 2015;56(4):567–74. https://doi.org/10.2967/jnumed.114.148981.

    Article  CAS  Google Scholar 

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Acknowledgements

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.

Author information

Author notes

  1. Sung Hoon Kang and Jeonghun Kim contributed equally to this work.

Authors and Affiliations

  1. Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, 06351, Seoul, South Korea

    Sung Hoon Kang, Jun Pyo Kim, Yeong Sim Choe, Hyemin Jang, Hee Jin Kim, Duk L. Na & Sang Won Seo

  2. Neuroscience Center, Samsung Medical Center, Seoul, 06351, South Korea

    Sung Hoon Kang, Jun Pyo Kim, Yeong Sim Choe, Hyemin Jang, Hee Jin Kim, Duk L. Na & Sang Won Seo

  3. Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, South Korea

    Sung Hoon Kang & Seong-Beom Koh

  4. Medical & Health Device Division, Korea Testing Laboratory, Seoul, South Korea

    Jeonghun Kim

  5. Department of Neurology, Chonnam National University Hospital, Chonnam National University Medical School, Gwangju, South Korea

    Soo Hyun Cho

  6. Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, South Korea

    Yeong Sim Choe & Sang Won Seo

  7. Department of Artificial Intelligence, Korea University, Seoul, South Korea

    Joon-Kyung Seong

  8. School of Biomedical Engineering, Korea University, Seoul, South Korea

    Joon-Kyung Seong

  9. Interdisciplinary Program in Precision Public Health, Korea University, Seoul, South Korea

    Joon-Kyung Seong

  10. Department of Intelligent Precision Healthcare Convergence, SAIHST, Sungkyunkwan University, Seoul, South Korea

    Sang Won Seo

  11. Samsung Alzheimer Research Center, Center for Clinical Epidemiology Medical Center, Seoul, South Korea

    Sang Won Seo

Authors
  1. Sung Hoon Kang
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Contributions

Sung Hoon Kang, Writing – original draft, Writing – review & editing, Formal analysis, Data curation; Jeonghun Kim, Writing – review & editing, Formal analysis, Data curation; Jun Pyo Kim, Data curation; Soo Hyun Cho, Data curation; Yeong Sim Choe, Data curation; Hyemin Jang, Data curation; Hee Jin Kim, Data curation; Seong-Beom Koh, Data curation; Duk L. Na, Data curation; Joon-Kyung Seong, Methodology, Software, Validation, Formal analysis, Writing – review & editing, Conceptualization; Sang Won Seo, Writing – review & editing, Conceptualization.

Corresponding authors

Correspondence to Joon-Kyung Seong or Sang Won Seo.

Ethics declarations

Ethics approval and consent to participate

Approval was obtained from the ethics committee of Samsung Medical Centre. The procedures used in this study adhere to the tenets of the Declaration of Helsinki. Written informed consent was obtained from all patients and caregivers.

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Not applicable.

Conflicts of interest

The authors declare no competing interests.

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This article is part of the Topical Collection on Neurology.

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Kang, S.H., Kim, J., Kim, J.P. et al. Harmonisation of PET imaging features with different amyloid ligands using machine learning-based classifier. Eur J Nucl Med Mol Imaging 49, 321–330 (2021). https://doi.org/10.1007/s00259-021-05499-6

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  • DOI: https://doi.org/10.1007/s00259-021-05499-6

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