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Improved quantification of amyloid burden and associated biomarker cut-off points: results from the first amyloid Singaporean cohort with overlapping cerebrovascular disease

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

Abstract

Purpose

The analysis of the [11C]PiB-PET amyloid images of a unique Asian cohort of 186 participants featuring overlapping vascular diseases raised the question about the validity of current standards for amyloid quantification under abnormal conditions. In this work, we implemented a novel pipeline for improved amyloid PET quantification of this atypical cohort.

Methods

The investigated data correction and amyloid quantification methods included motion correction, standardized uptake value ratio (SUVr) quantification using the parcellated MRI (standard method) and SUVr quantification without MRI. We introduced a novel amyloid analysis method yielding 2 biomarkers: AβL which quantifies the global Aβ burden and ns that characterizes the non-specific uptake. Cut-off points were first determined using visual assessment as ground truth and then using unsupervised classification techniques.

Results

Subject’s motion impacts the accuracy of the measurement outcome but has however a limited effect on the visual rating and cut-off point determination. SUVr computation can be reliably performed for all the subjects without MRI parcellation while, when required, the parcellation failed or was of mediocre quality in 10% of the cases. The novel biomarker AβL showed an association increase of 29.5% with the cognitive tests and increased effect size between positive and negative scans compared with SUVr. ns was found sensitive to cerebral microbleeds, white matter hyperintensity, volume, and age. The cut-off points for SUVr using parcellated MRI, SUVr without parcellation, and AβL were 1.56, 1.39, and 25.5. Finally, k-means produced valid cut-off points without the requirement of visual assessment.

Conclusion

The optimal processing for the amyloid quantification of this atypical cohort allows the quantification of all the subjects, producing SUVr values and two novel biomarkers: AβL, showing important increased in their association with various cognitive tests, and ns, a parameter sensitive to non-specific retention variations caused by age and cerebrovascular diseases.

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Data availability

Data generated for this work and which supports the findings are available upon request.

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Acknowledgments

We acknowledge all the NUH memory clinic and Memory Aging and Cognition Centre coordinators for their contributions to subject recruitment and data acquisition.

Funding

This study was supported by the following National Medical Research Council grants: NMRC/CG/NUHS/2010 - R-184-005-184-511, NMRC/CG/013/2013, and NMRC/CIRG/1446/2016.

Author information

Author notes

  1. Christopher P. Chen and Anthonin Reilhac contributed equally to this work.

Authors and Affiliations

  1. Clinical Imaging Research Centre, National University of Singapore, 14 Medical Drive, #B1-01, Singapore, 117599, Singapore

    Tomotaka Tanaka, Mary C. Stephenson, Ying-Hwey Nai, Ashley A. Weekes, John J. Totman, Edward G. Robins & Anthonin Reilhac

  2. Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Blk MD3, 16 Medical Drive. Level 4, #04-01, Singapore, 117600, Singapore

    Tomotaka Tanaka, Francis N. Saridin, Saima Hilal, Steven Villaraza, Bibek Gyanwali & Christopher P. Chen

  3. Department of Neurology, National Cerebral and Cardiovascular Center, 5-7-1 Fujishiro-dai, Suita, Osaka, 565-8565, Japan

    Tomotaka Tanaka & Masafumi Ihara

  4. Department of Diagnostic Imaging, National Cancer Institute of Singapore, 11 Hospital Drive, Singapore, 169610, Singapore

    Damian Khor

  5. Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore

    Saima Hilal

  6. Biomedical Imaging group Rotterdam, Erasmus MC, University Medical Center, P.O. Box 2040, 3000 CA, Rotterdam, Netherlands

    Henri Vrooman

  7. Singapore Bioimaging Consortium, Agency for Science, A*Star,1Fusionopolis way, #20-10 Connexis North Tower, Singapore, 138632, Singapore

    Edward G. Robins

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  1. Tomotaka Tanaka
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Contributions

TT, YHN, FNS, SV, BG, CPC, and AR developed the study concept. MRI data were collected and analyzed by SH, BG, and HV. PET data were analyzed by MCS, YHN, DK, AAW, and AR. Data simulation, analysis, and reporting were done by TT. The first draft of the manuscript was written by TT and AR, with critical revisions by YHN, FNS, SH, BG, MI, JJT, EGR, and CPC. All authors approved the final version of the manuscript for submission.

Corresponding author

Correspondence to Tomotaka Tanaka.

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Conflict of interest

The authors declare that they have no conflicts of interest.

Ethical approval

Ethics approval was obtained from the National-Healthcare Group Domain-Specific Review Board. The study was conducted in accordance with the Declaration of Helsinki.

Informed consent

Written informed consent was obtained in the preferred language of the participants or their legal representatives by bilingual study coordinators prior to their recruitment into the study.

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Tanaka, T., Stephenson, M.C., Nai, YH. et al. Improved quantification of amyloid burden and associated biomarker cut-off points: results from the first amyloid Singaporean cohort with overlapping cerebrovascular disease. Eur J Nucl Med Mol Imaging 47, 319–331 (2020). https://doi.org/10.1007/s00259-019-04642-8

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