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Tanaka T, Stephenson MC, Nai YH, Khor D, Saridin FN, Hilal S, Villaraza S, Gyanwali B, Ihara M, Vrooman H, Weekes AA, Totman JJ, Robins EG, Chen CP, Reilhac A. 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 2019; 47:319-331. [PMID: 31863136 DOI: 10.1007/s00259-019-04642-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Accepted: 11/26/2019] [Indexed: 12/19/2022]
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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Affiliation(s)
- Tomotaka Tanaka
- Clinical Imaging Research Centre, National University of Singapore, 14 Medical Drive, #B1-01, Singapore, 117599, Singapore. .,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. .,Department of Neurology, National Cerebral and Cardiovascular Center, 5-7-1 Fujishiro-dai, Suita, Osaka, 565-8565, Japan.
| | - Mary C Stephenson
- Clinical Imaging Research Centre, National University of Singapore, 14 Medical Drive, #B1-01, Singapore, 117599, Singapore
| | - Ying-Hwey Nai
- Clinical Imaging Research Centre, National University of Singapore, 14 Medical Drive, #B1-01, Singapore, 117599, Singapore
| | - Damian Khor
- Department of Diagnostic Imaging, National Cancer Institute of Singapore, 11 Hospital Drive, Singapore, 169610, Singapore
| | - Francis N Saridin
- 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
| | - Saima Hilal
- 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.,Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Steven Villaraza
- 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
| | - Bibek Gyanwali
- 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
| | - Masafumi Ihara
- Department of Neurology, National Cerebral and Cardiovascular Center, 5-7-1 Fujishiro-dai, Suita, Osaka, 565-8565, Japan
| | - Henri Vrooman
- Biomedical Imaging group Rotterdam, Erasmus MC, University Medical Center, P.O. Box 2040, 3000 CA, Rotterdam, Netherlands
| | - Ashley A Weekes
- Clinical Imaging Research Centre, National University of Singapore, 14 Medical Drive, #B1-01, Singapore, 117599, Singapore
| | - John J Totman
- Clinical Imaging Research Centre, National University of Singapore, 14 Medical Drive, #B1-01, Singapore, 117599, Singapore
| | - Edward G Robins
- Clinical Imaging Research Centre, National University of Singapore, 14 Medical Drive, #B1-01, Singapore, 117599, Singapore.,Singapore Bioimaging Consortium, Agency for Science, A*Star,1Fusionopolis way, #20-10 Connexis North Tower, Singapore, 138632, Singapore
| | - Christopher P Chen
- 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
| | - Anthonin Reilhac
- Clinical Imaging Research Centre, National University of Singapore, 14 Medical Drive, #B1-01, Singapore, 117599, Singapore
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