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

Stephenson, Mary C.; Nai, Ying-Hwey; Khor, Damian; Saridin, Francis; Hilal, Saima; Villaraza, Steven; Gyanwali, Bibek; Ihara, Masafumi; Vrooman, Henri A.; Weekes, Ashley Ann; Totman, John J.; Robins, Edward G.; Chen, Christopher; Reilhac, Anthonin

doi:10.1007/s00259-019-04642-8

Cited by 19 publications

(23 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The removal of the NS can be seen as an efficient spillover correction that not only removes the source of contamination and variability originating from the WM but also the NS binding in the GM. As a matter of fact, no attempt was made in this work to correct the data for partial volume effects as most correction approaches rely on having a segmented MRI spatially registered with the PET image, and segmentation methods do not always perform well in the presence of CeVD [ 7 ]. In addition, these methods often assume a homogeneous activity uptake within each region, an assumption that would be often violated with the manifestation of CeVD.…”

Section: Discussionmentioning

confidence: 99%

“…All [ 11 C]PiB-PET images were visually assessed by a junior PET image analysis researcher, a senior neuro-PET researcher, and a certified neurologist using the rainbow 16-bit scale on the Siemens Syngo platform (Siemens AG, Germany) in the transaxial plane and also in the sagittal and coronal planes when needed. Scans were visually interpreted as positive or negative based on 6 AD-specific regions: frontal lobe, parietal lobe, temporal lobe, anterior cingulate, precuneus/posterior cingulate [ 7 ].…”

Section: Methodsmentioning

confidence: 99%

“…In this work, we propose a novel Aβ-PET biomarker, named hereafter SAβ L , that is obtained from the subtraction in native space of the subject’s NS uptake volume, estimated with the optimal network, from the SUVr volume. For performance comparison, global SUVr values, measured in native space using the parcellated MRI, and global Aβ load (Aβ L ), measured in MNI-space, were derived following methods previously described [ 7 ] (supplementary material 2 ).…”

Section: Methodsmentioning

confidence: 99%

“…However, Aβ radiotracers also bind nonspecifically to myelin protein in the white matter (WM), which also has β-sheet structure as the Aβ plaques [ 5 , 6 ]. This nonspecific (NS) uptake in WM is subjected to global and local variations caused by factors such as age as well as the presence of cerebrovascular disease (CeVD) as detected by WM-hyperintensities (WMH), lacunes, cortical infarcts, and cerebral microbleeds (CMBs) [ 6 , 7 ]. Aβ is typically quantified within the cortical gray matter (GM) regions, which also contains myelinated axons [ 8 ], leading to the contamination of unwanted NS signals with specific Aβ uptake.…”

Section: Introductionmentioning

confidence: 99%

“…We have recently proposed a novel Aβ quantification method for static Aβ PET images yielding two computed biomarkers: Aβ L , which quantifies the global Aβ burden, and ns, which characterizes the NS uptake [ 7 ]. Both markers are obtained via modeling of the SUVr PET image in standardized MNI-space, as a linear combination of two template images derived from a pool of subjects, describing the amyloid deposition pattern and the NS binding.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Improved amyloid burden quantification with nonspecific estimates using deep learning

Liu

Nai

Saridin

et al. 2021

Eur J Nucl Med Mol Imaging

Self Cite

View full text Add to dashboard Cite

Purpose Standardized uptake value ratio (SUVr) used to quantify amyloid-β burden from amyloid-PET scans can be biased by variations in the tracer’s nonspecific (NS) binding caused by the presence of cerebrovascular disease (CeVD). In this work, we propose a novel amyloid-PET quantification approach that harnesses the intermodal image translation capability of convolutional networks to remove this undesirable source of variability. Methods Paired MR and PET images exhibiting very low specific uptake were selected from a Singaporean amyloid-PET study involving 172 participants with different severities of CeVD. Two convolutional neural networks (CNN), ScaleNet and HighRes3DNet, and one conditional generative adversarial network (cGAN) were trained to map structural MR to NS PET images. NS estimates generated for all subjects using the most promising network were then subtracted from SUVr images to determine specific amyloid load only (SAβL). Associations of SAβL with various cognitive and functional test scores were then computed and compared to results using conventional SUVr. Results Multimodal ScaleNet outperformed other networks in predicting the NS content in cortical gray matter with a mean relative error below 2%. Compared to SUVr, SAβL showed increased association with cognitive and functional test scores by up to 67%. Conclusion Removing the undesirable NS uptake from the amyloid load measurement is possible using deep learning and substantially improves its accuracy. This novel analysis approach opens a new window of opportunity for improved data modeling in Alzheimer’s disease and for other neurodegenerative diseases that utilize PET imaging.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Improved amyloid burden quantification with nonspecific estimates using deep learning

Liu

Nai

Saridin

et al. 2021

Eur J Nucl Med Mol Imaging

Self Cite

View full text Add to dashboard Cite

show abstract

Evaluation of novel data‐driven metrics of amyloid β deposition for longitudinal PET studies

Bollack¹,

Cash

Markiewicz³

et al. 2022

Alzheimer's & Dementia

View full text Add to dashboard Cite

BackgroundPositron emission tomography (PET) provides in vivo quantification of amyloid‐β (Aβ) pathology. The most common method for assessing PET is standardized uptake value ratio (SUVr), which can be affected by physiological and technical factors. Novel, data‐driven metrics have been developed to address these sources of variability. We evaluate cross‐sectional and longitudinal performance of four data driven metrics.MethodThree cohorts were used for evaluation: Insight 46, a neuroimaging sub‐study of the 1946 British birth cohort, Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing, and a test‐retest flutemetamol dataset (Table 1). Data from participants were included if good quality volumetric MRI and amyloid PET, using florbetapir (Insight 46) or flutemetamol (AIBL and test‐retest data), was available. No partial volume correction was applied. Four data driven metrics were extracted: the Centiloid derived from non‐negative matrix factorisation (CLNMF), the Aβ PET pathology accumulation index (Aβ‐index), amyloid load (Aβ‐load), and amyloid pattern similarity score (AMPSS). These data‐driven metrics were compared to a global composite SUVr, with either a pons (flutametamol) or cerebellar grey matter (florbetapir) reference region, and a Centiloid (CL) score. They were evaluated by measures of repeatability in test‐retest data, associations with non‐displaceable binding potential (BPND, computed from Logan graphical analysis), sample size estimates to detect a 20% slowing in Aβ accumulation with 95% significance and 80% power, and accuracy in predicting the second follow‐up visit.ResultAll metrics show good to excellent reliability. The repeatability is highly influenced by the amyloid burden, the range, and the offset of each metric. (Table 1) All metrics are strongly correlated to the BPND (R 2: 0.8 to 0.94), with SUVr and CL explaining more variance in BPND than the data‐driven metrics. (Figure) Sample size estimates were lowest in CL and CLNMF compared to the SUVr. (Table 2) The Aβ‐index had the best predictive power over ∼3 years while the other metrics were comparable. (Table 3)ConclusionNovel data driven metrics provide comparable performance in terms of accuracy and performance to more established quantification methods of Aβ PET tracer uptake, but with additional benefits, such as being MRI‐free, reference region independent, or more robust to change in tracer.

show abstract

AMYQ: An index to standardize quantitative amyloid load across PET tracers

Pegueroles

Montal

Bejanin

et al. 2021

Alzheimer's & Dementia

View full text Add to dashboard Cite

Introduction Positron emission tomography (PET) amyloid quantification methods require magnetic resonance imaging (MRI) for spatial registration and a priori reference region to scale the images. Furthermore, different tracers have distinct thresholds for positivity. We propose the AMYQ index, a new measure of amyloid burden, to overcome these limitations. Methods We selected 18F‐amyloid scans from ADNI and Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL) with the corresponding T1‐MRI. A subset also had neuropathological data. PET images were normalized, and the AMYQ was calculated based on an adaptive template. We compared AMYQ with the Centiloid scale on clinical and neuropathological diagnostic performance. Results AMYQ was related with amyloid neuropathological burden and had excellent diagnostic performance to discriminate controls from patients with Alzheimer's disease (AD) (area under the curve [AUC] = 0.86). AMYQ had a high agreement with the Centiloid scale (intraclass correlation coefficient [ICC] = 0.88) and AUC between 0.94 and 0.99 to discriminate PET positivity when using different Centiloid cutoffs. Discussion AMYQ is a new MRI‐independent index for standardizing and quantifying amyloid load across tracers.

show abstract

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

Cited by 19 publications

References 33 publications

Improved amyloid burden quantification with nonspecific estimates using deep learning

Improved amyloid burden quantification with nonspecific estimates using deep learning

Evaluation of novel data‐driven metrics of amyloid β deposition for longitudinal PET studies

AMYQ: An index to standardize quantitative amyloid load across PET tracers

Contact Info

Product

Resources

About