Derivation and utility of an Aβ-PET pathology accumulation index to estimate Aβ load

Leuzy, Antoine; Lilja, Johan; Buckley, Christopher; Ossenkoppele, Rik; Palmqvist, Sebastian; Battle, Mark; Farrar, Gill; Thal, Dietmar Rudolf; Bateman, Randall J.; Stomrud, Erik; Strandberg, Olof; Smith, Ruben; Hansson, Oskar

doi:10.1212/wnl.0000000000011031

Cited by 15 publications

(20 citation statements)

References 48 publications

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“…3c), and the load/IQ method is also being developed for DaTscan in the context of Parkinson's disease. Other cross-tracer harmonization methods have also been proposed [163,164]. While amyloid and tau PET have been the initial focus of these approaches due to their widespread use in AD research, similar harmonization efforts could be performed for other tracers and molecular targets as the imaging tools and data become available.…”

Section: Cross-tracer Comparability: Centiloids and Amyloid Loadmentioning

confidence: 99%

The Use, Standardization, and Interpretation of Brain Imaging Data in Clinical Trials of Neurodegenerative Disorders

Schwarz

2021

Neurotherapeutics

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Imaging biomarkers play a wide-ranging role in clinical trials for neurological disorders. This includes selecting the appropriate trial participants, establishing target engagement and mechanism-related pharmacodynamic effect, monitoring safety, and providing evidence of disease modification. In the early stages of clinical drug development, evidence of target engagement and/or downstream pharmacodynamic effect—especially with a clear relationship to dose—can provide confidence that the therapeutic candidate should be advanced to larger and more expensive trials, and can inform the selection of the dose(s) to be further tested, i.e., to “de-risk” the drug development program. In these later-phase trials, evidence that the therapeutic candidate is altering disease-related biomarkers can provide important evidence that the clinical benefit of the compound (if observed) is grounded in meaningful biological changes. The interpretation of disease-related imaging markers, and comparability across different trials and imaging tools, is greatly improved when standardized outcome measures are defined. This standardization should not impinge on scientific advances in the imaging tools per se but provides a common language in which the results generated by these tools are expressed. PET markers of pathological protein aggregates and structural imaging of brain atrophy are common disease-related elements across many neurological disorders. However, PET tracers for pathologies beyond amyloid β and tau are needed, and the interpretability of structural imaging can be enhanced by some simple considerations to guard against the possible confound of pseudo-atrophy. Learnings from much-studied conditions such as Alzheimer’s disease and multiple sclerosis will be beneficial as the field embraces rarer diseases.

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Section: Cross-tracer Comparability: Centiloids and Amyloid Loadmentioning

confidence: 99%

The Use, Standardization, and Interpretation of Brain Imaging Data in Clinical Trials of Neurodegenerative Disorders

Schwarz

2021

Neurotherapeutics

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“…The regional SUV R was computed across 48 ROIs defined with a probabilistic atlas 49 . The Aβ index is a novel measure of amyloid burden 49, 51 corresponding to the total weight of global amyloid deposition; its value ranges between -1 (Aβ-negative appearance) and +1 (Aβ-positive appearance). In this study, amyloid PET quantification was not used to define amyloid status, which was solely based on expert visual reads.…”

Section: Methodsmentioning

confidence: 99%

Examining real-world Alzheimer’s disease heterogeneity using neuroanatomical normative modelling

Loreto

Verdi

Kia

et al. 2022

Preprint

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Alzheimer′s disease (AD) has been traditionally associated with episodic memory impairment and medial temporal lobe atrophy. However, recent literature has highlighted the existence of atypical forms of AD, presenting with different cognitive and radiological profiles. Failure to appreciate the heterogeneity of AD in the past has led to misdiagnoses, diagnostic delays, clinical trial failures and risks limiting our understanding of the disease. AD research requires the incorporation of new analytic methods that are as free as possible from the intragroup homogeneity assumption underlying case-control approaches according to which patients belonging to the same group are comparable to each other. Neuroanatomical normative modelling is a promising technique allowing for modelling the variation in neuroimaging profiles and then assessing individual deviations from the respective distribution. Here, neuroanatomical normative modelling was applied for the first time to a real-worldclinical cohortof Alzheimer′s disease patients (n=86) who had a positive amyloid PET scan and a T1-weighted MR performed as part of their diagnostic workup. The model indexed normal cortical thickness distributions using a separate healthy reference dataset (n= 33,072), employing hierarchical Bayesian regression to predict cortical thickness per region using age and sex. Transfer learning was used to recalibrate the normative model on avalidation cohort(n=20) of scanner-matched cognitively normal individuals. Brain heterogeneity was quantified as z-scores at each of the 148 ROIs generated within each AD patient. Z-scores < -1.96 defined as outliers. Clinical features including disease severity, presenting phenotypes and comorbidities were collected from health records to explore their association with outlier profiles. Amyloid quantification was performed using an automated PET-only driven method to examine the association between amyloid burden and outliers. The total number of individual outliers (total outlier count) in biomarker-confirmed AD clinical patients ranged between 1 and 120 out of 148 (median 21.5). The superior temporal sulcus was the region with the highest count of outliers (60%) in AD patients. The mean proportion of outliers was higher in the temporal (31.5%) than in the extratemporal (19.1%) regions and up to 20% of patients had no temporal outliers. We found higher mean outlier count in patients with non-amnestic phenotypes, at more advanced disease stages and without depressive symptoms. Amyloid burden was negatively associated with outlier count. This study corroborates the heterogeneity of brain atrophy in AD and provides evidence that this approach can be used to explore anatomo-clinical correlations at an individual level.

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“…Importantly, these packages generate a continuous measure of amyloid burden which can be used in addition to dichotomous visual reads. Currently available measures are the more commonly used standardised uptake value ratio (SUVr) [ 95 ], the Centiloid (CL) scale [ 74 , 96 ], and reference-based z -scores [ 97 ], while the more recent methods include the Aβ load [ 98 ], Aβ index [ 99 ], and AMYQ [ 100 ]. Both CL and z -scores are calculated based on SUVr, whereas the emerging methods use different approaches to select the target and reference regions for segmenting regions of interest (ROIs).…”

Section: Quantitative Measures For Clinical Assessment Of Amyloid Burdenmentioning

confidence: 99%

“…The Aβ PET pathology accumulation index does not require an MRI as it is based on a PET-driven principal component analysis (PCA) method [ 99 , 172 ]. The Aβ index corresponds to a weighting factor acquired during spatial normalisation of the images to MNI space using a previously described adaptive principal component template [ 172 ].…”

Section: Quantitative Measures For Clinical Assessment Of Amyloid Burdenmentioning

confidence: 99%

Quantification of amyloid PET for future clinical use: a state-of-the-art review

Pemberton

Collij

Heeman

et al. 2022

Eur J Nucl Med Mol Imaging

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Amyloid-β (Aβ) pathology is one of the earliest detectable brain changes in Alzheimer’s disease (AD) pathogenesis. The overall load and spatial distribution of brain Aβ can be determined in vivo using positron emission tomography (PET), for which three fluorine-18 labelled radiotracers have been approved for clinical use. In clinical practice, trained readers will categorise scans as either Aβ positive or negative, based on visual inspection. Diagnostic decisions are often based on these reads and patient selection for clinical trials is increasingly guided by amyloid status. However, tracer deposition in the grey matter as a function of amyloid load is an inherently continuous process, which is not sufficiently appreciated through binary cut-offs alone. State-of-the-art methods for amyloid PET quantification can generate tracer-independent measures of Aβ burden. Recent research has shown the ability of these quantitative measures to highlight pathological changes at the earliest stages of the AD continuum and generate more sensitive thresholds, as well as improving diagnostic confidence around established binary cut-offs. With the recent FDA approval of aducanumab and more candidate drugs on the horizon, early identification of amyloid burden using quantitative measures is critical for enrolling appropriate subjects to help establish the optimal window for therapeutic intervention and secondary prevention. In addition, quantitative amyloid measurements are used for treatment response monitoring in clinical trials. In clinical settings, large multi-centre studies have shown that amyloid PET results change both diagnosis and patient management and that quantification can accurately predict rates of cognitive decline. Whether these changes in management reflect an improvement in clinical outcomes is yet to be determined and further validation work is required to establish the utility of quantification for supporting treatment endpoint decisions. In this state-of-the-art review, several tools and measures available for amyloid PET quantification are summarised and discussed. Use of these methods is growing both clinically and in the research domain. Concurrently, there is a duty of care to the wider dementia community to increase visibility and understanding of these methods.

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Derivation and utility of an Aβ-PET pathology accumulation index to estimate Aβ load

Cited by 15 publications

References 48 publications

The Use, Standardization, and Interpretation of Brain Imaging Data in Clinical Trials of Neurodegenerative Disorders

The Use, Standardization, and Interpretation of Brain Imaging Data in Clinical Trials of Neurodegenerative Disorders

Examining real-world Alzheimer’s disease heterogeneity using neuroanatomical normative modelling

Quantification of amyloid PET for future clinical use: a state-of-the-art review

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