Amyloid imaging refers to a diagnostic examination that allows for the in-vivo detection of amyloid aggregation, considered a pathological hallmark of Alzheimer's disease (AD). The technique of choice for amyloid imaging is PET with appropriate radioligands, which has become a key tool in the diagnosis and research framework of AD. Due to the non-straightforward relationship between the presentation of the disease and the underlying molecular pathology, the diagnosis based purely on the clinical manifestation is a non-trivial task. Therefore, a great interest has developed around the methodologies for the assessment of in-vivo biomarkers (such as the amyloid aggregation) that can be used to complement the clinical evaluation and provide direct evidence of the core features of the pathology. There are several evolving aspects related to the analysis of amyloid PET in clinical setting. For example, it is becoming of common wisdom that the dichotomous classification - classically used in clinical practice - based solely on visual analysis is inadequate, as it does not provide information on the level of positivity and is used to describe a phenomenon that is gradual and can last up to 15 years. Quantitative approaches that provide numerical estimates of the physiological processes of interest may help by giving the opportunity of ranking brain amyloidosis to find out, for example, patients who would benefit from a treatment. These approaches are constantly evolving and there is currently no consensus on which is the best way to perform a quantitative analysis of amyloid PET images or which is the most feasible in clinical setting. Other relevant issues are related to the differences between radiotracers and to image quality factors that that can affect the interpretation of the scan even for expert readers even when supported by quantification. Another poorly understood aspect is the potential information that is lost in amyloid PET images using current analysis pipelines. Up to now, the amyloid imaging data have mainly been used for the definition of global amyloid profile. Recent studies have shown that this imaging modality can provide much more detailed information when assessed at regional level. In this context, my research focused mainly on two aspects of amyloid PET assessment. The first is related to technical issues that can arise in the evaluation of the scans, such as the application of an appropriate analysis method to different radiotracers, the comparison of multiple-reader visual evaluations and the effect of image quality. A novel quantitative approach has also been developed, validated and compared to both standard and highly sophisticated techniques. The second aspect is more clinical-related and has to do with the possible interpretation of regional amyloid burden and the assessment of the relationship between the regional load, the cognitive decline and the regional tau (the other major biomarker in Alzheimer’s disease).

Advances and perspectives in amyloid PET quantitative interpretation

PEIRA, ENRICO
2022

Abstract

Amyloid imaging refers to a diagnostic examination that allows for the in-vivo detection of amyloid aggregation, considered a pathological hallmark of Alzheimer's disease (AD). The technique of choice for amyloid imaging is PET with appropriate radioligands, which has become a key tool in the diagnosis and research framework of AD. Due to the non-straightforward relationship between the presentation of the disease and the underlying molecular pathology, the diagnosis based purely on the clinical manifestation is a non-trivial task. Therefore, a great interest has developed around the methodologies for the assessment of in-vivo biomarkers (such as the amyloid aggregation) that can be used to complement the clinical evaluation and provide direct evidence of the core features of the pathology. There are several evolving aspects related to the analysis of amyloid PET in clinical setting. For example, it is becoming of common wisdom that the dichotomous classification - classically used in clinical practice - based solely on visual analysis is inadequate, as it does not provide information on the level of positivity and is used to describe a phenomenon that is gradual and can last up to 15 years. Quantitative approaches that provide numerical estimates of the physiological processes of interest may help by giving the opportunity of ranking brain amyloidosis to find out, for example, patients who would benefit from a treatment. These approaches are constantly evolving and there is currently no consensus on which is the best way to perform a quantitative analysis of amyloid PET images or which is the most feasible in clinical setting. Other relevant issues are related to the differences between radiotracers and to image quality factors that that can affect the interpretation of the scan even for expert readers even when supported by quantification. Another poorly understood aspect is the potential information that is lost in amyloid PET images using current analysis pipelines. Up to now, the amyloid imaging data have mainly been used for the definition of global amyloid profile. Recent studies have shown that this imaging modality can provide much more detailed information when assessed at regional level. In this context, my research focused mainly on two aspects of amyloid PET assessment. The first is related to technical issues that can arise in the evaluation of the scans, such as the application of an appropriate analysis method to different radiotracers, the comparison of multiple-reader visual evaluations and the effect of image quality. A novel quantitative approach has also been developed, validated and compared to both standard and highly sophisticated techniques. The second aspect is more clinical-related and has to do with the possible interpretation of regional amyloid burden and the assessment of the relationship between the regional load, the cognitive decline and the regional tau (the other major biomarker in Alzheimer’s disease).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/1081904
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