Statistical considerations in the kinetic analysis of PET-FDG brain tumour studies

Dynamic positron emission tomography (PET) imaging can be used to track the distribution of injected radio-labelled molecules over time in vivo. This is a powerful technique, which provides researchers and clinicians the opportunity to study the status of healthy and pathological tissue by examining how it processes substances of interest. Widely used tracers include 18F-uorodeoxyglucose, an analog of glucose, which is used as the radiotracer in over ninety percent of PET scans. This radiotracer provides a way of quantifying the distribution of glucose utilisation in vivo. The interpretation of PET time-course data is complicated because the measured signal is a combination of vascular delivery and tissue retention effects. If the arterial time-course is known, the tissue time-course can typically be expressed in terms of a linear convolution between the arterial time-course and the tissue residue function. As the residue represents the amount of tracer remaining in the tissue, this can be thought of as a survival function; these functions been examined in great detail by the statistics community. Kinetic analysis of PET data is concerned with estimation of the residue and associated functionals such as ow, ux and volume of distribution. This thesis presents a Markov chain formulation of blood tissue exchange and explores how this relates to established compartmental forms. A nonparametric approach to the estimation of the residue is examined and the improvement in this model relative to compartmental model is evaluated using simulations and cross-validation techniques. The reference distribution of the test statistics, generated in comparing the models, is also studied. We explore these models further with simulated studies and an FDG-PET dataset from subjects with gliomas, which has previously been analysed with compartmental modelling. We also consider the performance of a recently proposed mixture modelling technique in this study.

Metabolically healthy obesity across the life course: epidemiology, determinants and implications

In recent years, different subphenotypes of obesity have been described, including metabolically healthy obesity (MHO), in which a proportion of obese individuals, despite excess body fat, remain free of metabolic abnormalities and increased cardiometabolic risk. In the absence of a universally accepted set of criteria to classify MHO, the reported prevalence estimates vary widely. Our understanding of the determinants and stability of MHO over time and the associated cardiometabolic and mortality risks is improving, but many questions remain. For example, whether MHO is truly benign is debatable, and whether risk stratification of obese individuals on the basis of their metabolic health status may offer new opportunities for more personalized approaches in diagnosis, intervention, and treatment of diabetes remains speculative. Furthermore, as most of the research to date has focused on MHO in adults, little is known about childhood MHO. In this review, we focus on the epidemiology, determinants, stability, and health implications of MHO across the life course.