Preclinical Applications of 3'-Deoxy-3'-[18F]Fluorothymidine in Oncology - A Systematic Review

The positron emission tomography (PET) tracer 3'-deoxy-3'-[18F]fluorothymidine ([18F]FLT) has been proposed to measure cell proliferation non-invasively in vivo. Hence, it should provide valuable information for response assessment to tumor therapies. To date, [18F]FLT uptake has found limited use as a response biomarker in clinical trials in part because a better understanding is needed of the determinants of [18F]FLT uptake and therapy-induced changes of its retention in the tumor. In this systematic review of preclinical [18F]FLT studies, comprising 174 reports, we identify the factors governing [18F]FLT uptake in tumors, among which thymidine kinase 1 plays a primary role. The majority of publications (83 %) report that decreased [18F]FLT uptake reflects the effects of anticancer therapies. 144 times [18F]FLT uptake was related to changes in proliferation as determined by ex vivo analyses. Of these approaches, 77 % describe a positive relation, implying a good concordance of tracer accumulation and tumor biology. These preclinical data indicate that [18F]FLT uptake holds promise as an imaging biomarker for response assessment in clinical studies. Understanding of the parameters which influence cellular [18F]FLT uptake and retention as well as the mechanism of changes induced by therapy is essential for successful implementation of this PET tracer. Hence, our systematic review provides the background for the use of [18F]FLT in future clinical studies

FDG-PET imaging, EEG and sleep phenotypes as translational biomarkers for research in Alzheimer's disease

Peer reviewedPublisher PD

Direct estimation of kinetic parametric images for dynamic PET.

Dynamic positron emission tomography (PET) can monitor spatiotemporal distribution of radiotracer in vivo. The spatiotemporal information can be used to estimate parametric images of radiotracer kinetics that are of physiological and biochemical interests. Direct estimation of parametric images from raw projection data allows accurate noise modeling and has been shown to offer better image quality than conventional indirect methods, which reconstruct a sequence of PET images first and then perform tracer kinetic modeling pixel-by-pixel. Direct reconstruction of parametric images has gained increasing interests with the advances in computing hardware. Many direct reconstruction algorithms have been developed for different kinetic models. In this paper we review the recent progress in the development of direct reconstruction algorithms for parametric image estimation. Algorithms for linear and nonlinear kinetic models are described and their properties are discussed

Radiomics for Response and Outcome Assessment for Non-Small Cell Lung Cancer.

Routine follow-up visits and radiographic imaging are required for outcome evaluation and tumor recurrence monitoring. Yet more personalized surveillance is required in order to sufficiently address the nature of heterogeneity in nonsmall cell lung cancer and possible recurrences upon completion of treatment. Radiomics, an emerging noninvasive technology using medical imaging analysis and data mining methodology, has been adopted to the area of cancer diagnostics in recent years. Its potential application in response assessment for cancer treatment has also drawn considerable attention. Radiomics seeks to extract a large amount of valuable information from patients' medical images (both pretreatment and follow-up images) and quantitatively correlate image features with diagnostic and therapeutic outcomes. Radiomics relies on computers to identify and analyze vast amounts of quantitative image features that were previously overlooked, unmanageable, or failed to be identified (and recorded) by human eyes. The research area has been focusing on the predictive accuracy of pretreatment features for outcome and response and the early discovery of signs of tumor response, recurrence, distant metastasis, radiation-induced lung injury, death, and other outcomes, respectively. This review summarized the application of radiomics in response assessments in radiotherapy and chemotherapy for non-small cell lung cancer, including image acquisition/reconstruction, region of interest definition/segmentation, feature extraction, and feature selection and classification. The literature search for references of this article includes PubMed peer-reviewed publications over the last 10 years on the topics of radiomics, textural features, radiotherapy, chemotherapy, lung cancer, and response assessment. Summary tables of radiomics in response assessment and treatment outcome prediction in radiation oncology have been developed based on the comprehensive review of the literature

Bayesian model comparison for compartmental models with applications in positron emission tomography

We develop strategies for Bayesian modelling as well as model comparison, averaging and selection for compartmental models with particular emphasis on those that occur in the analysis of positron emission tomography (PET) data. Both modelling and computational issues are considered. Biophysically inspired informative priors are developed for the problem at hand, and by comparison with default vague priors it is shown that the proposed modelling is not overly sensitive to prior specification. It is also shown that an additive normal error structure does not describe measured PET data well, despite being very widely used, and that within a simple Bayesian framework simultaneous parameter estimation and model comparison can be performed with a more general noise model. The proposed approach is compared with standard techniques using both simulated and real data. In addition to good, robust estimation performance, the proposed technique provides, automatically, a characterisation of the uncertainty in the resulting estimates which can be considerable in applications such as PET