Simulation of hypoxia PET-tracer uptake in tumours:Dependence of clinical uptake-values on transport parameters and arterial input function

Poor radiotherapy outcome is in many cases related to hypoxia, due to the increased radioresistance of hypoxic tumour cells. Positron emission tomography may be used to non-invasively assess the oxygenation status of the tumour using hypoxia-specific radiotracers. Quantification and interpretation of these images remains challenging, since radiotracer binding and oxygen tension are not uniquely related. Computer simulation is a useful tool to improve the understanding of tracer dynamics and its relation to clinical uptake parameters currently used to quantify hypoxia. In this study, a model for simulating oxygen and radiotracer distribution in tumours was implemented to analyse the impact of physiological transport parameters and of the arterial input function (AIF) on: oxygenation histograms, time-activity curves, tracer binding and clinical uptake-values (tissue-to-blood ratio, TBR, and a composed hypoxia-perfusion metric, FHP). Results were obtained for parallel and orthogonal vessel architectures and for vascular fractions (VFs) of 1% and 3%. The most sensitive parameters were the AIF and the maximum binding rate (K-max). TBR allowed discriminating VF for different AIF, and FHP for different K-max, but neither TBR nor FHP were unbiased in all cases. Biases may especially occur in the comparison of TBR- or FHP-values between different tumours, where the relation between measured and actual AIF may vary. Thus, these parameters represent only surrogates rather than absolute measurements of hypoxia in tumours.Pontificia Universidad Catolica de Chile (UC) from the German Academic Exchange Service (DAAD) German Cancer Research Center (DKFZ) from the German Academic Exchange Service (DAAD) grant CONICYT Doctorado Nacional 21151353 Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT) CONICYT FONDECYT 111505601 1117057

Validation of an oxygen-dependent tumour response simulation model in three sublines of a rat prostate carcinoma

Radiotherapy outcome of poorly oxygenated tumours is currently limited. Mathematical models describing the oxygen-dependent tumour response may help to optimise treatment schedules and improve radiotherapy outcome. The tumour response model (TRM) predicts the spatiotemporal development of tumours based on radiation dose, microscopic oxygenation distributions, proliferation of tumour cells, angiogenesis, tumour growth, resorption of dead tumour cells and tumour shrinkage. In this thesis, the TRM was validated and its input parameters were adjusted to reproduce experimental dose-response curves of three rat prostate carcinoma sublines. The validation confirmed the correct implementation of the main TRM components and the dependence on input parameters were consistent with underlying principles. The adjustment to experimental data could only be achieved after changing the assumption on oxygen consumption of radiation-inactivated tumour cells. The adjusted intrinsic fractionation parameter,α/ß , was smaller than the experimentally obtained value, revealing the impact of additional biological processes on the tumour response to fractionated irradiations. Additionally, available experimental α/ß and α values were compatible with the values adjusted in the TRM. This study demonstrates the ability of the TRM to reproduce experimental in-vivo tumour response data

ISARIC-COVID-19 dataset: A Prospective, Standardized, Global Dataset of Patients Hospitalized with COVID-19

The International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) COVID-19 dataset is one of the largest international databases of prospectively collected clinical data on people hospitalized with COVID-19. This dataset was compiled during the COVID-19 pandemic by a network of hospitals that collect data using the ISARIC-World Health Organization Clinical Characterization Protocol and data tools. The database includes data from more than 705,000 patients, collected in more than 60 countries and 1,500 centres worldwide. Patient data are available from acute hospital admissions with COVID-19 and outpatient follow-ups. The data include signs and symptoms, pre-existing comorbidities, vital signs, chronic and acute treatments, complications, dates of hospitalization and discharge, mortality, viral strains, vaccination status, and other data. Here, we present the dataset characteristics, explain its architecture and how to gain access, and provide tools to facilitate its use