Toxicity of the respiratory system is quite common after radiotherapy in thoracic tumours. The quantification of lung tissue response to irradiation is important in designing treatments associated with a minimum of complications and maximum tumor control. This work aims to estimate volumes V13, V20 and V30 as an index of radiation pneumonitis occurrence, to evaluate the predictive strength of the relative seriality, Lyman-Kutcher-Burman(LKB) and parallel normal tissue complication probability (NTCP) models regarding the incidence of radiation pneumonitis in a group of patients following lung cancer radiotherapy when lung perceived as paired and single organ respectively and also software development for the determination of the best estimates of the models’ parameters based on maximum likelihood method. The study was based on 46 patients and for each patient, lung dose-volume histograms (DVHs) and the clinical treatment outcome was available. From the 46 patients treated, 28 of them were scored as having radiation induced pneumonitis, with RTOG criteria grade ≥2. Firstly lungs were evaluated as a paired organ. Analyzing this material we failed to associate volume V13, V20 and V30 with radiation pneumonitis occurrence (χ2-test: probability of agreement between observed and predicted results using the 0.05 significance level). By applying ANOVA of the NTCP models examined in the overall group considering lungs as paired organs the LKB with Martel et al parameter set gave the best results, whereas when lungs perceived as individual organ (unhealthy lung volume-PTV) the best model was appeared to be LKB with Burman et al parameter set. However, in this relatively small group of lung cancer patients NTCP models didn’t show excessive correlation with the clinical outcome. Nevertheless, when total lung volume irradiated and total dose received were taken into account as factors of radiation pneumonitis prediction, correlation was almost duplicated for both perception of lungs. In order to achieve the best fitting of models to the clinical outcome for the specific patient group, maximum likelihood analysis was applied via software development using mle programming language, to find those parameters that maximize the likelihood function. When lungs perceived as single organ, the best fitting of models to the clinical outcome for relative seriality were D50 = 22Gy, γ= 2, s=0.031, LKB model D50 = 23Gy, m=0.18, n=1 and for parallel model, D50 = 20Gy, m=0.2, n=0.6. Maximum likelihood analysis was not applied for paired lung assumption as constraints did not allow us to properly fit the models. / -

The search for the optimal choice of treatment time, dose and fractionation regimen is one of the major challenges in radiation therapy. Several aspects of the radiation response of tumours and normal tissues give different indications of how the parameters defining a fractionation schedule should be altered relative to each other which often results in contradictory conclusions. For example, the increased sensitivity to fractionation in late-reacting as opposed to early-reacting tissues indicates that a large number of fractions is beneficial, while the issue of accelerated repopulation of tumour cells starting at about three weeks into a radiotherapy treatment would suggest as short overall treatment time as possible. Another tumour-to-normal tissue differential relevant to the sensitivity as well as the fractionation and overall treatment time is the issue of tumour hypoxia and reoxygenation. The tumour oxygenation is one of the most influential factors impacting on the outcome of many types of treatment modalities. Hypoxic cells are up to three times as resistant to radiation as well-oxygenated cells, presenting a significant obstacle to overcome in radiotherapy as solid tumours often contain hypoxic areas as a result of their poorly functioning vasculature. Furthermore, the oxygenation is highly dynamic, with changes being observed both from fraction to  fraction and over a time period of weeks as a result of fast and slow reoxygenation of acute and chronic hypoxia. With an increasing number of patients treated with hypofractionated stereotactic body radiotherapy (SBRT), the clinical implications of a substantially reduced number of fractions and hence also treatment time thus have to be evaluated with respect to the oxygenation status of the tumour. One of the most promising tools available for the type of study aiming at determining the optimal radiotherapy approach with respect to fractionation is radiobiological modelling. With clinically validated in vitro-derived tissue-specific radiobiological parameters and well-established survival models, in silico modelling offers a wide range of opportunities to test various hypotheses with respect to time, dose, fractionation and details of the tumour microenvironment. Any type of radiobiological modelling study intended to provide a realistic representation of a clinical tumour should therefore take into account details of both the spatial and temporal tumour oxygenation. This thesis presents the results of three-dimensional radiobiological modelling of the response of tumours with heterogeneous oxygenation to various fractionation schemes, and oxygenation levels and dynamics using different survival models. The results of this work indicate that hypoxia and its dynamics play a major role in the outcome of radiotherapy, and that neglecting the oxygenation status of tumours treated with e.g. SBRT may compromise the treatment outcome substantially. Furthermore, the possibilities offered by incorporating modelling into the clinical routine are explored and demonstrated by the development of a new calibration function for converting the uptake of the hypoxia-PET tracer 18F-HX4 to oxygen partial pressure, and applying it for calculations of the doses needed to overcome hypoxia-induced radiation resistance. By hence demonstrating how the clinical impact of hypoxia on dose prescription and the choice of fractionation schedule can be investigated, this project will hopefully advance the evolution towards routinely incorporating functional imaging of hypoxia into treatment planning. This is ultimately expected to result in increased levels of local control with more patients being cured from their cancer. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 2: Manuscript. Paper 6: Manuscript.</p>