Evaluation of dose-response models and determination of several radiobiological parameters

• Κύριος συγγραφέας: Κούση, Ευανθία
• Άλλοι συγγραφείς: Κάππας, Κωνσταντίνος
• Μορφή: Thesis
• Γλώσσα: English
• Έκδοση: 2007
• Θέματα: Radiobiological modelling; Ραδιοβιολογικα μοντέλα στην ακτινοθεραπεία; 615.842

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.