Evaluation of normal tissue complication probability (NTCP) dose-response models predicting acute Pneumonitis in patients treated with conformal radiation therapy for non-small cell lung cancer, and development of a NTCP calculation software tool

• Κύριος συγγραφέας: Grout, Ioannis
• Άλλοι συγγραφείς: Τσούγκος, Ιωάννης
• Μορφή: Thesis
• Γλώσσα: English
• Έκδοση: 2007
• Θέματα: Radiobiological-models; Normal Tissue Complications; Ραδιοβιολογικά μοντέλα; Μετακτινικές επιπλοκές σε υγιείς ιστούς; 612.014 48

A set of mathematical models, known as radiobiological Dose-Response models, have been developed, to model the biological effects and complications that arise following irradiation. The overall objective is to be able to apply these in clinical practice with confidence, and ensure more successful treatments are given to patients. This investigation serves to assess these models and their predictive power of NTCP following irradiation of the lung. Clinical data, from patients treated for inoperable stage III non-small cell lung cancer is obtained and the consequent biological effect (severity of pneumonitis) observed as a result of this radiation treatment is assessed by the models. By gaining more knowledge about the 3D dose-distribution and the incidence of radiation pneumonitis through the evaluation of the models, the main treatment goal, which is to maximise TCP and minimise NTCP can be achieved. Post treatment data is obtained regarding the clinical outcome or clinical endpoint for each patient, considered to be Radiation Pneumonitis. The clinical endpoint is a specific biological effect that may or may not have occurred,after a certain period, following irradiation. The models are assessed on their ability to predict a NTCP value that corresponds to the resulting clinical endpoint following treatment. Furthermore a software tool for the calculation of NTCP’s by the models is developed, in an attempt to provide an important tool for optimization of radiotherapy treatment planning. With our findings from this study, our aim is to further strengthen, support and challenge already existing literature on dose-response modelling.