Dose computation in conformal radiation therapy including geometric uncertainties: Methods and clinical implications

The aim of any radiotherapy is to tailor the tumoricidal radiation dose to the target volume and to deliver as little radiation dose as possible to all other normal tissues. However, the motion and deformation induced in human tissue by ventilatory motion is a major issue, as standard practice usually uses only one computed tomography (CT) scan (and hence one instance of the patient's anatomy) for treatment planning. The interfraction movement that occurs due to physiological processes over time scales shorter than the delivery of one treatment fraction leads to differences between the planned and delivered dose distributions. Due to the influence of these differences on tumors and normal tissues, the tumor control probabilities and normal tissue complication probabilities are likely to be impacted upon in the face of organ motion.; In this thesis we apply several methods to compute dose distributions that include the effects of the treatment geometric uncertainties by using the time-varying anatomical information as an alternative to the conventional Planning Target Volume (PTV) approach. The proposed methods depend on the model used to describe the patient's anatomy. The dose and fluence convolution approaches for rigid organ motion are discussed first, with application to liver tumors and the rigid component of the lung tumor movements. For non-rigid behavior a dose reconstruction method that allows the accumulation of the dose to the deforming anatomy is introduced, and applied for lung tumor treatments. Furthermore, we apply the cumulative dose approach to investigate how much information regarding the deforming patient anatomy is needed at the time of treatment planning for tumors located in thorax. The results are evaluated from a clinical perspective. All dose calculations are performed using a Monte Carlo based algorithm to ensure more realistic and more accurate handling of tissue heterogeneities---of particular importance in lung cancer treatment planning.