Modeling geometric uncertainties in radiation therapy

Patient repositioning and organ motion lead to uncertainty in targeting the tumor in radiation therapy. This decreases the dose to the tumor and increases the dose to healthy tissues. Ultimately, tumor control is reduced and complications are increased. This work investigates the hypothesis that modeling geometric uncertainties can accurately estimate the dose distribution delivered when uncertainties are present. These modeling results provide a more accurate representation of the delivered dose distribution than present approaches. These uncertainties are conventionally addressed by adding a margin to the clinical target volume to define a planning target volume (PTV). Despite widespread use, some PTV implementation details have not been addressed. A mathematical model is developed to investigate these details and leads to recommendations for clinical implementation. However, limitations remain when using a PTV. A superior method of accounting for geometric uncertainties incorporates their effect into the dose calculation. This can be achieved by convolution of the planned dose distribution with a probability density function describing the uncertainty. Two assumptions in this model are that the dose distribution is shift invariant and that treatment extends over an infinite number of fractions. The errors resulting from each assumption are quantified. Assuming shift invariance leads to large errors near the patient surface. A 'Corrected Convolution' method that reduces these errors was developed. Errors due to finite fractionation are large for very few fractions (hypofractionation), but are unlikely to impact treatment plan evaluation. The impact of geometric uncertainties for hypofractionated prostate cancer treatment is further explored. Hypofractionated treatments were simulated to quantify the impact of geometric uncertainties. The results suggest geometric uncertainties will not limit the clinical effectiveness of prostate hypofractionation. Convolution can assess the sensitivity of different techniques to geometric uncertainties. Simplified intensity modulated arc therapy plans were compared to conventional techniques. The magnitude of the change caused by geometric uncertainties varied by technique. Contrary to common assumptions, geometric uncertainties do not always result in a worse treatment than planned. In conclusion, existing methods to account for geometric uncertainties are limited. Modeling geometric uncertainties with convolution has the potential to improve clinical treatment decisions