Mathematical Optimization of Radiation Therapy Goal Fulfillment

Cancer is one of the deadliest diseases today, and with increasingly larger and older populations, cancer constitutes an enormous contemporary and future challenge. Luckily, advances in technology and medicine are continuously contributing to a decrease in cancer mortality, and to the reduction of treatment side effects. The aim of this Master's thesis is to be a part of these advances, thereby increasing the survival chances and well-being of future cancer patients. The thesis regards specifically the improvement of radiation therapy, a form of treatment utilized in both curative and palliative cancer care. In radiation therapy, ionizing radiation is directed at cancerous cells in the body. The radiation prevents the further proliferation of malignant cells by damaging their DNA. However, the radiation is also harmful to healthy cells. It is therefore of utmost importance that the irradiation of the patient is done in such a way to spare the critical organs in the vicinity of the tumor. To obtain the best possible treatment, mathematical optimization algorithms are utilized. Using physical models of how radiation travels in the body, it is possible to calculate what effect the irradiation of the patient will have. To quantify the quality of the treatment, mathematical functions are used, which evaluate the radiation dose under certain criteria. Once these functions are defined, algorithms can be applied that find the optimal treatment with regard to the given criteria. The formulation of these functions and their properties is the main focus of this thesis. Using clinical evaluation criteria previously used to assess treatments, a framework for optimizing functions that directly correlate to the clinical goals is constructed. The framework is examined and used to generate radiation therapy plans for three cancer patients. In each of the cases, the constructed treatment plans demonstrate high quality, often better than or comparable to the plans created by experienced dose planners using existing tools. A particularly interesting application of the developed framework is the automatic generation of treatments. This relies on the clinician giving the clinical goals as input to the algorithm. A plan is then generated with maximal goal fulfillment. This eliminates the tedious and time consuming process of parameter tuning to achieve a satisfactory plan. Several studies have demonstrated the ability of automatic planning to retain the plan quality while substantially improving planning efficiency