Robust optimization considering uncertainties in adaptive proton therapy
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CCK Lecture Room
Cancercentrum Karolinska, Visionsgatan 56CCK Lecture Room
Proton therapy, a promising alternative to conventional photon therapy, has gained widespread acceptance in clinical practice. This is attributed to its superior depth-dose curve that has a negligible dose beyond the maximum range of the proton. A proton treatment planning requires a multitude of parameters and are either manually selected or optimized using mathematical formulation. However, a proton treatment plan is also subject to various systematic and random uncertainties that must be taken into account during optimization. Robust optimization is a commonly used method for integrating the setup and range uncertainties in proton therapy.
In addition to the uncertainties accounted for during the treatment planning phase, others can arise during the course of treatment and are often hard to predict. Changes in the patient's anatomy represent uncertainties that can significantly affect planned dose delivery. Therefore, adaptive planning is typically performed intermittently or regularly, depending on the changes in anatomy. Paper II included in this thesis proposed a method of adaptive planning that takes into account the impact of the patient's respiratory motion at the treatment site, such as the lungs and abdomen for 4D robust optimization. This method uses dose mimicking to reproduce the results as initially planned.
This additional stage of adaptive planning can introduce new complexities and uncertainties into the treatment process. One such uncertainty arise from daily cone beam computed tomography (CBCT) images which are required for treatment plan adaptation. Several strategies have been proposed in the past to improve the quality of these images, but each strategy has its advantages and disadvantages, depending on the site of treatment. In Paper I, a method was proposed that combined the advantages of other frequently used methods to create an improved method for generating daily images with CT-like image quality. This can contribute towards the goal of online adaptive in the near future with reduced uncertainties.
This thesis will provide a brief introduction and an in-depth chapter to elucidate the background, better understand the physics of proton therapy, the process of treatment planning, and the need for adaptive planning.
