Mitigating uncertainties in adaptive radiation therapy by robust optimization

The fractionated delivery of radiation therapy leads to discrepancies between the planning image and the patient geometry throughout the treatment course. Adaptive radiation therapy (ART) addresses this issue by modifying the plan based on additional image information acquired closer to the time of delivery. However, technologies used in ART introduce new uncertainties in the treatment modeling. This thesis deals with the mitigation of uncertainties that are introduced in the context of ART workflows.

Two papers address mitigating uncertainty related to localizing the tumor and the relevant organs-at-risk (OARs). In Papers A and B, we compare various formulations of the objective function for simple phantom cases and real retrospective cases of head-and-neck cancer. Notably, formulations based on the expected value of a conventional objective function improve OAR-sparing at the cost of increasing the total dose, compared to formulations based on treatment margins with similar target coverage.

Three papers then address motion-related uncertainty, which is particularly relevant in particle treatments. In Paper C, we investigate a robust optimization method that explicitly considers the radiation delivery’s time structure. The resultsindicate that it outperforms a conventional method that does not consider the time structure. In Paper D, we simulate the use of a real-time adaptive framework that re-optimizes the plan during delivery, based on the observed and anticipated patient motion. It is shown to have substantial dosimetric benefits, even under simplifying approximations that would facilitate an actual real-time implementation. In Paper E, we estimate the error associated with performing dose calculations that consider motion when the temporal resolution of the time-varying patient image is low. We apply a method to synthesize intermediate images and propose a temporal resolution required to mitigate the error.

Finally, in Paper F, we address some of the computational issues introduced by the robust optimization methods from the other papers. We propose methods that reduce the number of scenarios considered during robust optimization to reduce the associated computation times.