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. The first two appended papers address mitigating uncertainty related to localizing the tumor and the relevant organs-at-risk (OARs). In Paper A, we consider phantom cases with isotropic, microscopic tumor infiltration around a visible tumor. We compare minimization of the expected value of the objective function to the conventional minimization of an objective function applied to a margin designed to contain the tumor with sufficient probability. The results show that the approach can improve the sparing of a nearby OAR, at the expense of increasing the total dose. In Paper B, we compare multiple formulations of the objective function under contour uncertainty, given a non-isotropic uncertainty model represented by a set of contour scenarios. At comparable tumor dose, margins derived from the scenarios outperform methods from clinical practice in terms of sparing OARs and limiting the total dose. In comparison, considering the scenarios explicitly, including minimizing the expected value of the objective function over the scenarios, spares the OARs further at the expense of total dose. The three subsequent papers 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. It is applied to lung cancer cases with synthesized, irregular breathing motion, and the results indicate that it outperforms the 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 PaperE, 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.