On the Interplay between Robustness and Dynamic Planning for Adaptive Radiation Therapy

Interfractional geometric uncertainties can lead to deviations of the actual delivered dose from the prescribed dose distribution. To better handle these uncertainties during treatment, the authors propose a dynamic framework for robust adaptive radiation therapy in which a variety of robust adaptive treatment strategies are introduced and evaluated. This variety is a result of optimization variables with various degrees of freedom within robust optimization models that vary in their grade of conservativeness. We formulate an adaptive framework with dependence on time and/or uncertainty or neither. The framework is evaluated with either expected-value-, worst-case-, or conditional-value-at-risk-(CVaR)-optimization. The goal of this study is to identify the mathematical properties of the proposed robust adaptive strategies in order to restore the planned dose distribution. We apply a result from convex analysis to show that in our framework, the time-dependent problem has a time-independent optimal solution. This implies that the time-dependent problem can be solved by dynamically solving the corresponding time-independent problem. This is especially relevant in a clinical context, since it implies that adaptive radiation therapy can be implemented in clinical practice using commercially available treatment planning systems. We demonstrate that time-and-scenario-dependent variables are most compatible with worst-case-optimization and that including dose prediction potentially improves treatment compared to non-adaptive planning. Contrarily, time-and-scenario-independent variables appear most compatible with expected-value-optimization. Non-adaptive robust strategies may provide sufficient target coverage, comparable to robust adaptive strategies, if the occurring uncertainties follow the same distribution as those included in the robust model.