A Multiobjective Solution Method for Radiation Treatment Planning

The challenge in radiation treatment planning (RTP) is to ensure delivery of a prescription dose to the tumor while limiting the normal tissue toxicity. One way of dealing with this trade off is to use multiobjective optimization which no longer possesses a unique optimal objective function value. In multiobjective optimization, efficient solutions are used instead of the optimal solution which have the property that no improvement in any objective is possible without sacrificing in at least one other objective. In this study, we use achievement scalarization to obtain efficient solutions, i.e. treatment plans which are efficient, for the RTP. We adapt the parameters of the achievement scalarization to address a solution in a rectangle that is defined by the bounds on the objective functions. For a given set of bounds on each structure of the treatment volume, the formulation is able to attain a treatment plan that targets the bounds. We tested our approach on 10 locally advanced head-and-neck cancer cases. All of the cases include three tumor volumes, primary tumor, high-risk nodal volume, low-risk nodal volume, and five organs-at-risk (OAR), left and parotids, spinal cord, brain stem, oral cavity. We compare the proposed method with multiobjective solution algorithm from the literature and clinical plans. While satisfying the coverage of the target volumes, the proposed algorithm was able to improve the OAR sparing as much as 35%.