Multi-objective optimization in radiotherapy: applications to stereotactic radiosurgery and prostate brachytherapy

Abstract

Treatment planning for radiation therapy is a multi-objective optimization process. Here we present a machine intelligent scheme for treatment planning based on multi-objective decision analysis (MODA) and genetic algorithm (GA) optimization. Multi-objective ranking strategies are represented in the L(p) metric under the displaced ideal model. Goal setting, protocol satisficing and fuzzy ranking of objective importance can be incorporated into the decision scheme to assimilate clinical decision making. For distance measures in the L(p) metric, a dynamic gauge function is defined based on the state energy of the decision system, which is assumed to undergo thermodynamic cooling with iteration time. The MODA scheme interacts with a robust GA engine, which adaptively evolves in the multi-modal landscape that defines the treatment plan quality. A conventionally challenging case of stereotactic radiosurgery of a brain lesion was selected for GA optimization. The resulting dose distributions are compared to human-developed plans, which are commonly regarded as clinically relevant and empirically optimal. The GA-optimized plans achieve substantially better sparing of critical normal neuroanatomy surrounding the brain lesion while respecting the preset constraints on tumor dose uniformity. In addition, machine optimization tends to produce novel treatment strategies which complements expert knowledge. The run time for producing an optimal plan is considerably shorter than the typical planning time for human experts, thus GA can also be used to aid the human treatment planning process. In prostate brachytherapy, MODA-GA was specifically applied to non-ideal conditions in which typical surgical uncertainties in seed implant positioning occur, where noisy objectives were introduced into the optimization scheme. The noisy system is found to be manageable by MODA-GA at uncertainty levels corresponding to reasonably proficient surgery teams. In contrast, noisy objectives would be very difficult to explore by human expert planners. Potential use of noisy optimization with time series analysis is being explored for error-corrective computer guidance in the operating room for prostate seed implantation. In conclusion, the combination of MODA and GA optimization offers both a solution to practical treatment planning tasks and the potential for real time applications in radiotherapy.