Björn Hårdemark | Searcharxiv

arXiv2017

Explicit optimization of plan quality measures in intensity-modulated radiation therapy treatment planning

Conventional planning objectives in optimization of intensity-modulated radiotherapy treatment (IMRT) plans are designed to minimize the violation of dose-volume histogram (DVH) thresholds using penalty functions. Although successful in guiding the DVH curve towards these thresholds, conventional planning objectives offer limited control of the individual points on the DVH curve (doses-at-volume) used to evaluate plan quality. In this study, we abandon the usual penalty-function framework and propose planning objectives that more explicitly relate to DVH statistics. The proposed planning objectives are based on mean-tail-dose, resulting in convex optimization. We also demonstrate how to adapt a standard optimization method to the proposed formulation in order to obtain a substantial reduction in computational cost. We investigate the potential of the proposed planning objectives as tools for optimizing DVH statistics through juxtaposition with the conventional planning objectives on two patient cases. Sets of treatment plans with differently balanced planning objectives are generated using either the proposed or the conventional approach. Dominance in the sense of better distributed doses-at-volume is observed in plans optimized within the proposed framework, indicating that the DVH statistics are better optimized and more efficiently balanced using the proposed planning objectives.

physics.med-ph↗

arXiv2016

Towards Robust Adaptive Radiation Therapy Strategies

The authors propose robust adaptive strategies based on stochastic minimax optimization for a series of simulated treatments on a one-dimensional patient phantom. The plan applied during the first fractions should be able to handle anticipated systematic and random errors. At scheduled fractions, the impact of the measured errors on the delivered dose distribution is evaluated. For a patient receiving a dose that does not satisfy clinical goals, the plan is reoptimized based on these individually measured errors. The adapted plan is then applied during subsequent fractions until a new scheduled adaptation becomes necessary. In the first adaptive strategy, the measured systematic and random error scenarios and their assigned probabilities are updated to guide the robust reoptimization. In the second strategy, the grade of conservativeness is adapted in response to the measured dose delivery errors. In the third strategy, the uncertainty margins around the target are recalculated based on the measured errors. The simulated systematic and random errors are either similar to the anticipated errors or unpredictably larger, in order to critically evaluate the performance of the adaptive strategies. The simulations show that robustly optimized treatment plans provide sufficient treatment quality for those treatment error scenarios similar to the anticipated error scenarios. Combining robust planning with adaptation leads to improved OAR protection. In case of unpredictably larger treatment errors, the first strategy performs best at improving treatment quality in terms of target coverage and OAR protection. According to the simulations, these robust adaptive treatment strategies are able to identify candidates for an adaptive treatment, thus giving the opportunity to provide individualized plans, and improve their treatment quality through adaptation.

math.OC↗