PURPOSE: To set up a framework combining robust treatment planning with adaptive re-optimization in order to maintain high treatment quality, to respond to interfractional geometric variations and to identify those patients who will benefit the most from an adaptive fractionation schedule. METHODS: 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. Information on the individual geometric variations is gathered at each fraction. At scheduled fractions, the impact of the measured errors on the delivered dose distribution is evaluated. For a patient having received a dose that does not satisfy specified plan quality criteria, the plan is re-optimized based on these individually measured errors. The re-optimized plan is then applied during subsequent fractions until a new scheduled adaptation becomes necessary. In this study, three different adaptive strategies are introduced and investigated. (a) In the first adaptive strategy, the measured systematic and random error scenarios and their assigned probabilities are updated to guide the robust re-optimization. (b) In the second strategy, the degree of conservativeness is adapted in response to the measured dose delivery errors. (c) In the third strategy, the uncertainty margins around the target are recalculated based on the measured errors. The simulated treatments are subjected to systematic and random errors that are either similar to the anticipated errors or unpredictably larger in order to critically evaluate the performance of these three adaptive strategies. RESULTS: According to the simulations, robustly optimized treatment plans provide sufficient treatment quality for those treatment error scenarios similar to the anticipated error scenarios. Moreover, combining robust planning with adaptation leads to improved organ-at-risk protection. In case of unpredictably larger treatment errors, the first strategy in combination with at most weekly adaptation performs best at notably improving treatment quality in terms of target coverage and organ-at-risk protection in comparison with a non-adaptive approach and the other adaptive strategies. CONCLUSION: The authors present a framework that provides robust plan re-optimization or margin adaptation of a treatment plan in response to interfractional geometric errors throughout the fractionated treatment. 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. The simulated robust adaptive framework is a guide for further development of optimally controlled robust adaptive therapy models.
PURPOSE: To set up a framework combining robust treatment planning with adaptive re-optimization in order to maintain high treatment quality, to respond to interfractional geometric variations and to identify those patients who will benefit the most from an adaptive fractionation schedule. METHODS: 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. Information on the individual geometric variations is gathered at each fraction. At scheduled fractions, the impact of the measured errors on the delivered dose distribution is evaluated. For a patient having received a dose that does not satisfy specified plan quality criteria, the plan is re-optimized based on these individually measured errors. The re-optimized plan is then applied during subsequent fractions until a new scheduled adaptation becomes necessary. In this study, three different adaptive strategies are introduced and investigated. (a) In the first adaptive strategy, the measured systematic and random error scenarios and their assigned probabilities are updated to guide the robust re-optimization. (b) In the second strategy, the degree of conservativeness is adapted in response to the measured dose delivery errors. (c) In the third strategy, the uncertainty margins around the target are recalculated based on the measured errors. The simulated treatments are subjected to systematic and random errors that are either similar to the anticipated errors or unpredictably larger in order to critically evaluate the performance of these three adaptive strategies. RESULTS: According to the simulations, robustly optimized treatment plans provide sufficient treatment quality for those treatment error scenarios similar to the anticipated error scenarios. Moreover, combining robust planning with adaptation leads to improved organ-at-risk protection. In case of unpredictably larger treatment errors, the first strategy in combination with at most weekly adaptation performs best at notably improving treatment quality in terms of target coverage and organ-at-risk protection in comparison with a non-adaptive approach and the other adaptive strategies. CONCLUSION: The authors present a framework that provides robust plan re-optimization or margin adaptation of a treatment plan in response to interfractional geometric errors throughout the fractionated treatment. 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. The simulated robust adaptive framework is a guide for further development of optimally controlled robust adaptive therapy models.
Authors: Yubing Tong; Jayaram K Udupa; Xingyu Wu; Dewey Odhner; Gargi Pednekar; Charles B Simone; David McLaughlin; Chavanon Apinorasethkul; Geraldine Shammo; Paul James; Joseph Camaratta; Drew A Torigian Journal: Proc SPIE Int Soc Opt Eng Date: 2018-03-12