  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.
