  A framework for online robust adaptive radiation therapy (ART) is presented.
This framework is designed to (i) handle interfractional geometric variations
following a probability distribution different from the a priori hypothesis,
(ii) address adaptation cost and (iii) address computational tractability. The
novelty of this framework is the use of Bayesian inference and
scenario-reduction, which is evaluated in a series of treatment on a
one-dimensional phantom geometry. The initial robust plan is generated from a
robust optimization problem based on either expected-value- or
worst-case-optimization approach using the a priori hypothesis of the
probability distribution governing the interfractional geometric variations.
The actual interfractional variations are evaluated in terms of their
likelihood with respect to the a priori hypothesis and violation of
user-specified tolerance limits. During an adaptation the a posteriori
distribution is computed from the actual variations using Bayesian inference.
The adapted plan is optimized to better suit the actual interfractional
variations of the individual case, which is used until the next adaptation is.
To address adaptation cost, the proposed framework provides an option for
increased adaptation frequency. Computational tractability is addressed by
scenario-reduction algorithms to reduce the size of the optimization problem.
According to the simulations, the proposed framework may improve target
coverage compared to the corresponding non-adaptive robust approach. Combining
the worst-case-optimization approach with Bayesian inference may perform best
in terms of improving CTV coverage and organ-at-risk~(OAR) protection. Bayesian
inference may have a greater impact on handling adaptation cost than increased
adaptation frequency. The concept of scenario-reduction may be useful to
address computational tractability in ART and robust planning.
