  We consider the effects of parameter uncertainty on the optimal radiation
schedule in the context of the linear-quadratic model. Our interest arises from
the observation that if inter-patient variations in OAR and tumor sensitivities
to radiation or sparing factor of the OAR are not accounted for during
radiation scheduling, the performance of the therapy may be strongly degraded
or the OAR may receive a substantially larger dose than the maximum threshold.
This paper proposes two radiation scheduling concepts to incorporate
inter-patient variability into the scheduling optimization problem. The first
approach is a robust formulation that formulates the problem as a conservative
model that optimizes the worst case dose scheduling that may occur. The second
method is a probabilistic approach, where the model parameters are given by a
set of random variables. This formulation insures that our constraints are
satisfied with a given probability, and that our objective function achieves a
desired level with a stated probability. We used a transformation to reduce the
resulting optimization problem to two dimensions. We showed that the optimal
solution lies on the boundary of the feasible region and we used a branch and
bound algorithm to find the global optimal solution. We observed that if the
number of fractions in the optimal conventional schedule is the same as the
robust and stochastic solutions, it is preferable to administer equal or
smaller total dose. In addition if there exist more (fewer) treatment sessions
in the probabilistic or robust solution compared to the conventional schedule,
a reduction in total dose squared (total dose) will be expected. Finally, we
performed numerical experiments in the setting of head-and-neck tumors to
reveal the effect of parameter uncertainty on optimal schedules and to evaluate
the sensitivity of the model to the choice of key model parameters.
