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We present an optimization-based approach to radiation treatment planningover time. Our approach formulates treatment planning as an optimal controlproblem with nonlinear patient health dynamics derived from the standardlinear-quadratic cell survival model. As the formulation is nonconvex, wepropose a method for obtaining an approximate solution by solving a sequence ofconvex optimization problems. This method is fast, efficient, and robust tomodel error, adapting readily to changes in the patient's health betweentreatment sessions. Moreover, we show that it can be combined with the operatorsplitting method ADMM to produce an algorithm that is highly scalable and canhandle large clinical cases. We introduce an open-source Python implementationof our algorithm, AdaRad, and demonstrate its performance on several examples.

Operator Splitting for Adaptive Radiation Therapy with Nonlinear Health Dynamics