  Objective: Radiation therapy treatment planning is a time-consuming process
involving iterative adjustments of hyperparameters. To automate the treatment
planning process, we propose a meta-optimization framework, called MetaPlanner
(MP). Methods: Our MP algorithm automates planning by performing optimization
of treatment planning hyperparameters. The algorithm uses a derivative-free
method (i.e. parallel Nelder-Mead simplex search) to search for weight
configurations that minimize a meta-scoring function. Meta-scoring is performed
by constructing a tier list of the relevant considerations (e.g. dose
homogeneity, conformity, spillage, and OAR sparing) to mimic the clinical
decision-making process. Additionally, we have made our source code publicly
available via github. Results: The proposed MP method is evaluated on two
datasets (21 prostate cases and 6 head and neck cases) collected as part of
clinical workflow. MP is applied to both IMRT and VMAT planning and compared to
a baseline of manual VMAT plans. MP in both IMRT and VMAT scenarios has
comparable or better performance than manual VMAT planning for all evaluated
metrics. Conclusion: Our proposed MP provides a general framework for fully
automated treatment planning that produces high quality treatment plans.
Significance: Our MP method promises to substantially reduce the workload of
treatment planners while maintaining or improving plan quality.
