Python scripting for robustness evaluation in hybrid robustly optimised intensity modulated proton therapy treatment plan

The major challenge of intensity modulated proton therapy (IMPT) is its higher susceptibility to uncertainties. The two most important sources of errors in IMPT are the beam range and patient setup uncertainties. Recently, a new approach called robust optimization was introduced to consider the uncertainties during the optimization process itself. However, this approach leads to increase in computation time even with GPU based latest hardware configuration. The increase in planning time is significantly large in a complex clinical case like cranio spinal irradiation (CSI) where multiple isocenter, large field length and field abutment are used. Moreover, the present version of treatment planning system (TPS) can evaluate only one perturbed scenario at a time. The aim of this study is to a create hybrid, robustly optimized IMPT plan using different beam geometries and to evaluate the robustness of the competing plans using in-house developed python scripting