Session Item

Intensity modulated proton therapy (IMPT) uses multiple beams with steep in-field dose gradients and is therefore very sensitive for density and setup errors. Mathematical robust planning (RP) methods have been developed to account for these errors. However, RP methods take multiple scenario dose distributions into account during the optimization process, which makes RP generally slow as compared to margin-based planning methods. In this study, we developed and combined a Machine Learning Optimization (MLO) planning algorithm with a robust dose mimicking optimization algorithm to automatically create robust IMPT plans. We aimed to automatically generate robust IMPT plans with at least comparable target coverage robustness as clinically available robust IMPT plans for oropharyngeal cancer patients.

In this study, robustly optimized IMPT plans of 65 HNC patients from our clinical and research archives were included. Dose distributions, contours, and CT image features of 60 patients were used to train a model to predict dose distributions for novel patients. Dose prediction was based on a random forest model with 96 trees at depth 10 including a conditional random field optimization. The target coverage during training were based on the primary and elective PTV. The predicted dose was converted to a deliverable plan using robust voxel-wise dose mimicking optimization, accounting for target robustness of 5 mm setup error and ±3% density uncertainty. The remaining five patients were used for validation of the MLO method. The beam configuration was copied from the clinical plans. Target robustness was assessed by a multi-scenario plan evaluation method comprising 16 dose recalculations with 8 positional isocenter shifts of 5 mm and a ±3% density uncertainty. The scenario dose distributions were combined into a voxel-wise minimum (vw-min) dose distribution and evaluated using the D_98%>94% criteria. In addition, we evaluated the spinal cord (D_max <5400cGy), external D_mean and several organs-at-risk (OARs) such as the parotid glands and oral cavity D_mean.

The fully automated robustly optimized MLO plans fulfilled the D98>94% criteria for primary CTV in all five patients, see figure 1. The D₉₈ of the MLO plans was on average (± SD) higher than in the clinical plans (6783 ± 58 cGy for the MLO plans and 6624±143 cGy for the clinical plans). On average, the vw-min D98 of the elective CTV of the MLO and clinical plans were 5139 ±139 cGy and 5160±75 cGy, respectively. The dose to spinal cord and external in the MLO plans were comparable to the clinical plans and fulfilled the clinical criteria. For the other OARs, the dose in the clinical plans was lower compared to the MLO plans, see figure 2.

The MLO method fully automatically creates robust IMPT plans for oropharyngeal cancer patients with similar target robustness as compared to the clinical reference plans. In a next step, an OAR dose reduction algorithm will be added to further improve the MLO plans.