Development of robustness evaluation strategies for enabling statistically consistent reporting.

Robustness evaluation of proton therapy treatment plans is essential for ensuring safe treatment delivery. However, available evaluation procedures feature a limited exploration of the actual robustness of the plan and generally do not provide confidence levels. This study compared established and more sophisticated robustness evaluation procedures, with quantified confidence levels. We have evaluated several robustness evaluation methods for 5 bilateral head-and-neck patients optimized considering spot scanning delivery and with a conventional CTV-to-PTV margin of 4 mm. Method (1) good practice scenario selection (GPSS) (e.g. +/- 4 mm setup error 3% range uncertainty); (2) statistically sound scenario selection (SSSS) either only on or both on and inside isoprobability hypersurface encompassing 90% of the possible errors; (3) statistically sound dosimetric selection (SSDS). In the last method, the 90% best plans were selected according to either target coverage quantified by D 95 (SSDS_D 95) or to an approximation of the final objective function (OF) used during treatment optimization (SSDS_OF). For all methods, we have considered systematic setup and systematic range errors. A mix of systematic and random setup errors were also simulated for SSDS, but keeping the same conventional margin of 4 mm. All robustness evaluations have been performed using the fast Monte Carlo dose engine MCsquare. Both SSSS strategies yielded on average very similar results. SSSS and GPSS yield comparable values for target coverage (within 0.5 Gy). The most noticeable differences were found for the CTV between GPSS, on the one hand, and SSDS_D 95 and SSDS_OF, on the other hand (average worst-case D 98 were 2.8 and 2.0 Gy larger than for GPSS, respectively). Simulating explicitly random errors in SSDS improved almost all DVH metrics. We have observed that the width of DVH-bands and the confidence levels depend on the method chosen to sample the scenarios. Statistically sound estimation of the robustness of the plan in the dosimetric space may provide an improved insight on the actual robustness of the plan for a given confidence level.

Dosimetric evaluation of synthetic CT generated with GANs for MRI-only proton therapy treatment planning of brain tumors.

PURPOSE: The purpose of this study was to address the dosimetric accuracy of synthetic computed tomography (sCT) images of patients with brain tumor generated using a modified generative adversarial network (GAN) method, for their use in magnetic resonance imaging (MRI)-only treatment planning for proton therapy. METHODS: Dose volume histogram (DVH) analysis was performed on CT and sCT images of patients with brain tumor for plans generated for intensity-modulated proton therapy (IMPT). All plans were robustly optimized using a commercially available treatment planning system (RayStation, from RaySearch Laboratories) and standard robust parameters reported in the literature. The IMPT plan was then used to compute the dose on CT and sCT images for dosimetric comparison, using RayStation analytical (pencil beam) dose algorithm. We used a second, independent Monte Carlo dose calculation engine to recompute the dose on both CT and sCT images to ensure a proper analysis of the dosimetric accuracy of the sCT images. RESULTS: The results extracted from RayStation showed excellent agreement for most DVH metrics computed on the CT and sCT for the nominal case, with a mean absolute difference below 0.5% (0.3 Gy) of the prescription dose for the clinical target volume (CTV) and below 2% (1.2 Gy) for the organs at risk (OARs) considered. This demonstrates a high dosimetric accuracy for the generated sCT images, especially in the target volume. The metrics obtained from the Monte Carlo doses mostly agreed with the values extracted from RayStation for the nominal and worst-case scenarios (mean difference below 3%). CONCLUSIONS: This work demonstrated the feasibility of using sCT generated with a GAN-based deep learning method for MRI-only treatment planning of patients with brain tumor in intensity-modulated proton therapy.