Session Item

Robust planning is essential in proton therapy for ensuring adequate treatment delivery in the presence of uncertainties. For both robust optimization and evaluation, commonly-used techniques can be overly conservative by generating error scenarios from combinations of only maximum error values of each uncertainty source and they lack in providing quantified confidence levels. In this study, we explore whether a clinical benefit can be expected using scenario selection tools with improved statistical foundations, both at the level of robust optimization and evaluation.

Thirteen lung cancer patients were planned. Two robust optimization methods were used: scenario selection from marginal probabilities (SSMP) based on using maximum setup and range error values and scenario selection from joint probabilities (SSJP) that selects errors on a predefined 90% hypersurface. Two robust evaluation methods were used: conventional evaluation (CE) based on generating error scenarios from combinations of maximum errors of each uncertainty source and statistical evaluation (SE) via the Monte Carlo dose engine MCsquare which considers scenario probabilities. During evaluation we report for the target coverage the D₉₈ (Gy) nominal and worst-case values as well as D_mean (Gy) and V₃₀ (%) for heart and lungs-GTV and D₂ (Gy) for spinal cord and esophagus.

Plans optimized using SSJP had, on average, 0.5 Gy lower dose in CTV D_{98(worst-case)} than SSMP-optimized plans. This was expected as the SSJP tool aims at securing robustness at a predefined 90% confidence level with the aim of achieving a level of target robustness situated at the limit of clinical acceptability (i.e., adequate coverage for at least 90% of patients). When evaluated using CE only 76.9% of SSMP patients and 46.2% of SSJP patients passed our clinical threshold. Evaluating with SE, 92.3% of patients passed our clinical threshold in both optimization methods highlighting the impact of evaluating in a statistically consistent manner. Average gains in OAR sparing were recorded when transitioning from SSMP to SSJP in all metrics: esophagus (0.6 Gy D_2(nominal), 0.9 Gy D_{2(worst-case)}), spinal cord (3.9 Gy D_2(nominal), 4.1 Gy D_{2(worst-case)}) heart (1.1 Gy  D_mean, 1.9% V₃₀), lungs-GTV (1.0 Gy D_mean , 1.9% V₃₀). The reduction of the target margin to the bare minimum is the main drive that enables substantial and consistent OAR sparing.

Establishing a proper robust optimization and evaluation workflow is essential to realize the potential of proton therapy. Optimization using SSJP yielded significant OAR sparing in all recorded metrics with a target robustness within our clinical objectives, provided that a more statistically sound robustness evaluation method was used. This highlights the importance of using both advanced optimization and evaluation tools when we aim at ensuring a quantified level of robustness.