Improved healthy tissue sparing in proton therapy of lung tumors using statistically sound robust optimization and evaluation

Abstract

IntroductionRobust 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 in selecting error scenarios and lack in providing quantified confidence levels. In this study, established techniques are compared to comprehensive alternatives to assess the differences in target coverage and organ at risk (OAR) dose. MethodThirteen 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. ResultsPlans optimized using SSJP had, on average, 0.5 Gy lower dose in CTV D98(worst-case) than SSMP-optimized plans. When evaluated using SE, 92.3% of patients passed our clinical threshold in both optimization methods. Average gains in OAR sparing were recorded when transitioning from SSMP to SSJP: esophagus (0.6 Gy D2(nominal), 0.9 Gy D2(worst-case)), spinal cord (3.9 Gy D2(nominal), 4.1 Gy D2(worst-case)) heart (1.1 Gy Dmean, 1.9% V30), lungs-GTV (1.0 Gy Dmean , 1.9% V30). ConclusionOptimization 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.