Balancing robustness and adaptation rate for proton therapy of lung cancer patients.

INTRODUCTION: An increase in plan robustness leads to a higher dose to organs-at-risk (OARs), and an increased chance of post-treatment toxicities. In contrast, more conformal plans lead to sparing of healthy surrounding tissue at the expense of a higher sensitivity to anatomical changes, requiring costly adaptations. In this study, we assess the trade-off and impact of treatment plan robustness on the adaptation rate. METHOD: Treatment planning was performed for 40 lung cancer patients, each having a planning 4DCT and up to eight weekly repeated 4DCTs (reCTs). For each patient, plans were made with three different levels of robustness based on setup uncertainty of 3, 6 and 9 mm. These plans were robustly re-evaluated on all reCTs to assess whether the clinical constraints were met. RESULTS: For the 3, 6 and 9 mm robustness levels, adaptation rates of 87.5 %, 70.0 % and 57.5 %, respectively, were observed. A mean absolute normal tissue complication probability (NTCP) gain of 2.9 percentage points (pp) was calculated for pneumonitis grade ≥ 2 when transitioning from 9 mm plans to 3 mm plans, 7.6 pp for esophagitis grade ≥ 2, and 2.5 pp for mortality risk 2 years post-treatment. CONCLUSION: The lowered risk of post treatment toxicities at lower robustness levels is clinically relevant but comes at the expense of more treatment adaptations, particularly in cases where meeting our clinical goals is not compromised by having a dose that is more conformal to the target. The trade-off between workload and reduced NTCP needs to be individually assessed.

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

INTRODUCTION: 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 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. METHOD: 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. RESULTS: Plans 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). CONCLUSION: 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.