Robustness of intensity modulated proton treatment of esophageal cancer for anatomical changes and breathing motion.

BACKGROUND AND PURPOSE: In this study, we assessed the robustness of intensity modulated proton therapy (IMPT) in esophageal cancer for anatomical variations during treatment. METHODS: The first sixty esophageal cancer patients, treated clinically with chemoradiotherapy were included. The treatment planning strategy was based on an internal target volume (ITV) approach, where the ITV was created from the clinical target volumes (CTVs) delineated on all phases of a 4DCT. For optimization, a 3 mm isotropic margin was added to the ITV, combined with robust optimization using 5 mm setup and 3 % range uncertainty. Each patient received weekly repeat CTs (reCTs). Robust plan re-evaluation on all reCTs, and a robust dose summation was performed. To assess the factors influencing ITV coverage, a multivariate linear regression analysis was performed. Additionally, clinical adaptations were evaluated. RESULTS: The target coverage was adequate (ITV V94%>98 % on the robust voxel-wise minimum dose) on most reCTs (91 %), and on the summed dose in 92 % of patients. Significant predictors for ITV coverage in the multivariate analysis were diaphragm baseline shift and water equivalent depth (WED) of the ITV in the beam direction. Underdosage of the ITV mainly occurred in week 1 and 4, leading to treatment adaptation of eight patients, all on the first reCT. CONCLUSION: Our IMPT treatment of esophageal cancer is robust for anatomical variations. Adaptation appears to be most effective in the first week of treatment. Diaphragm baseline shifts and WED are predictive factors for ITV underdosage, and should be incorporated in an adaptation protocol.

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.

Consensus guide on CT-based prediction of stopping-power ratio using a Hounsfield look-up table for proton therapy.

BACKGROUND AND PURPOSE: Studies have shown large variations in stopping-power ratio (SPR) prediction from computed tomography (CT) across European proton centres. To standardise this process, a step-by-step guide on specifying a Hounsfield look-up table (HLUT) is presented here. MATERIALS AND METHODS: The HLUT specification process is divided into six steps: Phantom setup, CT acquisition, CT number extraction, SPR determination, HLUT specification, and HLUT validation. Appropriate CT phantoms have a head- and body-sized part, with tissue-equivalent inserts in regard to X-ray and proton interactions. CT numbers are extracted from a region-of-interest covering the inner 70% of each insert in-plane and several axial CT slices in scan direction. For optimal HLUT specification, the SPR of phantom inserts is measured in a proton beam and the SPR of tabulated human tissues is computed stoichiometrically at 100 MeV. Including both phantom inserts and tabulated human tissues increases HLUT stability. Piecewise linear regressions are performed between CT numbers and SPRs for four tissue groups (lung, adipose, soft tissue, and bone) and then connected with straight lines. Finally, a thorough but simple validation is performed. RESULTS: The best practices and individual challenges are explained comprehensively for each step. A well-defined strategy for specifying the connection points between the individual line segments of the HLUT is presented. The guide was tested exemplarily on three CT scanners from different vendors, proving its feasibility. CONCLUSION: The presented step-by-step guide for CT-based HLUT specification with recommendations and examples can contribute to reduce inter-centre variations in SPR prediction.