ESTRO 2023 - Abstract Book

S1701

Digital Posters

ESTRO 2023

to calculate it as a matrix multiplication, the presented type of functions is easy to handle in optimization. By including knowledge of the expected LD distribution for a specific case, a customization of the kernel might be beneficial. [1] Yang Y, et al. (2022): Exploratory study of seed spots analysis to characterize dose and linear-energy-transfer effect in adverse event initialization of pencil-beam-scanning proton therapy. Medical physics 49 (9), DOI: 10.1002/mp.15859 . [2] Craft D, et al. (2014): Shared data for intensity modulated radiation therapy (IMRT) optimization research: the CORT dataset, GigaScience, 3(1), DOI: 10.1186/2047-217X-3-37 [3] Wieser HP, et al. (2017): Development of the open-source dose calculation and optimization toolkit matRad. Medical physics 44 (6), . DOI: 10.1002/mp.12251

PO-1945 Balancing Robustness and Adaptation for Proton Therapy of Lung Cancer Patients

V. Badiu 1,2 , V.T. Taasti 3 , W. van Elmpt 3 , E. Sterpin 1,4

1 KU Leuven, Department of Oncology, Laboratory of Experimental Radiotherapy, Leuven, Belgium; 2 UZ Leuven, Particle Therapy Interuniversity Center Leuven – PARTICLE, Leuven, Belgium; 3 Maastricht University Medical Centre+, Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht, The Netherlands; 4 Université catholique de Louvain, Institut de Recherche Expérimentale et Clinique, Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Brussels, Belgium Purpose or Objective In proton therapy, robust planning is essential for accurate delivery in the presence of uncertainties. An increase in plan robustness leads to larger volumes irradiated with therapeutic doses and consequently a higher dose to adjacent organs at-risk (OARs) and an increased chance of potential post-treatment toxicities. The opposite occurs with plans with a higher dose conformity to the target and subsequently sparing of healthy surrounding tissue at the expense of a higher sensitivity to anatomical changes, resulting in work and cost demanding adaptations. In this study we aim to identify the optimal workflow where the adaptation rate and the associated increase in upfront costs is balanced against dosimetric parameters or the potential post treatment detriment to the patient’s quality of life in the form of unwanted toxicities. Materials and Methods Ten lung cancer patients were planned. Each patient had a planning CT and up to eight weekly repeated CTs (reCTs). Plans were made with three different levels of robustness with a setup error of 3, 6 or 9 mm, all with a range uncertainty of 2.6%. For each robustness level the planning dose was recomputed on all reCTs to assess whether the clinical constraints (e.g., target coverage and OAR dose) were met or if adaptation was required. Following the evaluation of dose metrics, we report the adaptation rate based on D98% nominal and worst-case values for target coverage as well as Dmean and V30% for heart and lungs-GTV and D2% for spinal cord and esophagus. Results For the 3, 6 and 9 mm robustness levels, an adaptation rate of 90%, 60% and 40%, respectively, was observed. From these patients 78%, 83% and 50% needed an adaptation already within the first two weeks of treatment. This was expected, as an increase in robustness will decrease the susceptibility to anatomical changes. Average gains in OAR dose sparing were recorded when transitioning from a 9 mm setup error to 6 mm (lungs-GTV – 0.8 Gy Dmean, 1.2% V30%, heart – 0.6 Gy Dmean, 1.0% V30%, spinal cord – 2 Gy D2%, esophagus – 2.5 Gy D2%) and when transitioning from a 6 mm setup error to 3 mm (lungs GTV – 1 Gy Dmean, 1.6% V30%, heart – 0.7 Gy Dmean, 1.0% V30%, spinal cord – 1.9 Gy D2%, esophagus – 2.9 Gy D2%). The gains for Dmean for Lungs-GTV and Heart are shown in Figure 1 as well as the weekly cumulative adaptation rate shown in Figure 2.