S1777
Physics - Dose prediction/calculation, optimisation and applications for particle therapy planning
ESTRO 2026
13.Pettersson E, Thilander-Klang A, Bäck A. Prediction of proton stopping power ratios using dual-energy CT basis material decomposition. Medical Physics2024; 51: 881–897. Keywords: SPR prediction, proton range, DECT, SECT, WET Mini-Oral 4336 Biological adaptive radiotherapy: how to overcome varying hypoxia in particle therapy Lars Glimelius 1 , Gustav Lidberg 1 , Elias Coniavitis 1 , Vittoria Pavanello 2,3 1 Physics, RaySearch Laboratories, Stockholm, Sweden. 2 Medical Physics, National Center for Oncological Hadrontherapy, Pavia, Italy. 3 Physics, IUSS, Pavia, Italy Purpose/Objective: In addition to geometrical uncertainties, variations in biological properties such as tumor hypoxia can have a large impact on local control. Imaging of biomarkers by PET or MRI [4] can be used for image-based dose and LETd painting in an adaptive workflow. An in-silico treatment adaptation study of the impact of tumor hypoxia in a lung phantom has been performed for helium, carbon and oxygen ion plans. Material/Methods: A biological online adaptive workflow was implemented in a research version of RayStation v2025, including support for different ion species and oxygen-effect incorporated RBE models. Automatic re- computation and optimization based on a reCT and average oxygen pressure enables selection of the scheduled or adapted plan before treatment.A three- beam plan was optimized for each ion species using the OSMK RBE model [2], assuming HSG cell type and an average tumor oxygen pressure of 10 mmHg [3]. Automated replanning was performed assuming oxygen pressures of 0.0, 5.0 and 7.5 mmHg in the GTV. All plans were analyzed based on RBE-weighted dose in the PTV and both lungs, as well as physical dose and LETd increase in the GTV. Results: Figure 1 shows RBE-weighted dose, DVH:s and clinical goals for the adapted vs. scheduled carbon plan, assuming 5.0 mmHg in the GTV. The increased radioresistance in the GTV causes a major reduction of target coverage for the scheduled plan. The adapted plan is able to overcome the increased radioresistance due to hypoxia and maintain target coverage by increasing both physical dose and LETd. The lung dose also increases in the adapted plan but remains within tolerance.All adapted plans are able to maintain PTV D90 > 3500 cGy for 5.0 and 7.5 mmHg as shown in table 1. For severe hypoxia (0.0 mmHg), only the oxygen plan can maintain target coverage, which can
images, Revolution Apex, Pettersson et al. 2024).SPR values of the inserts were extracted as the mean value in cylindrical volumes-of-interest (3 cm long, 2 cm diameter).Reference SPRs for each of the inserts were determined from measurements in a 220 MeV proton pencil beamusing a Peakfinder detector system (PTW dosimetry). CT-predicted SPRs were compared with reference SPRs.The average SPR deviation ( Δ SPR) for each of the two tissuegroups, soft and bone tissues,was summarizedseparately for the CIRS and Gammex surrogate sets. Δ SPRs for each of the tissue groups were used to calculate the impact on the clinical range ( Δ WET) of a proton beam for a
simulated beam path consisting of 80% soft tissues and 20% bones (Peters et al. 2021). Results:
The largest differences between CT-predicted and reference SPRs (SPR-SPRref)were found for the CIRS materials (Figure 1).The predicted SPRs for the bone materials were generally overestimated by the HLUT and MMSim methods and underestimated by the DirectSPR and MD-SPR methods (Table 1).In the simulated beam path, larger HLUT overestimations in boneswere compensated by underestimations in soft tissues.
Conclusion: Theestimatedimpact of predicted SPR accuracy on clinical range was influenced by the tissue surrogates used.Deviations in a simulated clinical beam path were within 1.3% for all SPR prediction methods. References: Peters N, Wohlfahrt P and Dahlgren C V et al.Experimental assessment of inter-centre variation in stopping-power andrange prediction in particle therapy. Radiotherapy and Oncology 2021; 163: 7-
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