S1933
Physics - Dose prediction/calculation, optimisation and applications for photon and electron planning
ESTRO 2026
(aSG) tests1,2 were performed to evaluate rounded leaf-end effects and tongue-and-groove (TG) behaviour, respectively. In aSG tests, a 20 mm sweeping gap was used while adjacent leaves were offset by s mm to expose the TG. Measurements and calculations were performed under isocentric conditions using a Farmer-type ionization chamber, or a large cylindrical structure in the TPS. SG data were used to derive DLGtotal and relate the DLG parameter in the ELM (DLGELM) to the legacy model parameter (DLGlegacy). Average doses to PTVs for ninety clinical VMAT/IMRT plans (6 MV and 6 FFF on TrueBeam with Millennium120) from lung, head and neck, breast, and prostate (with and without pelvis) were recalculated with fixed MUs using: (1) the legacy leaf model, (2) the ELM with auto-configured parameters, and (3) the ELM with manually adjusted DLGELM to match DLGlegacy, overriding auto-configured values. Results: The ELM introduces an intrinsic DLG (DLGintrinsic) via explicit ray-tracing of the rounded leaf tip, which adds to DLGELM to form a DLGtotal equivalent to the legacy value. For 6MV, DLGintrinsic=1.89mm (T=1.42%), with the auto-configuration tool producing DLGELM= − 0.68mm. For 6FFF, DLGintrinsic=1.81mm (T=1.21%), with an auto-configured DLGELM= − 0.75mm. The corresponding tuned DLGELM values were − 0.40mm for both energies, representing the adjustments required for the ELM to reproduce the DLGlegacy. The TG model remained unchanged and continued to overestimate dose shadowing near the leaf tip (Figure1), leading to systematic underdosage when using auto-configured parameters. Across 90 plans (Figure2), the PTV mean dose difference between the legacy model and the auto- configured ELM was − 0.9%±0.3%, while the tuned ELM differed by +0.1%±0.4%.
prescribed dose across cohorts, α / β and fractionations. Slightly larger differences were observed at lower α / β . The differences, however, were not clinically relevant with medians and interquartile differences <1GyEQD2, Figure 2a-f.Large voxel-wise differences were observed for the HN cohort, mostly located near the patient surface, with median values of -42 GyEQD2 and 57 GyEQD2 for extreme negative and positive differences, respectively, Figure 2g. These were substantially lower for the lung cohort, -1 GyEQD2, 6 GyEQD2, Figure 2h. No trends were observed for different α / β . Conclusion: Differences between convert-then-map and map-then- convert were generally small and mainly occurring at ~80% prescription dose, consistent with regions of high dose gradients. Depending on the use case, either sequence order is applicable. Larger voxel-wise discrepancies occurred particularly in the HN cohort, likely due to targets being close to the skin, though effects were also observed in lung cases. Further analysis accounting for distance to the patient surface is warranted. References: Thiong'o CM, et al. Dose Mapping using Image Registration for Reirradiation: A Systematic Review. Int J Radiat Oncol Biol Phys. Published online October 11, 2025. doi:10.1016/j.ijrobp.2025.09.053 Keywords: Reirradiation, Dose Mapping, Equieffective Dose Digital Poster 3352 On the relationship between the enhanced and the legacy leaf models in the Eclipse treatment planning system Jordi Saez, Sergi Serrano, Carla Cases, Cristian Candela-Juan, Artur Latorre-Musoll, Paula Navarro, Antonio Herreros Radiation Oncology Department, Hospital Clinic de Barcelona, Barcelona, Spain Purpose/Objective: Varian has introduced an Enhanced Leaf Model (ELM) in Eclipse(v18) which includes an automated configuration tool to derive multileaf collimator (MLC) transmission and DLG parameters directly from measurements. Although parameter names are shared with the legacy model, their physical meaning differs. This study aims to characterize the quantitative relationship between both models, clarify the interpretation of the DLG in the ELM, and assess the dosimetric impact of using auto-configured versus manually tuned parameters in clinical plans. Material/Methods: Sweeping gap (SG) and asynchronous sweeping gap
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