S1885
Physics - Dose prediction/calculation, optimisation and applications for photon and electron planning
ESTRO 2026
References: 1. Iori, Federico, et al. “Lattice Radiation Therapy in Clinical Practice: A Systematic Review.” Clinical and Translational Radiation Oncology, U.S. National Library of Medicine, 20 Dec. 2022,pmc.ncbi.nlm.nih.gov/articles/PMC9800252.2. Duriseti, Sai, et al. “Spatially Fractionated Stereotactic Body Radiation Therapy (Lattice) for Large Tumors.” Advances in Radiation Oncology, U.S. National Library of Medicine, 8 Jan. 2021,pmc.ncbi.nlm.nih.gov/articles/PMC8233471. Keywords: SFRT, Lattice, Radiotherapy Clinical Implementation of Virtual CT for Dose Evaluation Using RayStation: Comparison with In- House Method Esteban Sepulveda, Michel D'amours, Marie-Lynn Racine Radiation oncology, Charles LeMoyne Hospital, Montreal, Canada Purpose/Objective: Our in-house method for dose evaluation of an anatomy change during treatment delivery often relies on manual contour modification, disregarding internal motion, organ deformation, and volume loss due to Digital Poster 2445 weight changes. This can lead to potential dose inaccuracies and reduced workflow efficiency. RayStation offers a commercial solution, based on a CBCT “Virtual CT”, to calculate and track dose based on the daily anatomy. This study introduces an automated, script-based workflow using Virtual CT images generated from daily CBCTs in RayStation for dose evaluation. The clinical validation of Virtual CT and the implementation of automation scripts are presented and discussed. Material/Methods: An automated script was developed using the RayStation 11B Python API. For 10 patients, a Virtual CT was generated from the CBCT representing a typical anatomical change for our clinic. Dose was recalculated on the Virtual CT and compared with the conventional manual evaluation method. To assess deformable registration limitations, a second cohort of 20 breast cancer patients with partial rotation CBCTs was analyzed. Dose distributions were compared to the planning CT using PTV metrics (mean, max, D90%, D95%, and D98%). Total workflow time was recorded for both manual and automated methods. Results: Manual dose evaluation time was reduced from >60 minutes to ~2 minutes using the automated approach. Mean and maximum dose differences were 0.14% and 1.09%, respectively, with PTV coverage differences
optimising dose fall-off within the TPS. The method was evaluated on 11 clinical GTVs from multiple anatomical sites (abdomen, leg, shoulder, etc.). The prescription used for all cases followed the clinical Lattice standard of 10–15 Gy prescribed to the 80% isodose line, corresponding to maximum doses of approximately 12.5–18.75 Gy at the centres of the high-dose spheres. A clinical radiation oncologist reviewed all generated geometries. Results:
As a preliminary proof of concept, we applied the algorithm to a range of tumour shapes and sizes (Figure 1). On average, it placed 37% more spheres than manual positioning in about 10% of the time (3-5 vs 30-50 min) (Table 1). Extended optimisation spheres improved conformity during planning and supported peak-to-valley dose ratio (PVDR) control. Among the cohort of GTVs, the automatically generated lattices were considered clinically feasible by the expert oncologist and produced consistent geometric outcomes independent of tumour location or imaging parameters. The plans were delivered with PVDRs ranging from 2.5-3.5, meaning valleys received roughly 30-40% of the peak dose. Conclusion: This automated method offers a fast, reproducible, and clinically compatible solution for LRT sphere placement. Its TPS-independent design supports broad applicability, and early clinical use in patients demonstrates translational potential. The algorithm can be extended to consider additional targets such as the ones coming from PET imaging. The cohort is expanding, and further dosimetric analysis is in progress.
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