ESTRO 2025 - Abstract Book

S3517

Physics - Optimisation, algorithms and applications for ion beam treatment planning

ESTRO 2025

Conclusion: This novel scheduling optimization approach demonstrates significant potential for improving the efficiency at CIRT facilities. The tool’s rapid execution time and robust performance across various operational conditions make it particularly suitable for real-time schedule adjustments. The ability to maintain efficiency during both routine operations and challenging scenarios suggests broad applicability across different facility configurations. Future research will focus on validating with empirical data from operational CIRT centers and integrating additional operational factors such as staff scheduling and maintenance planning. This approach represents a significant step toward maximizing the therapeutic potential of limited CIRT resources while enhancing patient experience. References: 1. Kanai T, Endo M, Minohara S, et al. (1999) Biophysical characteristics of HIMAC clinical irradiation system for heavy-ion radiation therapy. Int J Radiat Oncol Biol Phys. 44(1):201–210. 2. Tsujii H, Kamada T. (2012) A review of update clinical results of carbon ion radiotherapy. Jpn J Clin Oncol. 42(8):670–685. 3. Durante M, Loeffler JS. (2010) Charged particle beams to combat cancer. Nat Rev Clin Oncol. 7(1):37–43. 4. Suzuki, K., Palmer, M.B., Sahoo, N., et al. (2016), Quantitative analysis of treatment process time and throughput capacity for spot scanning proton therapy. Med. Phys. , 43, 3975-3986. Keywords: Scheduling, wait times, carbon ion radiotherapy

3015

Proffered Paper Towards real-time adaptive proton therapy with a beamlet-free optimization Valentine Dormal 1 , Danah Pross 1 , Ana M. Barragan Montero 1 , John A. Lee 1 , Edmond Sterpin 1,2

1 Molecular Imaging, Radiotherapy and Oncology (MIRO), IREC, Université catholique de Louvain, Louvain-la-Neuve, Belgium. 2 Department of Oncology, Laboratory of Experimental Radiotherapy, KU Leuven, Leuven, Belgium

Purpose/Objective: Real-time adaptation of proton therapy plans during treatment delivery still faces multiple challenges, one of them being the integration of accurate dose engines (i.e. Monte Carlo) in a sufficiently fast optimization workflow. This work focuses on the intra-fraction optimization process needed to rapidly update the plan to the evolving anatomy, assuming that segmented images are available in real time. Typical optimization methods are static approaches that require time-consuming pre-computation of a dose influence matrix for each spot (i.e., beamlets), making real-time