ESTRO 2025 - Abstract Book

S3544

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

ESTRO 2025

course healthy without major complications. Transmural lesions were confirmed by histology. Significant decreases in the membrane potentials were also found for the treated myocardium (e.g., > ~20 mV).

Conclusion: This pilot study confirmed the safe and effective delivery of ablative radiation assisted by robustly managing a moving cardiac target through gating and non-ionizing real-time image guidance by ultrasound imaging and ECG.

Keywords: cardiac ablation, cardiac gating, animal study

References: [1] van der Ree, Martijn H., et al. "Cardiac radioablation—A systematic review." Heart rhythm 17.8 (2020): 1381-1392. [2] Alpert, Nathaniel M., et al. "Quantitative in vivo mapping of myocardial mitochondrial membrane potential." PloS one 13.1 (2018): e0190968.

3780

Digital Poster Bayesian optimization of cellular parameters to improve mechanistic model predictions Jorge Jara, Sophia Galvez, Sebastian Martinez, Maria Pia Valenzuela, Andrea Russomando Physics, Pontificia Universidad Catolica de Chile, Santiago, Chile

Purpose/Objective: Mechanistic models [1-4] simulate cellular responses based on primary principles like damage repair and lethality but require calibration with DNA damage simulations and experimental data. PIDE [5], a database of 1118 in-vitro experiments, provides a solid base for this purpose, but lacks crucial information, such as cell phase, with over 90% of data reported as asynchronous. Since simulations often assume G1-phase parameters, this study investigates whether it is possible to identify a weighted value of cellular parameters to optimize theoretical model predictions. The aim is to define a procedure that can be broadly applied to increase the predictive capability of mechanistic models, thereby facilitating their translation into clinical environments. Material/Methods: Cell survival was modeled through the mechanistic approach proposed by Wang et al. [2]. The Monte Carlo Damage Simulation code (MCDS [6]) was employed to model DNA damage, accounting for variables such as particle type, energy, cell size, and DNA content. Parameters were adjusted using Bayesian inferences with the pymc software [7].