S2013
Physics - Dose prediction/calculation, optimisation and applications for photon and electron planning
ESTRO 2026
València, València, Spain. 2 Instituto de Seguridad Industrial Radiofísica y Medioambiental (ISIRYM), Universitat Politècnica de València, València, Spain Purpose/Objective: Monte Carlo (MC) methods are extensively used for addressing complex radiation transport problems in a diverse array of scientific applications, including radiation therapy, medical imaging, electron microscopy, among others. However, their significant complexity creates high usability barriers, even for experts. Setting up simulations involves intricate, error-prone tasks like 3D geometry definition and configuration. Furthermore, integration with modern Python-centric data analysis and AI workflows remains challenging. While specialized tools like TOPAS and GATE exist for specific applications, the penRed framework takes a different approach as a general- purpose code. Derived from PENELOPE through a complete C++ rewrite, it features a modern, parallel, and modular architecture.This work introduces a comprehensive solution to enhance the accessibility, usability, and integration of penRed. Material/Methods: First, we present pyPenred (https://pypi.org/project/pyPenred/), a high- performance Python module that exposes the complete capabilities of penRed within the Python ecosystem. Built with pybind11, it allows computationally intensive particle transport to be handled by optimized C++ binaries while enabling seamless control and analysis in Python.Second, to simplify geometry creation and simulation setup, we developed a dedicated Blender(https://www.blender.org/) plug-in. This integrated graphical environment leverages Blender's powerful 3D modeling tools to support constructing models with both quadric surfaces and triangular meshes (Figure 1). It provides an intuitive interface for visually defining materials, radiation sources, and tallies (Figure 2) within a single environment, thereby minimizing errors and streamlining the workflow.
Conclusion: The developed automated script-based workflow of 40Gy/20F VMAT for cervical cancer achieved reproducible and clinically acceptable plan quality. The minimized planning time and inter-planner variability was demonstrated with potential for further muti- institutional validation. Keywords: script-based planning, automation, cervix Proffered Paper 4834 An Integrated Simulation Platform: pyPenredPython Module and Blender Plugin for thepenRed Monte Carlo Code Vicent Giménez Alventosa 1 , Sandra Oliver 2 1 Dpto. de Estad´ıstica e Investigaci´on Operativa Aplicadas y Calidad, Universitat Politècnica de
Figure 1: ICRP phantom loaded within the Blender environment
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