Abstract
Radiotherapy is one of the most widely used treatments for cancer by irradiating the tumor volume. However, one of its disadvantages is that healthy tissue is also affected, producing various side effects. For this reason, preliminary studies are required beforehand to determine the dose to be administered in each case, to avoid possible damage and to make sure that the dose received by the tumor is the correct one. These studies are carried out both using simulations and with routine machinery procedures using a mannequin that simulates the area to be treated. In this work a way of speeding up the previous study process is tackled, starting from simulated data whose optimized obtaining will be the objective of this work. The PENELOPE Monte Carlo simulation software is used to recreate the process and obtain the necessary previous data. Subsequently, regression models are applied to obtain the values of interest and accelerate the procedure, reducing, in addition, the energy consumption and storage required while obtaining very accurate approximations.
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Acknowledgements
This work was funded by the Spanish Ministry of Sciences, Innovation and Universities under Project PID2021-128317OB-I00 and the projects from Junta de Andalucia P20-00163.
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Fernández, I., Ovejero, C., Herrera, L.J., Rojas, I., Carrillo-Perez, F., Guillén, A. (2023). Speeding up Simulations for Radiotherapy Research by Means of Machine Learning. In: Rojas, I., Valenzuela, O., Rojas Ruiz, F., Herrera, L.J., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2023. Lecture Notes in Computer Science(), vol 13919. Springer, Cham. https://doi.org/10.1007/978-3-031-34953-9_12
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DOI: https://doi.org/10.1007/978-3-031-34953-9_12
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