127583 20241125101155.0 doi 10.1002/cnm.3760 sideral 134609 ART-2023-134609 eng Juma, Victor Ogesa Universidad de Zaragoza Effects of tumour heterogeneous properties on modelling the transport of radiative particles 2023 Access copy available to the general public Unrestricted Dose calculation plays a critical role in radiotherapy (RT) treatment planning, and there is a growing need to develop accurate dose deposition models that incorporate heterogeneous tumour properties. Deterministic models have demonstrated their capability in this regard, making them the focus of recent treatment planning studies as they serve as a basis for simplified models in RT treatment planning. In this study, we present a simplified deterministic model for photon transport based on the Boltzmann transport equation (BTE) as a proof‐of‐concept to illustrate the impact of heterogeneous tumour properties on RT treatment planning. We employ the finite element method (FEM) to simulate the photon flux and dose deposition in real cases of diffuse intrinsic pontine glioma (DIPG) and neuroblastoma (NB) tumours. Importantly, in light of the availability of pipelines capable of extracting tumour properties from magnetic resonance imaging (MRI) data, we highlight the significance of such data. Specifically, we utilise cellularity data extracted from DIPG and NB MRI images to demonstrate the importance of heterogeneity in dose calculation. Our model simplifies the process of simulating a RT treatment system and can serve as a useful starting point for further research. To simulate a full RT treatment system, one would need a comprehensive model that couples the transport of electrons and photons. info:eu-repo/grantAgreement/EC/H2020/826494/EU/PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers/PRIMAGE This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 826494-PRIMAGE info:eu-repo/grantAgreement/ES/MCIU/FPU18/04541 info:eu-repo/grantAgreement/ES/MICINN/PLEC2021-007709/AEI/10.13039/501100011033 info:eu-repo/semantics/openAccess by-nc-nd http://creativecommons.org/licenses/by-nc-nd/3.0/es/ 2.2 2023 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS 39 / 135 = 0.289 2023 Q2 T1 MATHEMATICAL & COMPUTATIONAL BIOLOGY 31 / 66 = 0.47 2023 Q2 T2 ENGINEERING, BIOMEDICAL 82 / 123 = 0.667 2023 Q3 T3 0.573 2023 Applied Mathematics 2023 Q2 Biomedical Engineering 2023 Q2 Software 2023 Q2 Modeling and Simulation 2023 Q2 Computational Theory and Mathematics 2023 Q2 Molecular Biology 2023 Q3 4.5 2023 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Sainz-DeMena, Diego Universidad de Zaragoza (orcid)0000-0001-7452-0437 Sánchez, María Teresa (orcid)0000-0002-3514-6443 García-Aznar, José Manuel Universidad de Zaragoza (orcid)0000-0002-9864-7683 5004 605 Universidad de Zaragoza Dpto. Ingeniería Mecánica Área Mec.Med.Cont. y Teor.Est. 39, 11 (2023), e3760 [21 pp.] International Journal for Numerical Methods in Biomedical Engineering 2040-7939 2334103 http://zaguan.unizar.es/record/127583/files/texto_completo.pdf Versión publicada 2299906 http://zaguan.unizar.es/record/127583/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:127583 articulos driver 2024-11-22-12:08:59 ARTICLE