Effects of tumour heterogeneous properties on modelling the transport of radiative particles

Juma, Victor Ogesa (Universidad de Zaragoza) ; Sainz-DeMena, Diego (Universidad de Zaragoza) ; Sánchez, María Teresa ; García-Aznar, José Manuel (Universidad de Zaragoza)

Resumen: 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.

Idioma: Inglés

DOI: 10.1002/cnm.3760

Año: 2023

Publicado en: International Journal for Numerical Methods in Biomedical Engineering 39, 11 (2023), e3760 [21 pp.]
ISSN: 2040-7939

Factor impacto JCR: 2.2 (2023)

Categ. JCR: MATHEMATICS, INTERDISCIPLINARY APPLICATIONS rank: 39 / 135 = 0.289 (2023) - Q2 - T1
Categ. JCR: MATHEMATICAL & COMPUTATIONAL BIOLOGY rank: 31 / 66 = 0.47 (2023) - Q2 - T2
Categ. JCR: ENGINEERING, BIOMEDICAL rank: 82 / 123 = 0.667 (2023) - Q3 - T3

Factor impacto CITESCORE: 4.5 - Applied Mathematics (Q1) - Software (Q2) - Computational Theory and Mathematics (Q2) - Modeling and Simulation (Q2) - Biomedical Engineering (Q2) - Molecular Biology (Q3)

Factor impacto SCIMAGO: 0.573 - Applied Mathematics (Q2) - Biomedical Engineering (Q2) - Software (Q2) - Modeling and Simulation (Q2) - Computational Theory and Mathematics (Q2) - Molecular Biology (Q3)

Financiación: 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
Financiación: info:eu-repo/grantAgreement/ES/MCIU/FPU18/04541
Financiación: info:eu-repo/grantAgreement/ES/MICINN/PLEC2021-007709/AEI/10.13039/501100011033

Tipo y forma: Article (Published version)

Área (Departamento): Área Mec.Med.Cont. y Teor.Est. (Dpto. Ingeniería Mecánica)