An adaptive semi‐implicit finite element solver for brain cancer progression modeling

Glioblastoma is the most aggressive and infiltrative glioma, classified as Grade IV, with the poorest survival rate among patients. Accurate and rigorously tested mechanistic in silico modeling offers great value to understand and quantify the progression of primary brain tumors. This paper presents...

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Vydáno v:International journal for numerical methods in biomedical engineering Ročník 39; číslo 7; s. e3734 - n/a
Hlavní autoři: Tzirakis, Konstantinos, Papanikas, Christos Panagiotis, Sakkalis, Vangelis, Tzamali, Eleftheria, Papaharilaou, Yannis, Caiazzo, Alfonso, Stylianopoulos, Triantafyllos, Vavourakis, Vasileios
Médium: Journal Article
Jazyk:angličtina
Vydáno: Hoboken, USA John Wiley & Sons, Inc 01.07.2023
Wiley Subscription Services, Inc
Témata:
Adaptive algorithms
adaptive mesh
Angiogenesis
Brain
Brain - pathology
Brain cancer
Brain Neoplasms - diagnostic imaging
Brain tumors
Cancer
computational model
Computer Simulation
Finite Element Analysis
finite element method
Glioblastoma
high performance computing
Humans
Hypoxia
in silico
Invasiveness
Magnetic resonance imaging
Medical imaging
Medical prognosis
Modelling
Necrosis
Neovascularization, Pathologic
Neuroimaging
PIHNA
Quality
Sensitivity analysis
Simulation
Solvers
Survival
Tumors
finite element method
glioblastoma
adaptive mesh
computational model
high performance computing
PIHNA
in silico
ISSN:2040-7939, 2040-7947, 2040-7947
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Shrnutí:Glioblastoma is the most aggressive and infiltrative glioma, classified as Grade IV, with the poorest survival rate among patients. Accurate and rigorously tested mechanistic in silico modeling offers great value to understand and quantify the progression of primary brain tumors. This paper presents a continuum‐based finite element framework that is built on high performance computing, open‐source libraries to simulate glioblastoma progression. We adopt the established proliferation invasion hypoxia necrosis angiogenesis model in our framework to realize scalable simulations of cancer, and has demonstrated to produce accurate and efficient solutions in both two‐ and three‐dimensional brain models. The in silico solver can successfully implement arbitrary order discretization schemes and adaptive remeshing algorithms. A model sensitivity analysis is conducted to test the impact of vascular density, cancer cell invasiveness and aggressiveness, the phenotypic transition potential, including that of necrosis, and the effect of tumor‐induced angiogenesis in the evolution of glioblastoma. Additionally, individualized simulations of brain cancer progression are carried out using pertinent magnetic resonance imaging data, where the in silico model is used to investigate the complex dynamics of the disease. We conclude by arguing how the proposed framework can deliver patient‐specific simulations of cancer prognosis and how it could bridge clinical imaging with modeling. We propose a high‐fidelity finite element method to simulate glioblastoma growth. The proposed in silico brain cancer model is robust in such that is capable to perform adaptive mesh refinement/coarsening and can realize scalable computations. Quantitative analysis permitted to assess the model sensitivity and stability, as well as to interrogate the parameter space and, thus, identify important mechanisms of glioblastoma progression. Our in silico model demonstrated great predictive potential, and can prospectively be used as a personalized treatment prognostic tool.
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ISSN:2040-7939
2040-7947
2040-7947
DOI:10.1002/cnm.3734