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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Veröffentlicht in:	International journal for numerical methods in biomedical engineering Jg. 39; H. 7; S. e3734 - n/a
Hauptverfasser:	Tzirakis, Konstantinos, Papanikas, Christos Panagiotis, Sakkalis, Vangelis, Tzamali, Eleftheria, Papaharilaou, Yannis, Caiazzo, Alfonso, Stylianopoulos, Triantafyllos, Vavourakis, Vasileios
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Hoboken, USA John Wiley & Sons, Inc 01.07.2023 Wiley Subscription Services, Inc
Schlagworte:	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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Abstract	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.
AbstractList	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. 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.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. 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.
Author	Stylianopoulos, Triantafyllos Sakkalis, Vangelis Vavourakis, Vasileios Papaharilaou, Yannis Papanikas, Christos Panagiotis Tzirakis, Konstantinos Tzamali, Eleftheria Caiazzo, Alfonso
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Snippet	Glioblastoma is the most aggressive and infiltrative glioma, classified as Grade IV, with the poorest survival rate among patients. Accurate and rigorously...
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SubjectTerms	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
Title	An adaptive semi‐implicit finite element solver for brain cancer progression modeling
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