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
Online-Zugang:	Volltext
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