Data-driven spatio-temporal modelling of glioblastoma

Data-driven spatio-temporal modelling of glioblastoma

Mathematical oncology provides unique and invaluable insights into tumour growth on both the microscopic and macroscopic levels. This review presents state-of-the-art modelling techniques and focuses on their role in understanding glioblastoma, a malignant form of brain cancer. For each approach, we...

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Bibliographic Details
Main Authors: Andreas Christ Sølvsten Jørgensen, Ciaran Scott Hill, Marc Sturrock, Wenhao Tang, Saketh R. Karamched, Dunja Gorup, Mark F. Lythgoe, Simona Parrinello, Samuel Marguerat, Vahid Shahrezaei
Format: Article
Language:English
Published: The Royal Society 2023-03-01
Series:Royal Society Open Science
Subjects:
agent-based modelling
glioblastoma
reaction–diffusion equations
Bayesian inference
data-driven modelling
Online Access:https://royalsocietypublishing.org/doi/10.1098/rsos.221444

Internet

https://royalsocietypublishing.org/doi/10.1098/rsos.221444

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