Royal College of Surgeons in Ireland
Data-driven spatio-temporal modelling of glioblastoma.pdf (984.52 kB)

Data-driven spatio-temporal modelling of glioblastoma

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journal contribution
posted on 2023-04-21, 16:09 authored by Andreas Christ Sølvsten Jørgensen, Ciaran Scott Hill, Marc SturrockMarc Sturrock, Wenhao Tang, Saketh R Karamched, Dunja Gorup, Mark F Lythgoe, Simona Parrinello, Samuel Marguerat, Vahid Shahrezaei

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 summarize the scope, drawbacks and assets. We highlight the potential clinical applications of each modelling technique and discuss the connections between the mathematical models and the molecular and imaging data used to inform them. By doing so, we aim to prime cancer researchers with current and emerging computational tools for understanding tumour progression. By providing an in-depth picture of the different modelling techniques, we also aim to assist researchers who seek to build and develop their own models and the associated inference frameworks. Our article thus strikes a unique balance. On the one hand, we provide a comprehensive overview of the available modelling techniques and their applications, including key mathematical expressions. On the other hand, the content is accessible to mathematicians and biomedical scientists alike to accommodate the interdisciplinary nature of cancer research. 


The Oli Hilsdon Foundation through The Brain Tumour Charity (grant no.GN-000595)

Cancer Research UK (CRUK) City of London Centre Award (C7893/A26233)

CRUK PioneerAward (C70568/A29787)

AMS Starter Grant (SGL021\1034)

National Brain Appeal Innovation Award(NBA/NSG/BTB)


The Edinburgh-UCL CRUK BrainTumour Centre of Excellence award (C7893/A27590)

Rosetrees Trust and the John Black Charitable Foundation (grant no. A2200)

CRUK (awards C55501/A21203 and 7550844)



The original article is available at

Published Citation

Jørgensen ACS. et al. Data-driven spatio-temporal modelling of glioblastoma. R Soc Open Sci. 2023;10(3):221444.

Publication Date

22 March 2023

PubMed ID



  • Physiology and Medical Physics


Royal Society Publishing


  • Published Version (Version of Record)