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Data-driven spatio-temporal modelling of glioblastoma

Jørgensen, Andreas Christ Sølvsten; Hill, Ciaran Scott; Sturrock, Marc; Tang, Wenhao; Karamched, Saketh R; Gorup, Dunja; Lythgoe, Mark F; ... Shahrezaei, Vahid; + view all (2023) Data-driven spatio-temporal modelling of glioblastoma. Royal Society Open Science , 10 (3) , Article 221444. 10.1098/rsos.221444. Green open access

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Abstract

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.

Type: Article
Title: Data-driven spatio-temporal modelling of glioblastoma
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1098/rsos.221444
Publisher version: https://doi.org/10.1098/rsos.221444
Language: English
Additional information: © 2023 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.
Keywords: Bayesian inference, agent-based modelling, data-driven modelling, glioblastoma, reaction–diffusion equations
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute > Research Department of Cancer Bio
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine > Experimental and Translational Medicine
URI: https://discovery.ucl.ac.uk/id/eprint/10167425
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