Data-driven spatio-temporal modelling of glioblastoma

TL;DR

This paper reviews state-of-the-art mathematical models of glioblastoma, emphasizing their clinical relevance, data integration, and potential to advance understanding of tumor progression.

Contribution

It provides a comprehensive overview of current modelling techniques, their applications, limitations, and how they connect with molecular and imaging data in glioblastoma research.

Findings

Summarizes various modelling approaches and their clinical applications.

Highlights the integration of molecular and imaging data into models.

Discusses the strengths and weaknesses of each modelling technique.

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 summarise 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. Finally, 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.