  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.
