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Data-driven spatio-temporal modelling of glioblastoma.

Andreas Christ Sølvsten Jørgensen1, Ciaran Scott Hill2,3, Marc Sturrock4

  • 1Department of Mathematics, Faculty of Natural Sciences, Imperial College London, London SW7 2AZ, UK.

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|March 27, 2023
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Summary

Mathematical oncology uses advanced modeling to study tumor growth, offering insights into glioblastoma progression. This review details computational tools for cancer researchers to understand and develop new therapeutic strategies.

Keywords:
Bayesian inferenceagent-based modellingdata-driven modellingglioblastomareaction–diffusion equations

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Area of Science:

  • Interdisciplinary research at the intersection of mathematics, computational biology, and oncology.

Background:

  • Mathematical oncology offers critical insights into tumor growth dynamics at various scales.
  • Glioblastoma, a complex brain cancer, presents unique challenges for understanding tumor progression.

Purpose of the Study:

  • To review state-of-the-art mathematical modeling techniques for understanding glioblastoma.
  • To highlight the clinical applications and data integration of these computational tools.
  • To provide a resource for cancer researchers on computational tools for tumor progression.

Main Methods:

  • Comprehensive review of diverse mathematical modeling approaches.
  • Analysis of the scope, advantages, and limitations of each technique.
  • Discussion of the integration of molecular and imaging data with mathematical models.

Main Results:

  • Detailed overview of current and emerging computational tools in mathematical oncology.
  • Summary of key mathematical expressions and their relevance to glioblastoma.
  • Exploration of the connections between models and empirical data.

Conclusions:

  • Mathematical modeling is essential for advancing the understanding of glioblastoma.
  • The review balances in-depth technical details with accessibility for interdisciplinary researchers.
  • This work aims to equip researchers with tools for developing novel models and inference frameworks.