In Silico Mathematical Modelling for Glioblastoma: A Critical Review and a Patient-Specific Case.

Area of Science:

• Neuro-oncology
• Biomathematics
• Computational Biology

Background:

• Glioblastoma (GBM) is an aggressive brain tumor with a poor prognosis despite current treatments.
• Understanding GBM evolution at a patient-specific level is crucial for developing tailored therapies.
• A multidisciplinary approach integrating mathematical, clinical, and radiological data is promising.

Purpose of the Study:

• To critically review mathematical models used in neuro-oncology for glioblastoma.
• To classify existing models and identify significant advancements.
• To explore the clinical implications and potential benefits of mathematical modeling in GBM patient care.

Main Methods:

• Comprehensive literature search and review of English-language articles on mathematical modeling in glioblastoma.
• Classification and analysis of different mathematical models and their contributions.
• Application of a novel mechanical model to a specific glioblastoma patient case.

Main Results:

• Identified and classified various mathematical models for glioblastoma.
• Demonstrated the potential of these models to describe tumor complexity and predict evolution.
• Presented a case study showcasing the application of an innovative mechanical model.

Conclusions:

• Mathematical models hold significant potential to benefit clinicians and improve glioblastoma treatment strategies.
• These models can aid in enhancing tumor control and improving patient prognosis.
• Further prospective comparative trials are needed to validate the clinical impact of mathematical neuro-oncology.