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Updated: Feb 19, 2026
Translational Orthotopic Models of Glioblastoma Multiforme
Published on: February 17, 2023
Clinical implications of in silico mathematical modeling for glioblastoma: a critical review
Maria Protopapa1, Anna Zygogianni1, Georgios S Stamatakos2
1Radiation Oncology Unit, 1st Department of Radiology, Aretaieio University Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece.
Abstract:
Glioblastoma remains a clinical challenge in spite of years of extensive research. Novel approaches are needed in order to integrate the existing knowledge. This is the potential role of mathematical oncology. This paper reviews mathematical models on glioblastoma from the clinical doctor's point of view, with focus on 3D modeling approaches of radiation response of in vivo glioblastomas based on contemporary imaging techniques. As these models aim to provide a clinically useful tool in the era of personalized medicine, the integration of the latest advances in molecular and imaging science and in clinical practice by the in silico models is crucial for their clinical relevance. Our aim is to indicate areas of GBM research that have not yet been addressed by in silico models and to point out evidence that has come up from in silico experiments, which may be worth considering in the clinic. This review examines how close these models have come in predicting the outcome of treatment protocols and in shaping the future of radiotherapy treatments.
Insights
Mathematical oncology offers novel approaches for glioblastoma (GBM) research by integrating knowledge. This review focuses on 3D mathematical models of GBM radiation response, assessing their clinical relevance and future potential in personalized medicine.
Area of Science:
- Oncology
- Mathematical Biology
- Medical Imaging
Background:
- Glioblastoma (GBM) presents a significant clinical challenge despite extensive research.
- Novel integrative approaches are required to advance GBM treatment strategies.
- Mathematical oncology offers a promising framework for integrating existing knowledge.
Purpose of the Study:
- To review mathematical models of glioblastoma from a clinical perspective.
- To focus on 3D modeling of radiation response in vivo using contemporary imaging.
- To identify underexplored areas in GBM research for in silico models and highlight clinically relevant findings.
Main Methods:
- Review of existing literature on mathematical oncology and glioblastoma.
- Focus on 3D modeling approaches integrating imaging data.
- Analysis of in silico model predictions versus clinical outcomes.
Main Results:
- 3D mathematical models show potential for predicting glioblastoma radiation response.
- Integration of molecular, imaging, and clinical data is crucial for model relevance.
- In silico experiments provide evidence that may inform clinical practice.
Conclusions:
- Mathematical oncology models are advancing towards clinical utility in personalized medicine for glioblastoma.
- Further development is needed to fully integrate diverse scientific and clinical data.
- These models hold promise for shaping future radiotherapy treatments and improving patient outcomes.
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