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Mathematical Oncology: How Modeling Is Transforming Clinical Decision-Making
Kevin R Scibilia1, Kit Gallagher1,2, M A Masud1
1Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida.
Cancer Research
|October 17, 2025
Summary
Mathematical oncology models move beyond the "maximum tolerated dose" by integrating tumor dynamics. These computational tools personalize cancer treatment schedules, improving patient outcomes and driving discoveries in cancer research.
Area of Science:
- Computational biology
- Mathematical oncology
- Cancer research
Background:
- Current chemotherapy and radiotherapy protocols rely on mathematical models.
- The
- maximum tolerated dose
- approach does not account for cancer's dynamic and heterogeneous nature, especially in metastatic disease.
Purpose of the Study:
- To explore the integration of mathematical models in cancer treatment.
- To advance therapeutic strategies beyond the
- maximum tolerated dose
- paradigm.
- To highlight the role of computational research in cancer discoveries.
Main Methods:
- Utilizing mathematical models to capture dose-response, ecological (tumor-immune, competition), and evolutionary dynamics.
- Integrating models with virtual patient frameworks, digital twins, and artificial intelligence.
- Analyzing data from preclinical and clinical trials.
Main Results:
- Mathematical models show promise in personalizing treatment schedules and preclinical experiments.
- Models can capture complex tumor dynamics, leading to advanced therapeutic strategies.
- Recent trials demonstrate the potential of mathematical oncology in clinical decision-making.
Conclusions:
- Mathematical oncology offers a promising approach to personalize cancer therapy.
- Overcoming translational barriers, such as data standardization and regulatory constraints, is crucial for clinical integration.
- Computational methods are essential for driving future cancer discoveries and improving treatment outcomes.
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