Mathematical Oncology: How Modeling Is Transforming Clinical Decision-Making

Mathematical models have played a significant role in the development of current chemo- and radiotherapy treatment protocols. The widespread use of cytotoxic drugs has shaped the paradigm of uniformly administering a "maximum tolerated dose" to patients; however, this approach fails to account for the dynamic and heterogeneous nature of challenging cancers, including metastatic disease. Recent clinical trials and regulatory decisions have aimed to address these issues by integrating mathematical models to drive preclinical experiments and personalize treatment schedules. By capturing mechanisms of dose-response dynamics, ecological dynamics such as tumor-immune interactions or competition dynamics, and evolutionary dynamics across different therapeutic regimens, mathematical models hold the potential to advance current therapeutic strategies. As more preclinical and clinical data become available, the integration of mathematical models with "virtual patient" frameworks, including "digital twins," and artificial intelligence methods could further advance the mechanistic complexity and decision support capabilities of such models. Nonetheless, translating mechanistic models to routine clinical workflows will require overcoming current translational barriers, notably access to clinical data in standardized formats and regulatory constraints. Overall, recent trials demonstrate the promise of the field of mathematical oncology in translating predictive dynamics into treatment decision-making beyond the "maximum tolerated dose" approach. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI .