Mathematical Perspectives on Clinical Oncology: Modelling Cancer Dynamics

The integration of mathematical models into clinical oncology has emerged as a powerful tool for understanding and predicting cancer dynamics, with applications ranging from forecasting tumor growth and identifying prognostic biomarkers to optimizing therapeutic response. This talk will provide an overview of recent advances in mathematical oncology, focusing on the role of quantitative models in unraveling the complexities of cancer progression and treatment.

At the Mathematical Oncology Laboratory (MOLAB), through close collaboration with biomedical institutions, we have built models that delve into key areas of cancer dynamics, including the discovery of universal scaling laws that govern tumor growth and metabolic demands across various tumor types, based on large patient cohorts. Another contribution has been the development of evolutionary models to capture tumor heterogeneity, offering novel biomarkers that predict patient outcomes by identifying regions of heightened proliferative activity at the tumor periphery. Through these interdisciplinary efforts, we aim to show how mathematical models are not only advancing our understanding of cancer biology but also driving innovations in personalized medicine and enhancing clinical decision-making. This talk will explore these developments and their transformative potential in cancer treatment.