Mathematical oncology is an interdisciplinary field that utilizes mathematical modeling and computer simulations to study cancer, with the primary goal of understanding tumor behavior and improving treatment strategies. Its foundational efforts date back to Teorell's 1937 work on drug kinetics, with epidemiological data modeling gaining prominence in 1954. The field employs various models, including those for mechanistical processes and cell population evolution, often using differential equations to represent tumor growth, angiogenesis, metastasis, and responses to therapy.

A crucial application is the optimization of cancer therapies like radiotherapy and chemotherapy, where control theory is applied to maximize treatment efficacy while minimizing adverse effects. Recent advancements in artificial intelligence (AI) and machine learning have significantly enhanced mathematical oncology, allowing for the prediction of individual treatment responses and the development of personalized therapeutic strategies by processing vast amounts of patient data. This innovative approach ultimately reduces the need for early experimental trials and accelerates the development of more effective, data-driven cancer treatment protocols.