On the Application of Mathematical Models in Tumor Growth and Treatment

Abstract

Mathematical models have been employed for a significant period to replicate dynamic biological processes. In more recent years, quantitative methods have gained widespread use in cancer research. Over the past century, modelers have characterized and examined tumor growth kinetics, resulting in a diverse range of models that encompass simple empirical ones as well as complex functional models considering cell cycle kinetics, cell-cell interactions, cell age distribution, and microenvironmental factors. Nonetheless, these models are seldom verified with experimental tumor growth data due to limited suitable data availability. Increasingly, techniques from mathematics, physics, computational science, and engineering are being utilized to comprehend how cancer populations react to clinical treatments. This article delves into the essential principles of mathematical modeling in tumor growth and tumor-host interactions while emphasizing crucial approaches vital for cancer research.