Mathematical Modeling in Improving Cancer Treatments

Abstract

Cancer is a leading cause of mortality worldwide and the major exhausting factor for social resources in healthcare, medical treatment, and the loss of working force. Therefore, developing cancer therapy methods and appropriate prognosis or assessment for cancer therapies are of critical importance. Due to the high cost in exploration and assessment of cancer therapy methods, mathematical modeling of the immune system is viewed as a potentially powerful tool in the development of improved treatment regimens and prediction of disease progression. In the present work, several general principles in mathematical modeling of immune–tumor interactions and cancer therapies are summarized first. Secondly, the acquisition of the parameter values and model calibration are discussed according to mathematical techniques in qualitative analysis. Moreover, various therapy strategies are tested on the constructed mathematical model, from which constructive suggestions for developing new clinical treatment methods are provided. Additionally, some general guidance for new therapies are also discussed by analyzing the sensitivity of the system parameters. In the end, we also discuss essential difficulties in building the mathematical model for cancer patients.