Abstract

The natural history of cancer in a patient can be simulated in a flexible broad-based disease model. Such models describe events and outcomes at the person level, the results of which can be aggregated to estimate the population morbidity. With minimal assumptions regarding the natural history of disease, models could project outcomes of interventions for cancer screening and treatment. The same could be used for testing conventional treatment protocols and assess the reasons for failure of a particular strategy. Leads from the analysis can be utilized for proposing improvisations for the treatment protocol such as combinatorial strategies, which enables better suppression of tumor despite the resistant cells. This commentary describes relevant co ncepts associated with simulation modeling of tumor growth and tumor-host interactions, and summarizes some of the prominent approaches.