| Summary: | Recent years have seen the significant developments in theoretical, experimental and clinical approaches to understand the dynamics of cancer cells, the mechanism, cancer evolution and treatment resistances. Within the last five years, attention has been given on intensively investigating the evolution of cancer cells through model-based approach. In order to rationalize the treatment personalization and address the treatment failure, the use of mathematical modelling is widely accepted to support drug and treatment decision making. By now, several mathematical models for the cancer cell growth process have been formulated in the literature. It is clear that cancer evolution operates in a highly uncertain environment as a result of the noisy behaviour in the human body. To reflect the realistic behaviour of the cancerous cell growth, a mathematical model that describes this process should take into account the stochastic effects. To the fact that the stochastic models incorporate the random effects that may influence the behaviour of physical systems, mathematical models which is a stochastic differential equation (SDE) known as stochastic Gompertz model have been developed to understand the cancer growth process and its response to the cancer treatment methods. The objective of this paper is to investigate the performance of stochastic Gompertz model in predicting the growth of cervical cancer cells compared to the performance of deterministic model and the forecasting model of cervical cancer cell growth. The stochastic and deterministic Gompertz model will be solved via numerical method and the result will be presented together with the forecasting model of cervical cancer cell growth. The performance of stochastic Gompertz model, deterministic model and cervical cancer forecasting model in predicting the growth of cervical cancer cells will be analysed by using Root Mean-squared error (RMSE) and Global error. The present result will suggest the best model representing the growth of cancer cells, hence provide the better understanding of cancer evolution to overcome the treatment resistance. This useful clinical knowledge may help oncologists to design better treatment strategies and brings opportunities to treat cancer patients. © 2024 Author(s).
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