Abstract

The growth of a tumour is a complex biological process driven by cell proliferation, nutrient supply, and interactions with the microenvironment. Accurate forecasting of tumor development is essential for early detection, treatment planning, and the customization of cancer treatment. The article presents a biomathematical method for predicting tumour growth based on computational modelling and parameter estimation. Tumour dynamics over time are represented using mathematical models, particularly growth models based on ordinary differential equations, such as exponential, logistic, and Gompertz formulations. Such models are coupled with computational simulations to study the growth behavior of various biological conditions. Experimental and clinical data are used to estimate parameters and determine essential growth parameters, including the tumor proliferation rate and carrying capacity, so that the models reflect real tumor behaviour. Estimation errors are minimized, and model reliability is maximized through optimization and numerical techniques. The performance of each model is evaluated by comparing simulated tumour growth curves with observed data, enabling assessment of the model's predictive accuracy and applicability. Results have demonstrated that, when combined with sound parameter estimation methods, bio-mathematical models can effectively elucidate tumour growth trends and provide useful predictions. The present strategy highlights the potential of computational modelling in oncology to gain insights into tumor dynamics, to inform treatment decisions, and to guide the design of tailored therapeutic approaches for patients.