Advancements in Digital Health Diagnostics: Mathematical Modelling in the Detection of Cancer Cells

The increasing understanding of cancer cells and its mathematical models has significantly advanced cancer research. Considerable progress has been made in theory, clinical methodologies and experimental techniques, enabling a deeper comprehension of cancer cell dynamics and their interactions with the immune system over recent decades. Utilizing a model-based approach has accelerated this process, facilitating the discovery of causative factors and effective treatments for cancer growth. This book chapter explores various tumour growth models, including the Gompertz model and linear growth model. Additionally, this chapter introduces a novel model that integrates elements of existing frameworks, extending their ability to assess the impact of nutrients such as glucose and oxygen on cancer cells. This extended model serves as a real-time tumour growth detector capable of evaluating the effects of time, glucose and oxygen concentration on cancer progression. Our findings demonstrate that over time, tumour cells transition from one stage, such as necrotic, to another, such as quiescent or proliferative, underscoring the dynamic nature of cancer development. This holistic approach sheds light on the dynamic interplay between cancer cells and the immune system, providing valuable insights for the development of targeted therapies and personalized treatment strategies in the digital health era.