Tumor growth dynamics serve as a critical aspect of understanding cancer progression and treatment response to mitigate one of the most pressing challenges in healthcare. The in silico approach to understanding tumor behavior computationally provides an efficient, cost-effective alternative to wet-lab examinations and are adaptable to different environmental conditions, time scales, and unique patient parameters. As a result, this paper explored modeling of free tumor growth in cancer, surveying contemporary literature on continuum, discrete, and hybrid approaches. Factors like predictive power and high-resolution simulation competed against drawbacks like simulation load and parameter feasibility in these models. Understanding tumor behavior in different scenarios and contexts became the first step in advancing cancer research and revolutionizing clinical outcomes.
Citation: Dashmi Singh, Dana Paquin. Modeling free tumor growth: Discrete, continuum, and hybrid approaches to interpreting cancer development[J]. Mathematical Biosciences and Engineering, 2024, 21(7): 6659-6693. doi: 10.3934/mbe.2024292
Tumor growth dynamics serve as a critical aspect of understanding cancer progression and treatment response to mitigate one of the most pressing challenges in healthcare. The in silico approach to understanding tumor behavior computationally provides an efficient, cost-effective alternative to wet-lab examinations and are adaptable to different environmental conditions, time scales, and unique patient parameters. As a result, this paper explored modeling of free tumor growth in cancer, surveying contemporary literature on continuum, discrete, and hybrid approaches. Factors like predictive power and high-resolution simulation competed against drawbacks like simulation load and parameter feasibility in these models. Understanding tumor behavior in different scenarios and contexts became the first step in advancing cancer research and revolutionizing clinical outcomes.
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Figures(8)
Dashmi Singh, Dana Paquin. Modeling free tumor growth: Discrete, continuum, and hybrid approaches to interpreting cancer development[J]. Mathematical Biosciences and Engineering, 2024, 21(7): 6659-6693. doi: 10.3934/mbe.2024292
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