肿瘤生长动态是了解癌症进展和治疗反应的一个重要方面，以缓解医疗保健中最紧迫的挑战之一。通过计算了解肿瘤行为的计算机方法为湿实验室检查提供了一种高效、经济的替代方案，并且可适应不同的环境条件、时间尺度和独特的患者参数。因此，本文探讨了癌症中游离肿瘤生长的建模，调查了有关连续、离散和混合方法的当代文献。预测能力和高分辨率仿真等因素与这些模型中的仿真负载和参数可行性等缺点相竞争。了解不同场景和背景下的肿瘤行为成为推进癌症研究和彻底改变临床结果的第一步。

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.