Cell-based models provide a helpful approach for simulating complex systems that exhibit adaptive, resilient qualities, such as cancer. Their focus on individual cell interactions makes them a particularly appropriate strategy to study the effects of cancer therapies, which often are designed to disrupt single-cell dynamics. In this work, we also propose them as viable methods for studying the time evolution of cancer imaging biomarkers (IBM). We propose a cellular automata model for tumor growth and three different therapies: chemotherapy, radiotherapy, and immunotherapy, following well-established modeling procedures documented in the literature. The model generates a sequence of tumor images, from which time series of two biomarkers: entropy and fractal dimension, is obtained. Our model shows that the fractal dimension increased faster at the onset of cancer cell dissemination, while entropy was more responsive to changes induced in the tumor by the different therapy modalities. These observations suggest that the predictive value of the proposed biomarkers could vary considerably with time. Thus, it is important to assess their use at different stages of cancer and for different imaging modalities. Another observation derived from the results was that both biomarkers varied slowly when the applied therapy attacked cancer cells in a scattered fashion along the automatons' area, leaving multiple independent clusters of cells at the end of the treatment. Thus, patterns of change of simulated biomarkers time series could reflect on essential qualities of the spatial action of a given cancer intervention.
翻译:以细胞为基础的模型为模拟具有适应性和耐受力性(如癌症)的复杂系统提供了一种有益的方法; 以细胞为基础的模型为模拟具有适应性和耐受性(如癌症)的复杂系统提供了一种有益的方法; 以个别细胞互动为重点,使它们成为研究癌症疗法影响的特别适当战略,而癌症疗法往往是用来破坏单细胞动态的。 在这项工作中,我们还提出这些模型作为研究癌症成像生物标志(IBM)时间演变的可行方法。 我们提议为肿瘤生长和三种不同的疗法(化疗、放射疗法和免疫疗法)提供一个细胞自动自动成像模型,遵循文献中记载的完善的模型程序。 模型产生一系列肿瘤图像,从中获取两个生物标志的时间序列:诱变和畸形维度。 我们的模型显示,在癌症细胞传播开始时,分形值增加的速度更快,同时对不同治疗模式引起的肿瘤变化反应更加迅速。 这些观察显示,拟议的生物标志的预测值随着时间的变化,可能随着时间的变化而有很大差异。 因此,必须评估它们在不同癌症阶段和不同的成像模式的使用情况。 另一个观察结果显示, 在癌症的模型中,在生物模型的分级分析中, 的分级的分级分析中, 的分级的分级反应在生物标记在生物模型的分级反应中, 的分级中,在生物标记在生物模型的分级中, 的分级的分级的分级的分级反应在生物序列中, 的分级反应在生物序列的分级反应在生物序列中, 的分级反应在生物序列中, 的分级的分级的分级反应了癌症的分级的分级的分级的分级反应在生物序列的分级反应了癌症的分级反应在生物序列的分级反应了癌症的分级的分级的分级的分级性反应了癌症的分级反应了癌症的分级反应了癌症的分级中, 的分级的分级的分级的分级的分级的分级的分级的分级中, 。