多尺度、多分辨率脑癌建模
Multi-Scale, Multi-Resolution Brain Cancer Modeling
摘要：
在将基于离散的计算癌症模型推向临床应用的过程中，人们面临着如何处理不断增长的生物医学数据的困境，这些数据最终应该以一种或另一种形式合并。模型的可伸缩性成为最重要的兴趣。为了开始解决这个关键问题，在这里，我们提出了一种新的多尺度和多分辨率的基于代理的计算机胶质瘤模型。虽然“多尺度”是指采用表皮生长因子受体（EGFR）驱动的分子网络来处理微观-宏观环境内的细胞表型决定，但“多分辨率”是通过将细胞分类为活性或非活性空间簇的算法来实现的，所述算法确定它们被模拟的分辨率。其目的是将计算资源分配给特定的肿瘤区域和特定的时间，以维持或提高算法的预测能力。使用先前描述的2D脑肿瘤模型，我们已经开发了四种不同的计算方法来实现多分辨率方案，其中三种被设计为动态训练作为控制的高分辨率模拟。为了量化算法的性能，我们通过权衡模拟运行的不同计算时间节省与方法准确再现控制的高分辨率结果的能力来对其进行排名。最后，为了展示底层概念的灵活性，我们展示了结合两种排名最高的方法的附加值。
英文摘要：
In advancing discrete-based computational cancer models towards clinical applications, one faces the dilemma of how to deal with an ever growing amount of biomedical data that ought to be incorporated eventually in one form or another. Model scalability becomes of paramount interest. In an effort to start addressing this critical issue, here, we present a novel multi-scale and multi-resolution agent-based in silico glioma model. While "multi-scale" refers to employing an epidermal growth factor receptor (EGFR)-driven molecular network to process cellular phenotypic decisions within the micro-macroscopic environment, "multi-resolution" is achieved through algorithms that classify cells to either active or inactive spatial clusters, which determine the resolution they are simulated at. The aim is to assign computational resources where and when they matter most for maintaining or improving the predictive power of the algorithm, onto specific tumor areas and at particular times. Using a previously described 2D brain tumor model, we have developed four different computational methods for achieving the multi-resolution scheme, three of which are designed to dynamically train on the high-resolution simulation that serves as control. To quantify the algorithms' performance, we rank them by weighing the distinct computational time savings of the simulation runs versus the methods' ability to accurately reproduce the high-resolution results of the control. Finally, to demonstrate the flexibility of the underlying concept, we show the added value of combining the two highest-ranked methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
