用于预测神经胶质瘤生长的机械耦合反应扩散模型：方法细节
Mechanically Coupled Reaction-Diffusion Model to Predict Glioma Growth: Methodological Details.
作者信息
Hormuth David A, Eldridge Stephanie L, Weis Jared A, Miga Michael I, Yankeelov Thomas E
机构信息
Institute for Computational and Engineering Sciences, The University of Texas at Austin, Austin, TX, USA.
Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA.
出版信息
Methods Mol Biol. 2018;1711:225-241. doi: 10.1007/978-1-4939-7493-1_11.
Abstract
Biophysical models designed to predict the growth and response of tumors to treatment have the potential to become a valuable tool for clinicians in care of cancer patients. Specifically, individualized tumor forecasts could be used to predict response or resistance early in the course of treatment, thereby providing an opportunity for treatment selection or adaption. This chapter discusses an experimental and modeling framework in which noninvasive imaging data is used to initialize and parameterize a subject-specific model of tumor growth. This modeling approach is applied to an analysis of murine models of glioma growth.
摘要
旨在预测肿瘤生长及对治疗反应的生物物理模型，有潜力成为临床医生治疗癌症患者的宝贵工具。具体而言，个体化的肿瘤预测可用于在治疗过程早期预测反应或耐药性，从而为治疗选择或调整提供机会。本章讨论了一个实验和建模框架，其中利用非侵入性成像数据来初始化和参数化肿瘤生长的个体特异性模型。这种建模方法应用于对神经胶质瘤生长的小鼠模型的分析。
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