Cisneros-Sanchez Ana Karina, Flores-Alvarez Eduardo, Melendez-Mier Guillermo, Roldan-Valadez Ernesto
Directorate of Research, General Hospital of Mexico "Dr. Eduardo Liceaga", Mexico City, Mexico.
Department of Neurosurgery, General Hospital of Mexico "Dr. Eduardo Liceaga", Mexico City, Mexico.
Neurol India. 2018 Nov-Dec;66(6):1575-1583. doi: 10.4103/0028-3886.246238.
The battle against cancer has intensified in the last decade. New experimental techniques and theoretical models have been been proposed to understand the behavior, growth, and evolution of different types of brain tumors. Unfortunately, for glioblastoma multiforme (GBM), except for methylation of the O6-methylguanine-DNA methyltransferase (MGMT) promoter that has some benefit in the local control of tumors using alkylating agents such as temozolomide, to date personalized treatments do not exist. In this article, we present a comprehensive review of different aspects intertwined in the mathematical growth modeling applied to high-grade gliomas. We briefly cover the following fundamental aspects related to the conventional imaging in GBM: defining the tumor regions in GBM, segmentation of the tumor regions using magnetic resonance imaging (MRI) of the brain, response assessment using the neuro-oncology response criteria versus the Macdonald criteria, availability of software for the segmentation of MRI of the brain, mathematical modeling applied to tumor growth, principles of mathematical modeling, factors involved in tumor growth models, mathematical modeling based on imaging data, most common equations used in high-grade glioma growth modeling, integration of mathematical growth models in computer simulators, tumor growth modeling as a part of brain's complex system, and challenges in mathematical growth modeling. We conclude by saying that it is the combination of biomedical imaging and mathematical modeling that allows the assembling of clinically relevant models of tumor growth and treatment response; the most appropriate model will depend on the premise and findings of each experiment.
在过去十年中，抗癌斗争愈演愈烈。人们提出了新的实验技术和理论模型，以了解不同类型脑肿瘤的行为、生长和演变。不幸的是，对于多形性胶质母细胞瘤（GBM），除了O6-甲基鸟嘌呤-DNA甲基转移酶（MGMT）启动子甲基化在使用替莫唑胺等烷化剂进行肿瘤局部控制方面有一定益处外，迄今为止尚无个性化治疗方案。在本文中，我们对应用于高级别胶质瘤的数学生长建模中相互交织的不同方面进行了全面综述。我们简要介绍了与GBM传统成像相关的以下基本方面：在GBM中定义肿瘤区域、使用脑部磁共振成像（MRI）对肿瘤区域进行分割、使用神经肿瘤学反应标准与麦克唐纳标准进行反应评估、用于脑部MRI分割的软件可用性、应用于肿瘤生长的数学建模、数学建模原理、肿瘤生长模型中涉及的因素、基于成像数据的数学建模、高级别胶质瘤生长建模中最常用的方程、将数学生长模型集成到计算机模拟器中、将肿瘤生长建模作为大脑复杂系统的一部分以及数学生长建模中的挑战。我们总结说，正是生物医学成像和数学建模的结合，使得能够构建出与临床相关的肿瘤生长和治疗反应模型；最合适的模型将取决于每个实验的前提和结果。
