Service de neurochirurgie du Pr George, hôpital Lariboisière, 2 rue Ambroise-Paré, Paris, France.
Rev Neurol (Paris). 2011 Oct;167(10):715-20. doi: 10.1016/j.neurol.2011.07.009. Epub 2011 Sep 3.
The advent of Magnetic Resonance Imaging (MRI) has enabled quantification of glioma growth with millimetric accuracy. Thus, it is now possible to monitor the growth curve of tumor diameter for each patient. Mathematical modeling contributes to the analysis of these curves and to determining individual parameters characterizing tumor dynamics. We will focus on the most studied model, based on a proliferation-diffusion equation. We will review how this approach, when applied to low-grade gliomas, has enabled defining a new way to quantify their natural history, leading to the inclusion of tumor kinetics among prognostic factors. Finally, quantitative imaging coupled with mathematical modeling is opening new avenues in our understanding of treatment effects, allowing to optimize therapeutic strategies for gliomas in the near future.
磁共振成像（MRI）的出现使胶质瘤的生长能够以毫米级的精度进行定量。因此，现在可以监测每个患者的肿瘤直径生长曲线。数学建模有助于分析这些曲线，并确定描述肿瘤动力学的个体参数。我们将重点介绍研究最多的模型，该模型基于增殖-扩散方程。我们将回顾当应用于低级别胶质瘤时，这种方法如何能够定义一种量化其自然史的新方法，从而使肿瘤动力学成为预后因素之一。最后，定量成像与数学建模相结合，为我们理解治疗效果开辟了新的途径，使我们能够在不久的将来优化胶质瘤的治疗策略。
