School of Mathematical & Statistical Sciences, Arizona State University, Tempe, AZ 85287-1804, USA.
Biol Direct. 2011 Dec 21;6:64. doi: 10.1186/1745-6150-6-64.
Data assimilation refers to methods for updating the state vector (initial condition) of a complex spatiotemporal model (such as a numerical weather model) by combining new observations with one or more prior forecasts. We consider the potential feasibility of this approach for making short-term (60-day) forecasts of the growth and spread of a malignant brain cancer (glioblastoma multiforme) in individual patient cases, where the observations are synthetic magnetic resonance images of a hypothetical tumor.
We apply a modern state estimation algorithm (the Local Ensemble Transform Kalman Filter), previously developed for numerical weather prediction, to two different mathematical models of glioblastoma, taking into account likely errors in model parameters and measurement uncertainties in magnetic resonance imaging. The filter can accurately shadow the growth of a representative synthetic tumor for 360 days (six 60-day forecast/update cycles) in the presence of a moderate degree of systematic model error and measurement noise.
The mathematical methodology described here may prove useful for other modeling efforts in biology and oncology. An accurate forecast system for glioblastoma may prove useful in clinical settings for treatment planning and patient counseling.
This article was reviewed by Anthony Almudevar, Tomas Radivoyevitch, and Kristin Swanson (nominated by Georg Luebeck).
数据同化是指通过将新的观测值与一个或多个先前的预报相结合，来更新复杂时空模型（如数值天气预报模型）的状态向量（初始条件）的方法。我们考虑了这种方法在对个体患者的恶性脑癌（多形性胶质母细胞瘤）的生长和扩散进行短期（60 天）预测中的潜在可行性，其中观测值是假设肿瘤的合成磁共振图像。
我们将一种现代状态估计算法（局部集合变换卡尔曼滤波器）应用于两种不同的胶质母细胞瘤数学模型，同时考虑了模型参数中的可能误差和磁共振成像中的测量不确定性。在存在中度系统模型误差和测量噪声的情况下，该滤波器可以准确地模拟代表合成肿瘤的生长 360 天（六个 60 天的预测/更新周期）。
本文描述的数学方法可能对生物学和肿瘤学的其他建模工作有用。胶质母细胞瘤的准确预测系统可能在临床环境中对治疗计划和患者咨询有用。
本文由 Anthony Almudevar、Tomas Radivoyevitch 和 Kristin Swanson（由 Georg Luebeck 提名）审稿。
