Center for Gamma-Ray Imaging, University of Arizona, Tucson, AZ, United States of America.
Program in Applied Mathematics, University of Arizona, Tucson, AZ, United States of America.
PLoS One. 2018 Jun 29;13(6):e0199823. doi: 10.1371/journal.pone.0199823. eCollection 2018.
Many different physiological processes affect the growth of malignant lesions and their response to therapy. Each of these processes is spatially and genetically heterogeneous; dynamically evolving in time; controlled by many other physiological processes, and intrinsically random and unpredictable. The objective of this paper is to show that all of these properties of cancer physiology can be treated in a unified, mathematically rigorous way via the theory of random processes. We treat each physiological process as a random function of position and time within a tumor, defining the joint statistics of such functions via the infinite-dimensional characteristic functional. The theory is illustrated by analyzing several models of drug delivery and response of a tumor to therapy. To apply the methodology to precision cancer therapy, we use maximum-likelihood estimation with Emission Computed Tomography (ECT) data to estimate unknown patient-specific physiological parameters, ultimately demonstrating how to predict the probability of tumor control for an individual patient undergoing a proposed therapeutic regimen.
许多不同的生理过程会影响恶性病变的生长及其对治疗的反应。这些过程在空间和遗传上都是异质的;随时间动态演变;受许多其他生理过程的控制,并且本质上是随机和不可预测的。本文的目的是表明,通过随机过程理论,可以以统一的、数学上严格的方式来处理癌症生理学的所有这些特性。我们将每个生理过程视为肿瘤内位置和时间的随机函数,通过无限维特征函数来定义此类函数的联合统计信息。通过分析几种药物输送模型和肿瘤对治疗的反应,对该理论进行了说明。为了将该方法应用于精准癌症治疗,我们使用最大似然估计和发射型计算机断层扫描 (ECT) 数据来估计未知的患者特异性生理参数,最终证明了如何为接受拟议治疗方案的个体患者预测肿瘤控制的概率。