Yankeelov Thomas E
Institute of Imaging Science, Vanderbilt University, AA-1105 Medical Center North, 1161 21st Avenue South, Nashville, TN 37232-2310, USA ; Department of Radiology and Radiological Sciences, Vanderbilt University, AA-1105 Medical Center North, 1161 21st Avenue South, Nashville, TN 37232-2310, USA ; Department of Biomedical Engineering, Vanderbilt University, AA-1105 Medical Center North, 1161 21st Avenue South, Nashville, TN 37232-2310, USA ; Department of Physics, Vanderbilt University, AA-1105 Medical Center North, 1161 21st Avenue South, Nashville, TN 37232-2310, USA ; Department of Cancer Biology, Vanderbilt University, AA-1105 Medical Center North, 1161 21st Avenue South, Nashville, TN 37232-2310, USA ; Vanderbilt Ingram Cancer Center, Vanderbilt University, 1161 21st Avenue South, Nashville, TN 37232-2310, USA.
ISRN Biomath. 2012;2012. doi: 10.5402/2012/287394.
While there is a mature literature on biomathematical and biophysical modeling in cancer, many of the existing approaches are not of clinical utility, as they require input data that are extremely difficult to obtain in an intact organism, and/or require a large number of assumptions on the free parameters included in the models. Thus, there has only been very limited application of such models to solve problems of clinical import. More recently, however, there has been increased activity at the interface of quantitative, noninvasive imaging data, and tumor mathematical modeling. In addition to reporting on bulk tumor morphology and volume, emerging imaging techniques can quantitatively report on for example tumor vascularity, glucose metabolism, cell density and proliferation, and hypoxia. In this paper, we first motivate the problem of predicting therapy response by highlighting some (acknowledged) shortcomings in existing methods. We then provide introductions to a number of representative quantitative imaging methods and describe how they are currently (and potentially can be) used to initialize and constrain patient specific mathematical and biophysical models of tumor growth and treatment response, thereby increasing the clinical utility of such approaches. We conclude by highlighting some of the exciting research directions when one integrates quantitative imaging and tumor modeling.
虽然在癌症的生物数学和生物物理建模方面已有成熟的文献，但许多现有方法并不具有临床实用性，因为它们需要在完整生物体中极难获得的输入数据，并且/或者需要对模型中包含的自由参数做出大量假设。因此，此类模型在解决具有临床重要性的问题上的应用非常有限。然而，最近在定量、无创成像数据与肿瘤数学建模的交叉领域有了更多的研究活动。除了报告肿瘤的整体形态和体积外，新兴的成像技术还可以定量报告例如肿瘤血管生成、葡萄糖代谢、细胞密度和增殖以及缺氧情况。在本文中，我们首先通过强调现有方法中的一些（公认的）缺点来引出预测治疗反应的问题。然后，我们介绍一些具有代表性的定量成像方法，并描述它们目前（以及潜在地可以）如何用于初始化和约束针对患者的肿瘤生长和治疗反应的数学和生物物理模型，从而提高此类方法的临床实用性。我们通过强调将定量成像与肿瘤建模相结合时一些令人兴奋的研究方向来结束本文。
