Biologically-Based Mathematical Modeling of Tumor Vasculature and Angiogenesis via Time-Resolved Imaging Data.
Biologically-Based Mathematical Modeling of Tumor Vasculature and Angiogenesis via Time-Resolved Imaging Data.
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DOI：
10.3390/cancers13123008
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发表时间：
2021-06-16
期刊：
影响因子：
5.2
通讯作者：
Yankeelov TE
中科院分区：
文献类型：
--
作者：
Hormuth DA 2nd;Phillips CM;Wu C;Lima EABF;Lorenzo G;Jha PK;Jarrett AM;Oden JT;Yankeelov TE
通过血管生成募集新血管是肿瘤发展的关键组成部分，其从根本上影响肿瘤生长和对治疗的反应。通过数学模型表征肿瘤诱导的血管生成可以使预测肿瘤反应和改善患者护理的方法成为可能。在这篇综述中，我们讨论了如何将时间分辨成像数据与数学建模相结合，系统地研究从细胞到组织的血管生成，并最终预测对治疗的反应。肿瘤相关的脉管系统负责输送营养物质，清除废物，并允许生长超过2-3 mm 3。此外，血管网络在空间和时间上都在变化，从根本上影响肿瘤对全身和放射治疗的反应。因此，对血管动力学的强大理解对于准确预测肿瘤生长以及建立最佳治疗方案以实现最佳肿瘤控制是必要的。这一目标需要理论和实验的紧密结合。定量和时间分辨成像方法已经成为能够在组织和细胞尺度上在治疗之前和治疗期间可视化和表征肿瘤血管特性的技术。与这些发展并行但独立的是，数学建模技术已经被开发出来，以使计算机模拟研究理论肿瘤和血管动力学成为可能。特别是，最近的努力试图整合理论和实验，以实现数据驱动的数学建模。这种数学模型通过从个体肿瘤血管系统获得的数据进行校准，以预测未来的血管生长、全身性药物的递送和对放射治疗的反应。在这篇综述中，我们讨论了可视化和量化血管动力学的实验技术，包括磁共振成像，微流体装置，共聚焦显微镜。然后，我们专注于这些实验措施与生物数学模型的整合，以产生可测试的预测。
The recruitment of new vasculature via angiogenesis is a critical component of tumor development, which fundamentally influences tumor growth and response to treatment. The characterization of tumor-induced angiogenesis via mathematical models could enable approaches to forecast tumor response and improve patient care. In this review, we discuss how time-resolved imaging data integrated with mathematical modeling can be used to systematically investigate angiogenesis from the cell to tissue scale and ultimately forecast response to therapy. Tumor-associated vasculature is responsible for the delivery of nutrients, removal of waste, and allowing growth beyond 2–3 mm3. Additionally, the vascular network, which is changing in both space and time, fundamentally influences tumor response to both systemic and radiation therapy. Thus, a robust understanding of vascular dynamics is necessary to accurately predict tumor growth, as well as establish optimal treatment protocols to achieve optimal tumor control. Such a goal requires the intimate integration of both theory and experiment. Quantitative and time-resolved imaging methods have emerged as technologies able to visualize and characterize tumor vascular properties before and during therapy at the tissue and cell scale. Parallel to, but separate from those developments, mathematical modeling techniques have been developed to enable in silico investigations into theoretical tumor and vascular dynamics. In particular, recent efforts have sought to integrate both theory and experiment to enable data-driven mathematical modeling. Such mathematical models are calibrated by data obtained from individual tumor-vascular systems to predict future vascular growth, delivery of systemic agents, and response to radiotherapy. In this review, we discuss experimental techniques for visualizing and quantifying vascular dynamics including magnetic resonance imaging, microfluidic devices, and confocal microscopy. We then focus on the integration of these experimental measures with biologically based mathematical models to generate testable predictions.
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影响因子：
3.5
作者：
Adhikarla V;Jeraj R
通讯作者：
Jeraj R
影响因子：
3.5
作者：
Adhikarla V;Jeraj R
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通讯作者：
Chaplain, MAJ
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通讯作者：
Merks, Roeland M. H.
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