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用于不同肿瘤生长和血管生成阶段的 FDG PET 成像的时空多尺度计算模型。

A spatiotemporal multi-scale computational model for FDG PET imaging at different stages of tumor growth and angiogenesis.

机构信息

Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran.

Department of Electrical and Computer Engineering, Faculty of Engineering, School of Optometry and Vision Science, Faculty of Science, University of Waterloo, Waterloo, Canada.

出版信息

Sci Rep. 2022 Jun 16;12(1):10062. doi: 10.1038/s41598-022-13345-4.

Abstract

A deeper understanding of the tumor microenvironment (TME) and its role in metabolic activity at different stages of vascularized tumors can provide useful insights into cancer progression and better support clinical assessments. In this study, a robust and comprehensive multi-scale computational model for spatiotemporal transport of F-18 fluorodeoxyglucose (FDG) is developed to incorporate important aspects of the TME, spanning subcellular-, cellular-, and tissue-level scales. Our mathematical model includes biophysiological details, such as radiopharmaceutical transport within interstitial space via convection and diffusion mechanisms, radiopharmaceutical exchange between intracellular and extracellular matrices by glucose transporters, cellular uptake of radiopharmaceutical, as well as its intracellular phosphorylation by the enzyme. Further, to examine the effects of tumor size by varying microvascular densities (MVDs) on FDG dynamics, four different capillary networks are generated by angiogenesis modeling. Results demonstrate that as tumor grows, its MVD increases, and hence, the spatiotemporal distribution of total FDG uptake by tumor tissue changes towards a more homogenous distribution. In addition, spatiotemporal distributions in tumor with lower MVD have relatively smaller magnitudes, due to the lower diffusion rate of FDG as well as lower local intravenous FDG release. Since mean standardized uptake value (SUV) differs at various stages of microvascular networks with different tumor sizes, it may be meaningful to normalize the measured values by tumor size and the MVD prior to routine clinical reporting. Overall, the present framework has the potential for more accurate investigation of biological phenomena within TME towards personalized medicine.

摘要

深入了解肿瘤微环境(TME)及其在血管化肿瘤不同阶段代谢活性中的作用,可以为癌症进展提供有用的见解,并更好地支持临床评估。在这项研究中,开发了一种强大而全面的多尺度计算模型,用于时空运输 F-18 氟脱氧葡萄糖(FDG),以纳入 TME 的重要方面,涵盖亚细胞、细胞和组织尺度。我们的数学模型包括生物物理细节,例如放射性药物通过对流和扩散机制在细胞间质中的运输、葡萄糖转运蛋白介导的细胞内外基质之间的放射性药物交换、放射性药物的细胞摄取以及酶对其的细胞内磷酸化。此外,为了通过改变微血管密度(MVD)来检查肿瘤大小的影响,通过血管生成建模生成了四个不同的毛细血管网络。结果表明,随着肿瘤的生长,其 MVD 增加,因此肿瘤组织对总 FDG 摄取的时空分布向更均匀的分布变化。此外,由于 FDG 的扩散率较低以及局部静脉内 FDG 释放较低,因此 MVD 较低的肿瘤中的时空分布幅度相对较小。由于 SUVmean 值在具有不同肿瘤大小的不同微血管网络的各个阶段有所不同,因此在常规临床报告之前,通过肿瘤大小和 MVD 对测量值进行归一化可能是有意义的。总体而言,该框架具有对 TME 内生物现象进行更准确研究的潜力,从而实现个性化医疗。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bafe/9203789/c6ffea101f1c/41598_2022_13345_Fig1_HTML.jpg

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