Adhikarla Vikram, Jeraj Robert
Department of Physics, University of Wisconsin, Madison, WI, USA. Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA.
Phys Med Biol. 2016 May 21;61(10):3885-902. doi: 10.1088/0031-9155/61/10/3885. Epub 2016 Apr 27.
Tumour growth, angiogenesis and oxygenation vary substantially among tumours and significantly impact their treatment outcome. Imaging provides a unique means of investigating these tumour-specific characteristics. Here we propose a computational model to simulate tumour-specific oxygenation changes based on the molecular imaging data. Tumour oxygenation in the model is reflected by the perfused vessel density. Tumour growth depends on its doubling time (T d) and the imaged proliferation. Perfused vessel density recruitment rate depends on the perfused vessel density around the tumour (sMVDtissue) and the maximum VEGF concentration for complete vessel dysfunctionality (VEGFmax). The model parameters were benchmarked to reproduce the dynamics of tumour oxygenation over its entire lifecycle, which is the most challenging test. Tumour oxygenation dynamics were quantified using the peak pO2 (pO2peak) and the time to peak pO2 (t peak). Sensitivity of tumour oxygenation to model parameters was assessed by changing each parameter by 20%. t peak was found to be more sensitive to tumour cell line related doubling time (30%) as compared to tissue vasculature density (10%). On the other hand, pO2peak was found to be similarly influenced by the above tumour- and vasculature-associated parameters (30-40%). Interestingly, both pO2peak and t peak were only marginally affected by VEGFmax (5%). The development of a poorly oxygenated (hypoxic) core with tumour growth increased VEGF accumulation, thus disrupting the vessel perfusion as well as further increasing hypoxia with time. The model with its benchmarked parameters, is applied to hypoxia imaging data obtained using a [(64)Cu]Cu-ATSM PET scan of a mouse tumour and the temporal development of the vasculature and hypoxia maps are shown. The work underscores the importance of using tumour-specific input for analysing tumour evolution. An extended model incorporating therapeutic effects can serve as a powerful tool for analysing tumour response to anti-angiogenic therapies.
肿瘤生长、血管生成和氧合在不同肿瘤之间存在显著差异,并对其治疗结果产生重大影响。成像为研究这些肿瘤特异性特征提供了独特的手段。在此,我们提出一种计算模型,基于分子成像数据模拟肿瘤特异性氧合变化。模型中的肿瘤氧合由灌注血管密度反映。肿瘤生长取决于其倍增时间(Td)和成像增殖情况。灌注血管密度招募率取决于肿瘤周围的灌注血管密度(sMVDtissue)以及导致血管完全功能障碍的最大VEGF浓度(VEGFmax)。对模型参数进行基准测试,以重现肿瘤在其整个生命周期内的氧合动态,这是最具挑战性的测试。使用峰值pO2(pO2peak)和达到峰值pO2的时间(t peak)对肿瘤氧合动态进行量化。通过将每个参数改变20%来评估肿瘤氧合对模型参数的敏感性。结果发现,与组织脉管系统密度(约10%)相比,t peak对肿瘤细胞系相关倍增时间更为敏感(约30%)。另一方面,发现pO2peak受上述肿瘤和脉管系统相关参数的影响类似(约30 - 40%)。有趣的是,pO2peak和t peak仅受到VEGFmax的轻微影响(约5%)。随着肿瘤生长,低氧(缺氧)核心的形成增加了VEGF积累,从而破坏血管灌注,并随着时间的推移进一步加剧缺氧。将具有基准参数的模型应用于使用[(64)Cu]Cu - ATSM PET扫描小鼠肿瘤获得的缺氧成像数据,并展示了脉管系统和缺氧图谱的时间演变。这项工作强调了使用肿瘤特异性输入来分析肿瘤演变的重要性。一个纳入治疗效果的扩展模型可作为分析肿瘤对抗血管生成疗法反应的有力工具。