Crispin-Ortuzar Mireia, Jeong Jeho, Fontanella Andrew N, Deasy Joseph O
Phys Med Biol. 2017 Apr 7;62(7):2658-2674. doi: 10.1088/1361-6560/aa5d42. Epub 2017 Jan 31.
Radiobiological models of tumour control probability (TCP) can be personalized using imaging data. We propose an extension to a voxel-level radiobiological TCP model in order to describe patient-specific differences and intra-tumour heterogeneity. In the proposed model, tumour shrinkage is described by means of a novel kinetic Monte Carlo method for inter-voxel cell migration and tumour deformation. The model captures the spatiotemporal evolution of the tumour at the voxel level, and is designed to take imaging data as input. To test the performance of the model, three image-derived variables found to be predictive of outcome in the literature have been identified and calculated using the model's own parameters. Simulating multiple tumours with different initial conditions makes it possible to perform an in silico study of the correlation of these variables with the dose for 50% tumour control ([Formula: see text]) calculated by the model. We find that the three simulated variables correlate with the calculated [Formula: see text]. In addition, we find that different variables have different levels of sensitivity to the spatial distribution of hypoxia within the tumour, as well as to the dynamics of the migration mechanism. Finally, based on our results, we observe that an adequate combination of the variables may potentially result in higher predictive power.
肿瘤控制概率（TCP）的放射生物学模型可利用成像数据实现个性化。我们提出对体素级放射生物学TCP模型进行扩展，以描述患者特异性差异和肿瘤内异质性。在所提出的模型中，肿瘤缩小通过一种用于体素间细胞迁移和肿瘤变形的新型动力学蒙特卡罗方法来描述。该模型在体素水平捕捉肿瘤的时空演变，并设计为以成像数据作为输入。为测试该模型的性能，已识别出文献中发现的可预测结果的三个图像衍生变量，并使用模型自身参数进行计算。通过模拟具有不同初始条件的多个肿瘤，能够对这些变量与模型计算的50%肿瘤控制剂量（[公式：见正文]）之间的相关性进行计算机模拟研究。我们发现这三个模拟变量与计算出的[公式：见正文]相关。此外，我们发现不同变量对肿瘤内缺氧的空间分布以及迁移机制的动力学具有不同程度的敏感性。最后，基于我们的结果，我们观察到变量的适当组合可能会带来更高的预测能力。
