IEEE Trans Med Imaging. 2017 Mar;36(3):815-825. doi: 10.1109/TMI.2016.2626443. Epub 2016 Nov 8.
In this article, we propose a proof of concept for the automatic planning of personalized radiotherapy for brain tumors. A computational model of glioblastoma growth is combined with an exponential cell survival model to describe the effect of radiotherapy. The model is personalized to the magnetic resonance images (MRIs) of a given patient. It takes into account the uncertainty in the model parameters, together with the uncertainty in the MRI segmentations. The computed probability distribution over tumor cell densities, together with the cell survival model, is used to define the prescription dose distribution, which is the basis for subsequent Intensity Modulated Radiation Therapy (IMRT) planning. Depending on the clinical data available, we compare three different scenarios to personalize the model. First, we consider a single MRI acquisition before therapy, as it would usually be the case in clinical routine. Second, we use two MRI acquisitions at two distinct time points in order to personalize the model and plan radiotherapy. Third, we include the uncertainty in the segmentation process. We present the application of our approach on two patients diagnosed with high grade glioma. We introduce two methods to derive the radiotherapy prescription dose distribution, which are based on minimizing integral tumor cell survival using the maximum a posteriori or the expected tumor cell density. We show how our method allows the user to compute a patient specific radiotherapy planning conformal to the tumor infiltration. We further present extensions of the method in order to spare adjacent organs at risk by re-distributing the dose. The presented approach and its proof of concept may help in the future to better target the tumor and spare organs at risk.
在本文中，我们提出了一个概念验证，用于自动规划脑肿瘤的个性化放射治疗。将胶质母细胞瘤生长的计算模型与指数细胞存活模型相结合，以描述放射治疗的效果。该模型针对特定患者的磁共振图像（MRI）进行个性化处理。它考虑了模型参数的不确定性，以及 MRI 分割的不确定性。计算出的肿瘤细胞密度概率分布，以及细胞存活模型，用于定义处方剂量分布，这是后续强度调制放射治疗（IMRT）计划的基础。根据可用的临床数据，我们比较了三种不同的情况来个性化模型。首先，我们考虑在治疗前进行单次 MRI 采集，这通常是临床常规的情况。其次，我们使用两个不同时间点的两次 MRI 采集来个性化模型并规划放射治疗。第三，我们包括分割过程中的不确定性。我们在两个被诊断患有高级别胶质瘤的患者身上展示了我们方法的应用。我们引入了两种方法来推导放疗处方剂量分布，这两种方法基于使用最大后验或期望肿瘤细胞密度最小化积分肿瘤细胞存活。我们展示了我们的方法如何允许用户计算出符合肿瘤浸润的患者特异性放疗计划。我们进一步提出了该方法的扩展，以通过重新分配剂量来保护相邻的危险器官。所提出的方法及其概念验证可能有助于未来更好地靶向肿瘤并保护危险器官。
