Prediction of Lung Tumor Evolution During Radiotherapy in Individual Patients With PET
Prediction of Lung Tumor Evolution During Radiotherapy in Individual Patients With PET
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DOI：
10.1109/tmi.2014.2301892
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发表时间：
2014-04-01
影响因子：
10.6
通讯作者：
Ruan, Su
中科院分区：
文献类型：
--
作者：
Mi, Hongmei;Petitjean, Caroline;Ruan, Su
我们提出了一种基于部分微分方程的患者特异性模型，以预测放射疗法期间肺部肿瘤的演变。肿瘤细胞密度的演化由三个术语提出：1）描述肿瘤细胞的对流通量转运的对流，2）代表建模为Gompertz微分方程的肿瘤细胞增殖的增殖，3）量化量化近距性二次制剂的放射性治疗效率的处理。我们认为，肿瘤细胞密度变化可以从正电子发射断层扫描图像中得出，新颖的想法是通过从顺序图像中计算3D光流场来建模对流项。为了估计患者特异性参数，我们提出了预测图像和观察到的图像之间的优化，在全球限制下，肿瘤体积随着辐射剂量的增加而呈指数下降。然后，使用对预测的肿瘤细胞密度的阈值来定义肿瘤轮廓，肿瘤体积和最大标准化摄取值（SUVMAX）。七名患者获得的结果表明，预测的肿瘤轮廓与专家绘制的肿瘤轮廓之间达成了令人满意的一致性。
We propose a patient-specific model based on partial differential equation to predict the evolution of lung tumors during radiotherapy. The evolution of tumor cell density is formulated by three terms: 1) advection describing the advective flux transport of tumor cells, 2) proliferation representing the tumor cell proliferation modeled as Gompertz differential equation, and 3) treatment quantifying the radiotherapeutic efficacy from linear quadratic formulation. We consider that tumor cell density variation can be derived from positron emission tomography images, the novel idea is to model the advection term by calculating 3D optical flow field from sequential images. To estimate patient-specific parameters, we propose an optimization between the predicted and observed images, under a global constraint that the tumor volume decreases exponentially as radiation dose increases. A thresholding on the predicted tumor cell densities is then used to define tumor contours, tumor volumes and maximum standardized uptake values (SUVmax). Results obtained on seven patients show a satisfying agreement between the predicted tumor contours and those drawn by an expert.