FEM-Based 3-D Tumor Growth Prediction for Kidney Tumor
FEM-Based 3-D Tumor Growth Prediction for Kidney Tumor
复制标题
DOI：
10.1109/tbme.2010.2089522
复制
发表时间：
2011-03-01
影响因子：
4.6
通讯作者：
Yao, Jianhua
中科院分区：
文献类型：
--
作者：
Chen, Xinjian;Summers, Ronald;Yao, Jianhua
预测肿瘤的生长非常重要，以便可以在早期制定适当的治疗方案。在这封信中，我们提出了一种使用纵向肾肿瘤图像的基于有限元方法 (FEM) 的 3D 肿瘤生长预测系统。据我们所知，这是第一个肾脏肿瘤生长预测系统。肾脏组织分为三种类型：肾皮质、肾髓质和肾盂。应用反应扩散模型作为肿瘤生长模型。模型中考虑了不同的扩散特性：肾髓质的扩散被认为是各向异性的，而肾皮质和肾盂的扩散被认为是各向同性的。采用有限元法求解扩散模型。使用混合优化并行搜索包通过重叠精度目标函数的优化来估计模型参数。所提出的方法在两项纵向研究中进行了测试，研究涉及五个肿瘤的七个时间点。所有肿瘤的平均真阳性体积分数和假阳性体积分数分别为 91.4% 和 4.0%。实验结果表明了该方法的可行性和有效性。
It is important to predict the tumor growth so that appropriate treatment can be planned in the early stage. In this letter, we propose a finite-element method (FEM)-based 3-D tumor growth prediction system using longitudinal kidney tumor images. To the best of our knowledge, this is the first kidney tumor growth prediction system. The kidney tissues are classified into three types: renal cortex, renal medulla, and renal pelvis. The reaction-diffusion model is applied as the tumor growth model. Different diffusion properties are considered in the model: the diffusion for renal medulla is considered as anisotropic, while those of renal cortex and renal pelvis are considered as isotropic. The FEM is employed to solve the diffusion model. The model parameters are estimated by the optimization of an objective function of overlap accuracy using a hybrid optimization parallel search package. The proposed method was tested on two longitudinal studies with seven time points on five tumors. The average true positive volume fraction and false positive volume fraction on all tumors is 91.4% and 4.0%, respectively. The experimental results showed the feasibility and efficacy of the proposed method.