FEM-based 3-D tumor growth prediction for kidney tumor.

Abstract

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.