Kidney tumor growth prediction by coupling reaction-diffusion and biomechanical model.

It is desirable to predict the tumor growth rate so that appropriate treatment can be planned in the early stage. Previously, we proposed a finite-element-method (FEM)-based 3-D kidney tumor growth prediction system using longitudinal images. A reaction-diffusion model was applied as the tumor growth model. In this paper, we not only improve the tumor growth model by coupling the reaction-diffusion model with a biomechanical model, but also take the surrounding tissues into account. Different diffusion and biomechanical properties are applied for different tissue types. An FEM is employed to simulate the coupled tumor growth model. Model parameters are estimated by optimizing an objective function of overlap accuracy using a hybrid optimization parallel search package. The proposed method was tested with kidney CT images of eight tumors from five patients with seven time points. The experimental results showed that the performance of the proposed method improved greatly compared to our previous work.