Work

FEM-Based 3-D Tumor Growth Prediction for Kidney Tumor

Year: 2010

Type: article

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.

Source: IEEE Transactions on Biomedical Engineering

DOI: 10.1109/tbme.2010.2089522

Authors: Xinjian Chen, R Summers, Jianhua Yao

Institution: National Institutes of Health

Language: English

Topic: Mathematical Biology Tumor Growth

Subfield: Modeling and Simulation

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funder: National Institutes of Health