Description of Invention: The invention pertains to a computerized method of predicting kidney tumor growth for early stage treatment planning. The method utilizes a finite element method (FEM)-based 3D tumor growth prediction system using longitudinal kidney tumor images. 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: anisotropic for renal medulla and isotropic for renal cortex and renal pelvis. The FEM is employed to solve the diffusion model. The model parameters are estimated by optimizing of an objective function. Ultimately, longitudinal data is used to fit the tumor growth model. The technique was tested on two longitudinal studies with seven time points on five tumors. The experimental results (average of 91.4% true positive volume fraction and 4.0% of false positive volume fraction) showed the feasibility and efficacy of the technique.
Applications: The technique can be used to predict kidney tumor growth pattern using CT data. It can be effectively used in planning therapeutic regimen in early stage kidney tumors.
Advantages: The technique is the first kidney tumor growth prediction system. It can be implemented in the oncology package that most major imaging companies have in their commercial workstation.
For Licensing Information Please Contact: Michael Shmilovich Esq. NIH Office of Technology Transfer 6011 Executive Blvd. Suite 325, Rockville, MD 20852 United States Email: shmilovm@mail.nih.gov Phone: 301-435-5019 Fax: 301-402-0220
Description of Invention:
The invention pertains to a computerized method of predicting kidney tumor growth for early stage treatment planning. The method utilizes a finite element method (FEM)-based 3D tumor growth prediction system using longitudinal kidney tumor images. 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: anisotropic for renal medulla and isotropic for renal cortex and renal pelvis. The FEM is employed to solve the diffusion model. The model parameters are estimated by optimizing of an objective function. Ultimately, longitudinal data is used to fit the tumor growth model. The technique was tested on two longitudinal studies with seven time points on five tumors. The experimental results (average of 91.4% true positive volume fraction and 4.0% of false positive volume fraction) showed the feasibility and efficacy of the technique.
Applications:
The technique can be used to predict kidney tumor growth pattern using CT data. It can be effectively used in planning therapeutic regimen in early stage kidney tumors.
Advantages:
The technique is the first kidney tumor growth prediction system. It can be implemented in the oncology package that most major imaging companies have in their commercial workstation.
Development Status:
Inventors:
Ronald M Summers (CC)
Patent Status:
HHS, Reference No. E-250-2011/0
Research Tool — Patent protection is not being pursued for this technology.
Relevant Publication:
For Licensing Information Please Contact:
Michael Shmilovich Esq.
NIH Office of Technology Transfer
6011 Executive Blvd. Suite 325,
Rockville, MD 20852
United States
Email: shmilovm@mail.nih.gov
Phone: 301-435-5019
Fax: 301-402-0220
Ref No: 2316
Updated: 09/2011