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Updated: Feb 15, 2026
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A Coupled Experiment-finite Element Modeling Methodology for Assessing High Strain Rate Mechanical Response of Soft Biomaterials
Published on: May 18, 2015
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Mechanically Coupled Reaction-Diffusion Model to Predict Glioma Growth: Methodological Details
David A Hormuth1, Stephanie L Eldridge2,3, Jared A Weis4,5
1Institute for Computational and Engineering Sciences, The University of Texas at Austin, Austin, TX, USA. david.hormuth@austin.utexas.edu.
Methods in Molecular Biology (Clifton, N.J.)
|January 19, 2018
Summary
This study introduces a biophysical modeling framework using noninvasive imaging to predict tumor growth and treatment response. This approach enables individualized cancer forecasts for early treatment adaptation in patients.
Area of Science:
- Oncology
- Biophysics
- Computational Biology
Background:
- Biophysical models can predict tumor growth and treatment response.
- Individualized tumor forecasts can guide early treatment selection or adaptation.
- Noninvasive imaging data is crucial for subject-specific modeling.
Purpose of the Study:
- To present an experimental and modeling framework for subject-specific tumor growth prediction.
- To utilize noninvasive imaging data for initializing and parameterizing tumor models.
- To apply this modeling approach to murine glioma models.
Main Methods:
- Developing a biophysical model for tumor growth.
- Integrating noninvasive imaging data for model initialization.
- Parameterizing subject-specific tumor models.
- Analyzing murine glioma models using the developed framework.
Main Results:
- The framework successfully initializes and parameterizes subject-specific tumor growth models.
- The approach is applicable to analyzing tumor dynamics in experimental models.
- Demonstrated potential for predicting tumor response to treatment.
Conclusions:
- Biophysical modeling combined with noninvasive imaging offers a powerful tool for personalized cancer care.
- This framework can lead to early prediction of treatment response or resistance.
- Enables timely treatment selection or adaptation for improved patient outcomes.
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