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Modeling Breast Cancer in Human Breast Tissue using a Microphysiological System

Modeling Breast Cancer in Human Breast Tissue using a Microphysiological System

Published on: April 23, 2021

Non-linear modelling of breast tissue.

Jonathan P Whiteley¹, David J Gavaghan, S Jonathan Chapman

¹Oxford University Computing Laboratory, Wolfson Building, Parks Road, Oxford OX1 3QD, UK. jonathan.whiteley@comlab.ox.ac.uk

Mathematical Medicine and Biology : a Journal of the IMA

|September 25, 2007

Summary

Simplified models for breast tissue deformation may be inaccurate. This study compares linear and pseudo-non-linear elasticity models against full non-linear elasticity for breast imaging applications.

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Area of Science:

Biomedical Engineering
Computational Mechanics
Medical Imaging

Background:

Accurate modeling of large deformations in breast tissue is crucial for medical imaging techniques like MRI and mammography.
Existing methods often employ linear or pseudo-non-linear elasticity, approximating complex tissue behavior with simpler models.
These approximations may not fully capture the non-linear stress-strain relationships inherent in biological tissues.

Purpose of the Study:

To compare the accuracy of linear and pseudo-non-linear elasticity models against a full non-linear elastic model for breast tissue deformation.
To evaluate the suitability of these simplified models in scenarios like human breast deformation under gravity.
To establish relationships between coefficients across different modeling approaches.

Main Methods:

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Formulation of linear, pseudo-non-linear, and full non-linear elastic models for tissue with exponential stress-strain relationships.
Relating the material coefficients between the different modeling approaches.
Performing numerical simulations on an incompressible material block to analyze deformation behavior.

Main Results:

The study demonstrates that simpler linear and pseudo-non-linear elasticity models may not adequately represent the large deformations of breast tissue.
Numerical simulations highlight potential inaccuracies when these models are applied to scenarios such as breast deformation under gravity.
The comparison reveals significant differences between the approximated and full non-linear solutions.

Conclusions:

The findings suggest that simplified elasticity models may be insufficient for accurate breast tissue deformation modeling in medical imaging.
A full non-linear elastic approach is recommended for scenarios involving significant tissue deformation, such as under gravitational effects.
Further research into advanced constitutive models is warranted for improved biomechanical simulations in breast imaging.