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Computational Modeling of Patient-Specific Healing and Deformation Outcomes Following Breast-Conserving Surgery Based
Zachary Harbin1, Carla Fisher2, Sherry Voytik-Harbin3,4
1School of Mechanical Engineering, Purdue University, West Lafayette, IN, USA.
Annals of Biomedical Engineering
|November 13, 2025
Summary
Computational models predict breast deformation after breast-conserving surgery (BCS). Integrating patient imaging data helps anticipate healing and cosmetic outcomes, improving quality of life for cancer patients.
Area of Science:
- Biomedical Engineering
- Computational Biology
- Oncology
Background:
- Breast-conserving surgery (BCS) is standard for early breast cancer, but healing causes variable tissue remodeling, impacting aesthetics and quality of life.
- Predicting post-BCS breast deformation is challenging due to complex biological and patient-specific factors.
Purpose of the Study:
- To enhance a computational mechanobiological model of BCS healing by integrating patient-specific imaging data.
- To evaluate how individual breast and tumor characteristics influence healing and deformation after BCS.
Main Methods:
- Developed a multi-scale model simulating fibroblast activity, collagen remodeling, and nonlinear tissue mechanics.
- Integrated patient-specific geometries from MRI into finite element simulations.
- Trained Gaussian process surrogate models for rapid prediction of healing dynamics and surface deformation.
Main Results:
- Identified key factors influencing cavity contraction and breast surface deformation, including breast density, cavity volume, breast volume, and cavity depth.
- Demonstrated the model's ability to predict healing trajectories and cosmetic outcomes across diverse patient profiles.
Conclusions:
- The developed framework offers a personalized, predictive tool for surgical planning in BCS.
- Aims to optimize surgical results and enhance patient quality of life by anticipating healing and cosmetic outcomes.
