Computational Modeling of Patient-Specific Healing and Deformation Outcomes Following Breast-Conserving Surgery Based on MRI Data.

Summary

An MRI-informed, multiscale mechanobiological model simulated post-BCS healing and trained surrogate models to rapidly predict breast surface deformation. It identified breast density, cavity volume, breast volume, and cavity depth as key drivers of postoperative contraction and cosmetic asymmetry.

Key Findings

• Integrated preoperative MRI-derived geometries into finite element simulations of post-BCS healing.
• Gaussian process surrogate models enabled rapid prediction of breast surface deformation.
• Breast density, cavity volume, breast volume, and cavity depth were key predictors of contraction and deformation.

Clinical Implications

Can inform surgical planning (incision, cavity management, oncoplastic techniques) and patient counseling by predicting likely deformation trajectories, potentially reducing revision surgeries and improving satisfaction.

Limitations

• Lack of prospective clinical validation correlating predictions with longitudinal measured outcomes.
• Model assumptions may simplify complex wound biology and tissue heterogeneity.

Future Directions

Prospective validation against longitudinal 3D surface imaging; integration of radiotherapy effects and oncoplastic techniques; deployment as a clinical decision-support tool.