Purpose Breast-conserving surgery (BCS) is the standard of care for early-stage breast cancer, offering recurrence and survival rates comparable to mastectomy while preserving healthy breast tissue. However, surgical cavity healing post-BCS often leads to highly variable tissue remodeling, including scar tissue formation and contracture, leading to visible breast deformation or asymmetry. These outcomes significantly impact patient quality of life but are difficult to predict due to the complex interplay between biological healing processes and individual patient variability. To address this challenge, we extended our previously calibrated computational mechanobiological model of post-BCS healing by incorporating diagnostic imaging data to evaluate how patient-specific breast and tumor characteristics shape healing trajectories and breast deformation.

Methods The model captured multiscale biological and biomechanical processes, including fibroblast activity, collagen remodeling, and nonlinear tissue mechanics, to simulate time-dependent tissue remodeling. Preoperative magnetic resonance imaging (MRI) scans provided patient-specific breast and tumor geometries and characteristics, which were integrated into finite element simulations of cavity healing. Simulation outputs were used to train Gaussian process surrogate models, enabling rapid, accurate prediction of healing dynamics and breast surface deformation across diverse patient profiles.

Results These models revealed how factors including breast density, cavity volume, breast volume, and cavity depth influence post-surgical cavity contraction and measures of breast surface deformation.

Conclusion This framework has the potential to provide a personalized, predictive tool for surgical planning and decision-making, enabling clinicians and patients to anticipate healing trajectories and cosmetic outcomes, with the goal of optimizing surgical results and enhancing patient quality of life.

The authors (ZJH, SLH, ABT) declare that a provisional patent application has been filed related to the computational model and code described in this work.

This work was supported by an NSF CMMI Multiscale Mechanobiology of Growth and Remodeling During Wound Healing grant (A.B.T.; 1911346). Z.H. was a recipient of the Leslie Bottorff Fellowship.

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This study used only openly available de-identified human patient data from the Cancer Imaging Archive https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=70226903

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