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

## TL;DR

This paper presents a computational model using MRI data to predict healing and deformation after breast-conserving surgery, aiming to improve surgical outcomes and patient quality of life.

## Contribution

The novelty lies in integrating patient-specific MRI data into a mechanobiological model to predict post-surgical healing and deformation.

## Key findings

- The model simulates tissue remodeling by incorporating fibroblast activity and collagen remodeling.
- Factors like breast density and cavity volume significantly influence cavity contraction and deformation.
- Gaussian process models enable rapid prediction of healing dynamics for diverse patient profiles.

## Abstract

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 biologic healing processes and individual patient variability. To address this challenge, we extended our calibrated computational mechanobiological model of post-BCS healing by incorporating diagnostic imaging data to evaluate how patient-specific breast and tumor characteristics influence healing trajectories and deformation.

The model captured multi-scale biologic and biomechanical processes, including fibroblast activity, collagen remodeling, and nonlinear tissue mechanics, to simulate time-dependent tissue remodeling. Patient-specific breast and tumor geometries from preoperative magnetic resonance imaging (MRI) were integrated into finite element simulations of cavity healing, whose outputs trained Gaussian process surrogate models for rapid prediction of healing dynamics and breast surface deformation across diverse patient profiles.

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.

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 online version contains supplementary material available at 10.1007/s10439-025-03902-z.