Harbin Zachary, Fisher Carla, Voytik-Harbin Sherry, Tepole Adrian Buganza
School of Mechanical Engineering, Purdue University, West Lafayette, IN, USA.
Division of Breast Surgery, Indiana University School of Medicine, Indianapolis, IN, USA.
Ann Biomed Eng. 2025 Nov 13. doi: 10.1007/s10439-025-03902-z.
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.
保乳手术（BCS）是早期乳腺癌的标准治疗方法，其复发率和生存率与乳房切除术相当，同时保留了健康的乳腺组织。然而，BCS术后手术腔愈合常常导致高度可变的组织重塑，包括瘢痕组织形成和挛缩，进而导致明显的乳房变形或不对称。这些结果对患者的生活质量有显著影响，但由于生物愈合过程与个体患者差异之间的复杂相互作用，难以预测。为应对这一挑战，我们通过纳入诊断成像数据扩展了我们校准的BCS术后愈合的计算力学生物学模型，以评估患者特定的乳房和肿瘤特征如何影响愈合轨迹和变形。
该模型捕捉了多尺度的生物和生物力学过程，包括成纤维细胞活性、胶原蛋白重塑和非线性组织力学，以模拟随时间变化的组织重塑。术前磁共振成像（MRI）获得的患者特定乳房和肿瘤几何形状被整合到腔愈合的有限元模拟中，其输出结果训练高斯过程替代模型，以便快速预测不同患者特征下的愈合动态和乳房表面变形。
这些模型揭示了乳房密度、腔体积、乳房体积和腔深度等因素如何影响术后腔收缩和乳房表面变形的测量。
该框架有潜力为手术规划和决策提供个性化的预测工具，使临床医生和患者能够预测愈合轨迹和美容效果，目标是优化手术结果并提高患者生活质量。
