School of Mechanical Engineering Purdue University, West Lafayette, IN, USA.
Weldon School of Biomedical Engineering Purdue University, West Lafayette, IN, USA; Indiana University School of Medicine, Indianapolis, IN, USA.
Comput Biol Med. 2023 Oct;165:107342. doi: 10.1016/j.compbiomed.2023.107342. Epub 2023 Aug 18.
Breast cancer is the most commonly diagnosed cancer type worldwide. Given high survivorship, increased focus has been placed on long-term treatment outcomes and patient quality of life. While breast-conserving surgery (BCS) is the preferred treatment strategy for early-stage breast cancer, anticipated healing and breast deformation (cosmetic) outcomes weigh heavily on surgeon and patient selection between BCS and more aggressive mastectomy procedures. Unfortunately, surgical outcomes following BCS are difficult to predict, owing to the complexity of the tissue repair process and significant patient-to-patient variability. To overcome this challenge, we developed a predictive computational mechanobiological model that simulates breast healing and deformation following BCS. The coupled biochemical-biomechanical model incorporates multi-scale cell and tissue mechanics, including collagen deposition and remodeling, collagen-dependent cell migration and contractility, and tissue plastic deformation. Available human clinical data evaluating cavity contraction and histopathological data from an experimental porcine lumpectomy study were used for model calibration. The computational model was successfully fit to data by optimizing biochemical and mechanobiological parameters through Gaussian process surrogates. The calibrated model was then applied to define key mechanobiological parameters and relationships influencing healing and breast deformation outcomes. Variability in patient characteristics including cavity-to-breast volume percentage and breast composition were further evaluated to determine effects on cavity contraction and breast cosmetic outcomes, with simulation outcomes aligning well with previously reported human studies. The proposed model has the potential to assist surgeons and their patients in developing and discussing individualized treatment plans that lead to more satisfying post-surgical outcomes and improved quality of life.
乳腺癌是全球最常见的癌症类型。由于生存率较高,人们越来越关注长期治疗结果和患者的生活质量。虽然保乳手术(BCS)是早期乳腺癌的首选治疗策略,但 BCS 和更激进的乳房切除术之间的选择,很大程度上取决于外科医生和患者对预期愈合和乳房变形(美容)结果的重视。不幸的是,由于组织修复过程的复杂性和患者之间的显著差异,BCS 后的手术结果难以预测。为了克服这一挑战,我们开发了一种预测性计算机械生物学模型,该模型模拟了 BCS 后的乳房愈合和变形。该耦合的生化-力学模型纳入了多尺度的细胞和组织力学,包括胶原蛋白的沉积和重塑、胶原蛋白依赖性细胞迁移和收缩性,以及组织的塑性变形。我们使用了评估腔室收缩的可用人类临床数据和来自实验性猪乳房切除术研究的组织病理学数据来校准模型。通过高斯过程代理优化生化和机械生物学参数,成功地使计算模型拟合数据。然后,将校准后的模型应用于定义影响愈合和乳房变形结果的关键机械生物学参数和关系。进一步评估了患者特征的变异性,包括腔室与乳房体积百分比和乳房组成,以确定它们对腔室收缩和乳房美容结果的影响,模拟结果与先前报道的人类研究一致。该模型有可能帮助外科医生及其患者制定和讨论个性化的治疗计划,从而获得更满意的术后结果和提高生活质量。