Computational mechanobiology model evaluating healing of postoperative cavities following breast-conserving surgery
Computational mechanobiology model evaluating healing of postoperative cavities following breast-conserving surgery
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DOI：
10.1016/j.compbiomed.2023.107342
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发表时间：
2023-08-28
影响因子：
7.7
通讯作者：
Tepole，Adrian Buganza
中科院分区：
文献类型：
--
作者：
Harbin，Zachary;Sohutskay，David;Tepole，Adrian Buganza
乳腺癌是全世界最常见的癌症类型。考虑到高存活率，人们更加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，但预期的愈合和乳房变形(整形)结果在很大程度上取决于外科医生和患者在BCS和更激进的乳房切除术之间的选择。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术后的手术结果很难预测。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，模拟BCS后的乳房愈合和变形。生物化学-生物力学耦合模型融合了多尺度细胞和组织力学，包括胶原沉积和重塑、胶原依赖的细胞迁移和收缩以及组织塑性变形。可获得的评估空洞收缩的临床数据和来自实验性猪肿瘤切除术研究的组织病理学数据被用于模型校准。通过对生化参数和力学生物学参数的优化，通过高斯过程模拟，成功地将计算模型与数据进行了拟合。然后应用校准后的模型来定义影响愈合和乳房畸形结果的关键力学生物学参数和关系。进一步评估了患者特征的变异性，包括空洞与乳房体积百分比和乳房成分，以确定对空洞收缩和乳房美容结果的影响，模拟结果与之前报道的人类研究很好地一致。建议的模式有可能帮助外科医生和他们的患者制定和讨论个性化的治疗计划，从而导致更令人满意的术后结果和改善的生活质量。
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.