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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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\u00a0The 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\u00a0These 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s10439-025-03902-z","type":"journal-article","created":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T18:20:58Z","timestamp":1763058058000},"page":"495-513","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Computational Modeling of Patient-Specific Healing and Deformation Outcomes Following Breast-Conserving Surgery Based on MRI Data"],"prefix":"10.1007","volume":"54","author":[{"given":"Zachary","family":"Harbin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carla","family":"Fisher","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sherry","family":"Voytik-Harbin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8531-0603","authenticated-orcid":false,"given":"Adrian Buganza","family":"Tepole","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,13]]},"reference":[{"issue":"6","key":"3902_CR1","first-page":"477","volume":"74","author":"AN Giaquinto","year":"2024","unstructured":"Giaquinto AN, Sung H, Newman LA, Freedman RA, Smith RA, Star J, Jemal A, Siegel RL (2024) Breast cancer statistics 2024. 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