Breast-conserving surgery (BCS; lumpectomy) is a standard treatment for early-stage breast cancer, but postoperative cavity remodeling is complex and varies across patients. Inflammatory and vascular processes are central determinants of healing trajectories and the resulting physical outcomes, yet existing mathematical models of tissue healing often do not capture the coupled interactions between these processes or are not calibrated against experimental data. The study extends a prior computational model of breast cavity healing to explicitly represent the interplay between inflammation and vascular remodeling, with the goal of improving mechanistic understanding and enabling future patient-specific predictions.
The extended computational framework integrates multiple biological processes that drive post-lumpectomy remodeling. Key incorporated components are angiogenesis, oxygen transport, and inflammatory cell activity. These elements are coupled to represent how inflammatory responses and blood-vessel growth influence oxygen availability and tissue remodeling within the surgical cavity. The approach is described as mechanistically grounded, emphasizing process-level representations rather than purely phenomenological fits.
Model calibration was performed using preclinical porcine lumpectomy histology together with relevant data from the literature. The porcine histology served as experimental ground truth for features of cavity remodeling captured in the model, while literature-derived data supplemented and constrained model behavior where direct experimental measurements were not available. The source abstract does not report the specific histological metrics used, sample sizes, or exact literature sources; those details were not provided in the abstract.
To align model predictions with experimental observations and quantify uncertainty, the authors inferred model parameters using a multi-task Gaussian process surrogate within a Bayesian inference framework. The surrogate model approach is intended to accelerate inference by approximating the computational model across multiple tasks or outputs, while the Bayesian formulation provides posterior distributions that reflect parameter uncertainty given the calibration data. The abstract reports this inference strategy but does not list specific parameter estimates or convergence diagnostics.
The calibrated model yields predictions of inflammatory and vascular remodeling dynamics in the breast cavity following lumpectomy, incorporating angiogenesis and oxygen transport as mediators of tissue healing. By using Bayesian posterior distributions and the surrogate model, the framework provides a means to quantify uncertainty in model predictions. The abstract frames these outputs as a foundation for predicting healing trajectories and physical outcomes, though numerical performance metrics, validation against independent datasets, and concrete predictive examples are not reported in the abstract.
By combining mechanistic modeling of inflammation and vascular processes with experimental calibration and uncertainty-aware parameter inference, the work aims to establish a basis for future patient-specific prediction of healing after breast-conserving surgery. Translational potential includes improved ability to anticipate variability in cavity remodeling and to inform interventions that might modify healing trajectories. The abstract positions the framework as a foundational step rather than a finalized clinical tool.
This report appears as a preprint on bioRxiv and has not been certified by peer review. The abstract does not provide exhaustive methodological or numerical details within the preprint listing; specific model equations, parameter values, quantitative validation, or implementation code references are not included in the abstract. The authors disclose that a patent application is pending related to the computational model and associated methods. Additional limitations, performance benchmarks, and external validation would be expected to appear in the full manuscript and during peer review.
The study extends a computational model of post-lumpectomy breast cavity healing by integrating coupled inflammatory and vascular remodeling dynamics, including angiogenesis and oxygen transport, and calibrates the model to preclinical porcine histology and literature data. Parameters were inferred using a multi-task Gaussian process surrogate within a Bayesian inference framework to align predictions with observations and quantify uncertainty. The work offers a mechanistically grounded platform intended to support future patient-specific predictions of healing and physical outcomes after lumpectomy. Specific quantitative results and full methodological detail were not reported in the abstract of the preprint.