Experimentally calibrated computational modeling of inflammation and vascular remodeling in breast cavity healing following breast-conserving surgery

Breast-conserving surgery (BCS; lumpectomy) is widely used to treat early-stage breast cancer, yet the complexity and patient-specific variability of postoperative cavity remodeling make healing trajectories and physical outcomes difficult to predict. Although inflammatory and vascular processes are central to these outcomes, mathematical models of tissue healing do not capture their coupled interactions or calibrate them against experimental data. Here, we extend a computational model of breast cavity healing to incorporate coupled inflammatory and vascular dynamics, including angiogenesis, oxygen transport, and inflammatory cell activity. The model is calibrated using preclinical porcine lumpectomy histology and literature data. Model parameters are inferred using a multi-task Gaussian process surrogate within a Bayesian inference frame-work to align predictions with experimental observations and quantify uncertainty. Thus, this work provides a mechanistically grounded framework for inflammatory and vascular remodeling during breast cavity healing and provides a foundation for patient-specific prediction of healing and physical outcomes following lumpectomy.