Biology subjects

Fisher, C. S.

Publications and source records attributed to Fisher, C. S..

3 recordsLinked to original sources

bioRxiv2026

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.

systems biology↗

bioRxiv2026

Capillary Network Generation Framework for Estimating Volumetric Capillary Density from Histological Vascular Measurements

Angiogenesis drives the formation and remodeling of capillary networks throughout tissue repair, regulating the vascular environment that supports healing and tissue remodeling. Experimental characterization of these processes is commonly performed using CD31-stained histological tissue sections to quantify capillary surface density and morphology throughout healing. However, these measurements provide only two-dimensional characterization of an underlying three-dimensional (3D) vascular network, limiting direct estimation of volumetric capillary density and vascular architecture. To address this limitation, an experimentally informed framework was developed to generate representative 3D capillary networks, enabling estimation of volumetric capillary density from histologically quantified vascular measurements. CD31-stained histological sections obtained from a longitudinal porcine lumpectomy study were analyzed to quantify the percentage of CD31-positive area (%CD31+) and capillary morphology within healthy tissue and healing surgical cavities. Histologically quantified morphology distributions and literature-informed vascular branching characteristics were incorporated into a capillary network generation framework to construct representative 3D vascular networks. Capillary branches were iteratively generated within representative tissue volumes until virtual histological sections reproduced experimental %CD31+ measurements, enabling estimation of volumetric capillary density. Generated capillary networks demonstrated good agreement with experimentally characterized 3D vascular architecture, while simulated histological sections accurately reproduced experimentally quantified capillary counts and vascularization measurements. Application of the framework to the porcine lumpectomy dataset captured temporal changes in vascular remodeling throughout healing, revealing progressive increases in volumetric capillary density and vascular maturation. Collectively, this framework provides an experimentally informed methodology for relating histological vascular measurements to volumetric capillary density estimates, supporting future computational studies of angiogenesis and tissue repair.

bioengineering↗

bioRxiv2023

Computational Mechanobiology Model Evaluating Healing of Postoperative Cavities Following Breast-Conserving Surgery

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 the Gaussian Process. 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.

bioengineering↗