Speaker
Description
Hydraulic conductivity (K) is a key transport property governing interstitial fluid movement through the porous tumor microenvironment. Despite its importance, measurement of hydraulic conductivity relies on invasive and technically demanding experiments, mostly performed on non-human tumor tissues. Experimental studies also show substantial intra- and inter-tumoral heterogeneity in K, complicating measurement and highlighting the need for patient-specific characterization in drug delivery models. In this work, we develop a mathematical framework to infer spatially varying K from drug concentration profiles. We first formulate a continuum model describing interstitial fluid flow and drug transport in an isolated tumor nodule representative of peritoneal carcinomatosis treated with intraperitoneal chemotherapy. Finite element simulations show that heterogeneity in hydraulic conductivity substantially alters interstitial fluid pressure and drug distribution. We then derive an explicit analytical inversion formula to reconstruct spatially varying hydraulic conductivity from steady-state concentration profiles. To improve robustness under noisy experimental data, we also implement a parametric inversion approach. The proposed framework provides a theoretical basis for the non-invasive estimation of K from particle distributions and suggests a pathway toward integrating imaging data with mathematical models for quantitative characterization of the tumor microenvironment.
