Predicting red blood cell transport and capillary hemodynamics in angiogenic and tumor vascular networks in silico.

Blood flow through capillary vessels in cancerous tissue plays a crucial role in disease progression and treatment. Unlike the microvasculature in healthy tissue, which is hierarchically well organized, a cancerous tissue is characterized by chaotic organization. A detailed quantification of blood cell transport and capillary hemodynamics in tumor microvasculature is lacking. Specifically, the relationship between tumor vascular geometric abnormalities, the dynamics of flowing blood cells, and the resulting blood flow anomalies at the vasculature scale is unknown. To fill this knowledge gap, we utilize a high-fidelity computational model of the flow of a deformable red blood cell (RBC) suspension through angiogenic and tumor microvasculatures in silico, built from in vivo/ex vivo images. We provide detailed quantitative distinctions between the healthy, angiogenic, and tumor microcirculation by predicting hemodynamic parameters that are difficult to experimentally measure but physiologically significant. These include the shape and dynamics of individual RBCs, the Fahraeus effect, blood viscosity, wall shear stress, and a 3D mapping of the RBC-depleted region near the vascular surface. This study opens a new avenue for studying tumor microcirculation using high-fidelity computational modeling to reveal novel microcirculatory phenomena in silico.