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Not all tumor vessels are equal. Tumor-associated vasculature includes immature vessels, regressing vessels, transport vessels undergoing arteriogenesis and peritumor vessels influenced by tumor growth factors. Current techniques for analyzing tumor blood flow do not discriminate between vessel subtypes and only measure average changes from a population of dissimilar vessels. We developed methodologies for simultaneously quantifying blood flow (velocity, flux, hematocrit and shear rate) in extended networks at single-capillary resolution in vivo. Our approach relies on deconvolution of signals produced by labeled red blood cells as they move relative to the scanning laser of a confocal or multiphoton microscope and provides fully resolved three-dimensional flow profiles within vessel networks. Using this methodology, we show that blood velocity profiles are asymmetric near intussusceptive tissue structures in tumors in mice. Furthermore, we show that subpopulations of vessels, classified by functional parameters, exist in and around a tumor and in normal brain tissue.

Background: Recent clinical trials of VEGF inhibitors have shown promise in the treatment of recurrent glioblastomas (GBM). However, the survival benefit is usually short-lived as tumors escape anti-VEGF therapies. Here we tested the hypothesis that Platelet Derived Growth Factor-C (PDGF-C), an isoform of the PDGF family, affects GBM progression independent of VEGF pathway and hinders anti-VEGF therapy. Principal Findings: We first showed that PDGF-C is present in human GBMs. Then, we overexpressed or downregulated PDGF-C in a human GBM cell line, U87MG, and grew them in cranial windows in nude mice to assess vessel structure and function using intravital microscopy. PDGF-C overexpressing tumors had smaller vessel diameters and lower vascular permeability compared to the parental or siRNA-transfected tumors. Furthermore, vessels in PDGF-C overexpressing tumors had more extensive coverage with NG2 positive perivascular cells and a thicker collagen IV basement membrane than the controls. Treatment with DC101, an anti-VEGFR-2 antibody, induced decreases in vessel density in the parental tumors, but had no effect on the PDGF-C overexpressing tumors. Conclusion: These results suggest that PDGF-C plays an important role in glioma vessel maturation and stabilization, and that it can attenuate the response to anti-VEGF therapy, potentially contributing to escape from vascular normalization.

Normalization of tumor vasculature is an emerging strategy to improve cytotoxic therapies. Here we show that eliminating nitric oxide (NO) production from tumor cells via neuronal NO synthase silencing or inhibition establishes perivascular gradients of NO in human glioma xenografts in mice and normalizes the tumor vasculature, resulting in improved tumor oxygenation and response to radiation treatment. Creation of perivascular NO gradients may be an effective strategy for normalizing abnormal vasculature.

This paper presents methods to model complex vasculature in three-dimensional (3-D) images using cylindroidal superellipsoids, along with robust estimation and detection algorithms for automated image analysis. This model offers an explicit, low-order parameterization, enabling joint estimation of boundary, centerlines, and local pose. It provides a geometric framework for directed vessel traversal, and extraction of topological information like branch point locations and connectivity. M-estimators provide robust region-based statistics that are used to drive the superellipsoid toward a vessel boundary. A robust likelihood ratio test is used to differentiate between noise, artifacts, and other complex unmodeled structures, thereby verifying the model estimate. The proposed methodology behaves well across scale-space, shows a high degree of insensitivity to adjacent structures and implicitly handles branching. When evaluated on synthetic imagery mimicking specific structural complexities in tumor microvasculature, it consistently produces ubvoxel accuracy estimates of centerlines and widths in the presence of closely-adjacent vessels, branch points, and noise. An edit-based validation demonstrated a precision level of 96.6% at a recall level of 95.4%. Overall, it is robust enough for large-scale application.

This paper presents model-based information-theoretic methods to quantify the complexity of tumor microvasculature, taking into account shape, textural, and structural irregularities. The proposed techniques are completely automated, and are applicable to optical slices (3-D) or projection images (2-D). Improvements upon the prior literature include: (i) measuring local (vessel segment) as well as global (entire image) vascular complexity without requiring explicit segmentation or tracing; (ii) focusing on the vessel boundaries in the complexity estimate; and (iii) added robustness to image artifacts common to tumor microvasculature images. Vessels are modeled using a family of super-Gaussian functions that are based on the superquadric modeling primitive common in computer vision. The superquadric generalizes a simple ellipsoid by including shape parameters that allow it to approximate a cylinder with elliptical cross-sections (generalized cylinder). The super-Gaussian is obtained by composing a superquadric with an exponential function giving a form that is similar to a standard Gaussian function but with the ability to produce level sets that approximate generalized cylinders. Importantly, the super-Gaussian is continuous and differentiable so it can be fit to image data using robust non-linear regression. This fitting enables quantification of the intrinsic complexity of vessel data vis-a-vis the super-Gaussian model within a minimum message length (MML) framework. The resulting measures are expressed in units of information (bits). Synthetic and real-data examples are provided to illustrate the proposed measures.