Accuracy and Precision of Iodine Quantification in Subtracted Micro-Computed Tomography: Effect of Reconstruction and Noise Removal Algorithms.

PURPOSE: To evaluate the effect of reconstruction and noise removal algorithms on the accuracy and precision of iodine concentration (CI) quantified with subtracted micro-computed tomography (micro-CT). PROCEDURES: Two reconstruction algorithms were evaluated: a filtered backprojection (FBP) algorithm and a simultaneous iterative reconstruction technique (SIRT) algorithm. A 3D bilateral filter (BF) was used for noise removal. A phantom study evaluated and compared the image quality, and the accuracy and precision of CI in four scenarios: filtered FBP, filtered SIRT, non-filtered FBP, and non-filtered SIRT. In vivo experiments were performed in an animal model of chemically-induced mammary cancer. RESULTS: Linear relationships between the measured and nominal CI values were found for all the scenarios in the phantom study (R2 > 0.95). SIRT significantly improved the accuracy and precision of CI compared to FBP, as given by their lower bias (adj. p-value = 0.0308) and repeatability coefficient (adj. p-value < 0.0001). Noise removal enabled a significant decrease in bias in filtered SIRT images only; non-significant differences were found for the repeatability coefficient. The phantom and in vivo studies showed that CI is a reproducible imaging parameter for all the scenarios (Pearson r > 0.99, p-value < 0.001). The contrast-to-noise ratio showed non-significant differences among the evaluated scenarios in the phantom study, while a significant improvement was found in the in vivo study when SIRT and BF algorithms were used. CONCLUSIONS: SIRT and BF algorithms improved the accuracy and precision of CI compared to FBP and non-filtered images, which encourages their use in subtracted micro-CT imaging.

Quantitative Imaging Parameters of Contrast-Enhanced Micro-Computed Tomography Correlate with Angiogenesis and Necrosis in a Subcutaneous C6 Glioma Model.

The aim of this work was to systematically obtain quantitative imaging parameters with static and dynamic contrast-enhanced (CE) X-ray imaging techniques and to evaluate their correlation with histological biomarkers of angiogenesis in a subcutaneous C6 glioma model. Enhancement (E), iodine concentration (CI), and relative blood volume (rBV) were quantified from single- and dual-energy (SE and DE, respectively) micro-computed tomography (micro-CT) images, while rBV and volume transfer constant (Ktrans) were quantified from dynamic contrast-enhanced (DCE) planar images. CI and rBV allowed a better discernment of tumor regions from muscle than E in SE and DE images, while no significant differences were found for rBV and Ktrans in DCE images. An agreement was found in rBV for muscle quantified with the different imaging protocols, and in CI and E quantified with SE and DE protocols. Significant strong correlations (Pearson r > 0.7, p < 0.05) were found between a set of imaging parameters in SE images and histological biomarkers: E and CI in tumor periphery were associated with microvessel density (MVD) and necrosis, E and CI in the complete tumor with MVD, and rBV in the tumor periphery with MVD. In conclusion, quantitative imaging parameters obtained in SE micro-CT images could be used to characterize angiogenesis and necrosis in the subcutaneous C6 glioma model.

Mechanisms of Vasculogenic Mimicry in Ovarian Cancer.

Solid tumors carry out the formation of new vessels providing blood supply for growth, tumor maintenance, and metastasis. Several processes take place during tumor vascularization. In angiogenesis, new vessels are derived from endothelial cells of pre-existing vessels; while in vasculogenesis, new vessels are formed de novo from endothelial progenitor cells, creating an abnormal, immature, and disorganized vascular network. Moreover, highly aggressive tumor cells form structures similar to vessels, providing a pathway for perfusion; this process is named vasculogenic mimicry (VM), where vessel-like channels mimic the function of vessels and transport plasma and blood cells. VM is developed by numerous types of aggressive tumors, including ovarian carcinoma which is the second most common cause of death among gynecological cancers. VM has been associated with poor patient outcome and survival in ovarian cancer, although the involved mechanisms are still under investigation. Several signaling molecules have an important role in VM in ovarian cancer, by regulating the expression of genes related to vascular, embryogenic, and hypoxic signaling pathways. In this review, we provide an overview of the current knowledge of the signaling molecules involved in the promotion and regulation of VM in ovarian cancer. The clinical implications and the potential benefit of identification and targeting of VM related molecules for ovarian cancer treatment are also discussed.