Characterisation of tumour vasculature in mouse brain by USPIO contrast-enhanced MRI

To enhance the success rate of antiangiogenic therapies in the clinic, it is crucial to identify parameters for tumour angiogenesis that can predict response to these therapies. In brain tumours, one such parameter is vascular leakage, which is a response to tumour-derived vascular endothelial growth factor-A and can be measured by Gadolinium-DTPA (Gd-DTPA)-enhanced magnetic resonance imaging (MRI). However, as vascular permeability and angiogenesis are not strictly coupled, tumour blood volume may be another potentially important parameter. In this study, contrast-enhanced MR imaging was performed in three orthotopic mouse models for human brain tumours (angiogenic melanoma metastases and E34 and U87 human glioma xenografts) using both Gd-DTPA to detect vascular leakage and ultrasmall iron oxide particles (USPIO) to measure blood volume. Pixel-by-pixel maps of the enhancement in the transverse relaxation rates (ΔR2 and ΔR2*) after injection of USPIO provided an index proportional to the blood volume of the microvasculature and macrovasculature, respectively, for each tumour. The melanoma metastases were characterised by a blood volume and vessel leakage higher than both glioma xenografts. The U87 glioblastoma xenografts displayed higher permeability and blood volume in the rim than in the core. The E34 glioma xenografts were characterised by a relatively high blood volume, accompanied by only a moderate blood–brain barrier disruption. Delineation of the tumour was best assessed on post-USPIO gradient-echo images. These findings suggest that contrast-enhanced MR imaging using USPIOs and, in particular, ΔR2 and ΔR2* quantitation, provides important additional information about tumour vasculature

Computed Tomography Imaging of Primary Lung Cancer in Mice Using a Liposomal-Iodinated Contrast Agent

To investigate the utility of a liposomal-iodinated nanoparticle contrast agent and computed tomography (CT) imaging for characterization of primary nodules in genetically engineered mouse models of non-small cell lung cancer.Primary lung cancers with mutations in K-ras alone (Kras(LA1)) or in combination with p53 (LSL-Kras(G12D);p53(FL/FL)) were generated. A liposomal-iodine contrast agent containing 120 mg Iodine/mL was administered systemically at a dose of 16 µl/gm body weight. Longitudinal micro-CT imaging with cardio-respiratory gating was performed pre-contrast and at 0 hr, day 3, and day 7 post-contrast administration. CT-derived nodule sizes were used to assess tumor growth. Signal attenuation was measured in individual nodules to study dynamic enhancement of lung nodules.A good correlation was seen between volume and diameter-based assessment of nodules (R(2)>0.8) for both lung cancer models. The LSL-Kras(G12D);p53(FL/FL) model showed rapid growth as demonstrated by systemically higher volume changes compared to the lung nodules in Kras(LA1) mice (p<0.05). Early phase imaging using the nanoparticle contrast agent enabled visualization of nodule blood supply. Delayed-phase imaging demonstrated significant differential signal enhancement in the lung nodules of LSL-Kras(G12D);p53(FL/FL) mice compared to nodules in Kras(LA1) mice (p<0.05) indicating higher uptake and accumulation of the nanoparticle contrast agent in rapidly growing nodules.The nanoparticle iodinated contrast agent enabled visualization of blood supply to the nodules during the early-phase imaging. Delayed-phase imaging enabled characterization of slow growing and rapidly growing nodules based on signal enhancement. The use of this agent could facilitate early detection and diagnosis of pulmonary lesions as well as have implications on treatment response and monitoring

Magnetic resonance imaging (MRI) contrast agents for tumor diagnosis

10.1260/2040-2295.4.1.23Journal of Healthcare Engineering4123-4

Mechanistic modelling of dynamic MRI data predicts that tumour heterogeneity decreases therapeutic response

Mechanistic modelling of dynamic MRI data predicts that tumour heterogeneity decreases therapeutic respons

Gray matter imaging in multiple sclerosis: what have we learned?

At the early onset of the 20th century, several studies already reported that the gray matter was implicated in the histopathology of multiple sclerosis (MS). However, as white matter pathology long received predominant attention in this disease, and histological staining techniques for detecting myelin in the gray matter were suboptimal, it was not until the beginning of the 21st century that the true extent and importance of gray matter pathology in MS was finally recognized. Gray matter damage was shown to be frequent and extensive, and more pronounced in the progressive disease phases. Several studies subsequently demonstrated that the histopathology of gray matter lesions differs from that of white matter lesions. Unfortunately, imaging of pathology in gray matter structures proved to be difficult, especially when using conventional magnetic resonance imaging (MRI) techniques. However, with the recent introduction of several more advanced MRI techniques, the detection of cortical and subcortical damage in MS has considerably improved. This has important consequences for studying the clinical correlates of gray matter damage. In this review, we provide an overview of what has been learned about imaging of gray matter damage in MS, and offer a brief perspective with regards to future developments in this field