Dynamic contrast-enhanced MR imaging of carotid atherosclerotic plaque: model selection, reproducibility, and validation.

PURPOSE: To compare four known pharmacokinetic models for their ability to describe dynamic contrast material-enhanced magnetic resonance (MR) imaging of carotid atherosclerotic plaques, to determine reproducibility, and to validate the results with histologic findings. MATERIALS AND METHODS: The study was approved by the institutional medical ethics committee. Written informed consent was obtained from all patients. Forty-five patients with 30%-99% carotid stenosis underwent dynamic contrast-enhanced MR imaging. Plaque enhancement was measured at 16 time points at approximately 25-second image intervals by using a gadolinium-based contrast material. Pharmacokinetic parameters (volume transfer constant, K(trans); extracellular extravascular volume fraction, v(e); and blood plasma fraction, v(p)) were determined by fitting a two-compartment model to plaque and blood gadolinium concentration curves. The relative fit errors and parameter uncertainties were determined to find the most suitable model. Sixteen patients underwent imaging twice to determine reproducibility. Carotid endarterectomy specimens from 16 patients who were scheduled for surgery were collected for histologic validation. Parameter uncertainties were compared with the Wilcoxon signed rank test. Reproducibility was assessed by using the coefficient of variation. Correlation with histologic findings was evaluated with the Pearson correlation coefficient. RESULTS: The mean relative fit uncertainty (±standard error) for K(trans) was 10% ± 1 with the Patlak model, which was significantly lower than that with the Tofts (20% ± 1), extended Tofts (33% ± 3), and extended graphical (29% ± 3) models (P < .001). The relative uncertainty for v(p) was 20% ± 2 with the Patlak model and was significantly higher with the extended Tofts (46% ± 9) and extended graphical (35% ± 5) models (P < .001). The reproducibility (coefficient of variation) for the Patlak model was 16% for K(trans) and 26% for v(p). Significant positive correlations were found between K(trans) and the endothelial microvessel content determined on histologic slices (Pearson ρ = 0.72, P = .005). CONCLUSION: The Patlak model is most suited for describing carotid plaque enhancement. Correlation with histologic findings validated K(trans) as an indicator of plaque microvasculature, and the reproducibility of K(trans) was good.

Synergy between anti-CCL2 and docetaxel as determined by DW-MRI in a metastatic bone cancer model.

Metastatic prostate cancer continues to be the second leading cause of cancer death in American men with an estimated 28,660 deaths in 2008. Recently, monocyte chemoattractant protein-1 (MCP-1, CCL2) has been identified as an important factor in the regulation of prostate metastasis. CCL2, shown to attract macrophages to the tumor site, has a direct promotional effect on tumor cell proliferation, migration, and survival. Previous studies have shown that anti-CCL2 antibodies given in combination with docetaxel were able to induce tumor regression in a pre-clinical prostate cancer model. A limitation for evaluating new treatments for metastatic prostate cancer to bone is the inability of imaging to objectively assess response to treatment. Diffusion-weighted MRI (DW-MRI) assesses response to anticancer therapies by quantifying the random (i.e., Brownian) motion of water molecules within the tumor mass, thus identifying cells undergoing apoptosis. We sought to measure the treatment response of prostate cancer in an osseous site to docetaxel, an anti-CCL2 agent, and combination treatments using DW-MRI. Measurements of tumor apparent diffusion coefficient (ADC) values were accomplished over time during a 14-day treatment period and compared to response as measured by bioluminescence imaging and survival studies. The diffusion data provided early predictive evidence of the most effective therapy, with survival data results correlating with the DW-MRI findings. DW-MRI is under active investigation in the pre-clinical and clinical settings to provide a sensitive and quantifiable means for early assessment of cancer treatment outcome.