A quantitative comparison of the influence of individual versus population-derived vascular input functions on dynamic contrast enhanced-MRI in small animals

For quantitative analysis of DCE-MRI data, the time course of the concentration of the contrast agent in the blood plasma, or vascular input function (VIF), is required. We compared pharmacokinetic parameters derived using individual and population-based VIFs in mice for two different contrast agents, Gd-DTPA and P846. Eleven mice with subcutaneous 4T1 breast cancer xenografts were imaged at 7T. A pre-contrast T(1) map was acquired along with dynamic T(1)-weighted gradient echo images before, during, and after a bolus injection of contrast agent delivered via a syringe pump. Each animal's individual VIF (VIF(ind)) and derived population-averaged VIF (VIF(pop)) were used to extract parameters from the signal-time curves of tumor tissue at both the region of interest (ROI) and voxel level. The results indicate that for both contrast agents, K(trans) values estimated using VIF(pop) have a high correlation (CCC > 0.85) with K(trans) values estimated using VIF(ind) on both an ROI and voxel level. This work supports the validity of using of a population-based VIF with a stringent injection protocol in pre-clinical DCE-MRI studies

Evaluation of dynamic contrast-enhanced MRI biomarkers for stratified cancer medicine: How do permeability and perfusion vary between human tumours?

Solid tumours exhibit enhanced vessel permeability and fenestrated endothelium to varying degree, but it is unknown how this varies in patients between and within tumour types. Dynamic contrast-enhanced (DCE) MRI provides a measure of perfusion and permeability, the transfer constant Ktrans, which could be employed for such comparisons in patients.To test the hypothesis that different tumour types exhibit systematically different Ktrans.DCE-MRI data were retrieved from 342 solid tumours in 230 patients. These data were from 18 previous studies, each of which had had a different analysis protocol. All data were reanalysed using a standardised workflow using an extended Tofts model. A model of the posterior density of median Ktrans was built assuming a log-normal distribution and fitting a simple Bayesian hierarchical model.12 histological tumour types were included. In glioma, median Ktrans was 0.016min-1 and for non-glioma tumours, median Ktrans ranged from 0.10 (cervical) to 0.21min-1 (prostate metastatic to bone). The geometric mean (95% CI) across all the non-glioma tumours was 0.15 (0.05, 0.45)min-1. There was insufficient separation between the posterior densities to be able to predict the Ktrans value of a tumour given the tumour type, except that the median Ktrans for gliomas was below 0.05min-1 with 80% probability, and median Ktrans measurements for the remaining tumour types were between 0.05 and 0.4min-1 with 80% probability.With the exception of glioma, our hypothesis that different tumour types exhibit different Ktrans was not supported. Studies in which tumour permeability is believed to affect outcome should not simply seek tumour types thought to exhibit high permeability. Instead, Ktrans is an idiopathic parameter, and, where permeability is important, Ktrans should be measured in each tumour to personalise that treatment