Multi-parametric assessment of the anti-angiogenic effects of liposomal glucocorticoids

Inflammation plays a prominent role in tumor growth. Anti-inflammatory drugs have therefore been proposed as anti-cancer therapeutics. In this study, we determined the anti-angiogenic activity of a single dose of liposomal prednisolone phosphate (PLP-L), by monitoring tumor vascular function and viability over a period of one week. C57BL/6 mice were inoculated subcutaneously with B16F10 melanoma cells. Six animals were PLP-L-treated and six served as control. Tumor tissue and vascular function were probed using MRI before and at three timepoints after treatment. DCE-MRI was used to determine Ktrans, ve, time-to-peak, initial slope and the fraction of non-enhancing pixels, complemented with immunohistochemistry. The apparent diffusion coefficient (ADC), T2 and tumor size were assessed with MRI as well. PLP-L treatment resulted in smaller tumors and caused a significant drop in Ktrans 48 h post-treatment, which was maintained until one week after drug administration. However, this effect was not sufficient to significantly distinguish treated from non-treated animals. The therapy did not affect tumor tissue viability but did prevent the ADC decrease observed in the control group. No evidence for PLP-L-induced tumor vessel normalization was found on histology. Treatment with PLP-L altered tumor vascular function. This effect did not fully explain the tumor growth inhibition, suggesting a broader spectrum of PLP-L activities

Impact of the arterial input function on the classification of contrast-agent uptake curves in dynamic contrast-enhanced (DCE) MR images based on heuristic shape modeling

Purpose: To demonstrate that inter-patient differences in the spreading of the contrast agent throughout the blood, described by the arterial input function (AIF), should be considered in the classification of contrast-agent uptake curves in the tissue of interest, e.g., a suspicious lesion in the breast. In the application of heuristic shape modeling (Kuhl et al. 1999, three-time-point (3TP) by Weinstein et al. 1999), the AIF is not extracted from the DCE-MR image series and, therefore, not taken into account.Methods and Materials: A two-compartment model (extended Kety) with fixed pharmacokinetic parameters is used to simulate the tissue-response curves for different AIFs. The shape of these curves is classified into benign, suspicious or malignant by means of the 3TP method.Results: While AIFs are known to differ in a wide range, our simulations indicate that already small changes of the AIF considerably alter the shape of the response curve. These changes may even lead to different curve classifications, although the simulated response curves relate to ’tissue’ with fixed pharmacokinetic properties. Conclusion: The shape of contrast-agent uptake curves expressed by simulated tissue with fixed pharmacokinetic properties can get classified differently in different patients owing to inter-patient variations of the AIF. Evaluation of the influence of variation in AIFs in real patient data is work in progress. Validation of our observation with real patient data might suggest that deconvolution with patient-specific AIFs, as it is done in pharmacokinetic modeling, improves reliability of tissue classification derived from the shape of contrast-agent uptake curves

Pharmacokinetic analysis of dynamic contrast-enhanced (DCE) MR breast images

DCE-MR breast imaging is becoming an important modality as a problem-solving tool and in screening of high-risk patients. In DCE-MR a T1-weighted time series is recorded during the uptake of a contrast agent (CA: Gd-DTPA) that enables the assessment of voxel-specific dynamic tissue properties. Tumor malignancy often goes hand in hand with high vascularity and high vessel wall permeability because of angiogenesis; i.e. a large number of new vessels is formed at such a rate that their walls are not properly constructed and therefore highly permeable. These 'malignant' tissue characteristics affect the shape of the CA-uptake curve. The goal is to investigate the use of a pharmacokinetic two-compartment model (extended Kety [1]) in the context of dynamic analysis of CA-uptake curves as a contribution to automatic detection and characterization of breast cancer. The equations: ve*(dCe(t)/dt)=Ktrans(Cp(t)-Ce(t)) and Ct(t)=vp*Cp(t)+ve*Ce(t)describe the model. Ktrans = volume transfer constant [min-1], vc = cell fraction [-], ve = extravascular, extracellular fraction [-] (EES), vp = blood plasma fraction [-], vbc = blood cell fraction [-]. Ct / Cp / Ce = tissue / blood plasma / EES concentration CA [mM]. However, the models that are currently dominating the clinical practice are 'heuristic shape models' such as Kuhl's model [2] and the Three-Time-Point (3TP) model [3]. These models classify the shape of the CA-uptake curves into three categories: (1) persistent uptake (benign), (2) plateau uptake in the intermediate to late phase (suspicious), and (3) washout in the late to intermediate phase (malignant). This type of classification is based on clinical experience and statistics supported by biopsy-proven data. One of our aims is to uncover the limitations of these shape models and to investigate if two-compartment modeling can lead to a better, i.e. physiology-based, tissue classification