Ultra-early changes in vascular parameters from dynamic contrast enhanced MRI of breast cancer xenografts following systemic therapy with doxorubicin and liver X receptor agonist

Abstract

BackgroundDynamic contrast enhanced magnetic resonance imaging (DCE-MRI) may be used to depict tumour vascular structure and for therapy response assessment in various tumour sites. The purpose of the current work is to examine whether ultra-early changes in tumour physiology following cytotoxic treatment with doxorubicin and liver X receptor (LXR) agonist GW3965 are detectable by DCE-MRI.Methods36 female, athymic nude foxn1nu mice with bilaterally implanted breast cancer xenografts (17 with ER-positive HBCx34, 19 with triple-negative HBCx39) were randomised in the following treatment groups; control, GW3965 (40 mg/kg p.o.), doxorubicin (8 mg/kg i.v.) and a combination therapy of GW3965 and doxorubicin. DCE-MRI (3D FLASH on a 7 T preclinical scanner) was performed at baseline and one and six days after onset of treatment. Wash-in (30 s p.i.) and wash-out (300 s p.i.) enhancement were quantified from dynamic uptake curves, before voxel-by-voxel fitting to the pharmacokinetic Tofts model and generation of maps for the resulting parameters Ktrans, νe and νB. Treatment effect was evaluated by univariate repeated measures mixed-effects maximum likelihood regression models applied to median tumour data.ResultsWe found no effects of any treatment 24 h post treatment. After 6 days, doxorubicin given as both mono- and combination therapy gave significant increases of ~ 30% in wash-in enhancement (p < 0.011) and Ktrans (p < 0.017), and 40–50% in νB (p < 0.024) for HBCx34, but not for HBCx39. No effects of GW3965 were observed at any time (p > 0.1).ConclusionsTwenty-four h after onset of treatment was too early to evaluate treatment effects by DCE-MRI. Early enhancement and Ktrans were approximately equally sensitive metrics to capture treatment effects six days pt. Pharmacokinetic modelling however allowed us to attribute the observed effect to changes in tumour perfusion rather than increased retention.

Artificial Intelligence Algorithm in Classification and Recognition of Primary Hepatic Carcinoma Images under Magnetic Resonance Imaging

“…Because the morphological basis of tumor tissue growth and infiltration lies in blood vessels, the new tumor tissues and blood vessels can provide nutrients. If the blood vessels are immature, their permeability is higher; the contrast agent injected in DCE-MRI examinations has a small molecular weight and is easy to exudate from blood vessels into the extracellular space [ 23 , 24 ]. It was found in this research that the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of MRI diagnosis processed by the bias field correction algorithm were 93.63%, 71.62%, 95.63%, 71.62%, and 90.01%, respectively.…”

The potential of advanced MR techniques for precision radiotherapy of glioblastoma

“…By measuring the time course of the contrast agent as it diffuses from the blood pool into tissue, information on flow and permeability of the vasculature can be obtained. DCE-MR imaging can be used to study the degree of vascular leakiness and has great potential to monitor changes in vascular permeability arising from antiangiogenic therapies [ 35 ].…”

Dynamic Contrast-Enhanced MRI in the Abdomen of Mice with High Temporal and Spatial Resolution Using Stack-of-Stars Sampling and KWIC Reconstruction

“…As such, magnetization in the blood pool does not achieve a steady state before traversing the imaging slab, as is assumed in the modeling. A variety of approaches have been proposed for addressing this limitation, including: use of a fixed parametric model for the AIF [ 15 ], use of a population average AIF [ 16 , 17 ], observation of the AIF in the left ventricular blood pool [ 18 ], shunting blood to an external catheter in which the AIF is accurately measured [ 19 ] and the reference tissue method [ 20 , 21 , 22 ]. Of these, the reference tissue method has the advantage of providing individualized data directly from the dynamic data without explicitly measuring the AIF.…”