Multi-slice DCE-MRI data using P760 distinguishes between metastatic and non-metastatic rodent prostate tumors

Fast Temporal Resolution Dynamic Contrast-Enhanced MRI: Histogram Analysis Versus Visual Analysis for Differentiating Benign and Malignant Breast Lesions

OBJECTIVE

The purpose of this study was to validate a kinetic assessment based on visually identified peak enhancement, which is routinely used in clinical practice, for differentiating benign from malignant lesions during fast dynamic contrast-enhanced MRI.

MATERIALS AND METHODS

Between January 2015 and December 2016, 90 consecutively registered patients with 105 breast lesions (40 benign, 65 malignant) underwent dynamic contrast-enhanced 1.5-T MRI that included one unenhanced and eight contrast-enhanced fast temporal resolution (10 seconds) whole-breast acquisitions. Histogram analysis was performed to measure the voxel-based enhancement of the entire lesion to obtain 90th, 75th, and 50th percentile values at each time point and to generate kinetic curves. Two observers selected visually identified peak enhancement within the lesions to generate the kinetic curves. The kinetic curves from histogram and visually identified peak enhancement analyses were fitted by means of an empiric mathematic model (EMM): ΔS(t) = A × (1 - e^-αt), where A is the upper limit of signal intensity, e indicates the exponential function, and α (min^-1) is the rate of increase in signal intensity. The initial slope of the kinetic curve (A × α) and the initial AUC (AUC₃₀) were calculated. These parameters were compared between benign and malignant lesions, and results from visually identified peak enhancement analysis were compared with those from histogram analysis.

RESULTS

Benign lesions were successfully differentiated from malignant lesions in both visually identified peak enhancement and histogram analyses (90th and 75th percentile values) on the basis of α, A × α, and AUC₃₀ from the EMM. There was no significant difference in ROC AUC in these EMM parameters between visually identified peak enhancement and histogram analyses (p = 0.21).

CONCLUSION

Kinetic assessment with visually identified peak enhancement was acceptable for differentiating benign from malignant lesions.

Dynamic Contrast-Enhanced Magnetic Resonance Imaging as a Pharmacodynamic Biomarker for Pazopanib in Metastatic Renal Carcinoma

INTRODUCTION/BACKGROUND

Traditional imaging assessment criteria might not correlate well with clinical benefit from vascular endothelial growth factor pathway-directed therapy in metastatic renal cancer. Preclinical data suggest tumor growth is preceded by a rise in K^trans level, a parameter derived from dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) that reflects vascular permeability. We thus hypothesized that K^trans might be a predictive biomarker for pazopanib.

PATIENTS AND METHODS

Patients with metastatic renal cancer were treated with pazopanib at 800 mg oral daily until disease progression. MRI of the abdomen and pelvis with a DCE-MRI sequence was obtained at baseline and every 8 weeks.

RESULTS

Seventy-three DCE-MRI scans were completed and 66 were technically assessable. Of the 17 patients with at least 1 DCE-MRI scan after the baseline scan, 16 (94%) had a decline in K^trans level. Changes in K^trans compared with baseline after 1, 8, 16, and 24 weeks were -49%, -65%, -63%, and -53%, respectively (P = .0052, repeated measures analysis of variance). The median K^trans nadir occurred at 8 weeks. The median progression-free survival (PFS) was 32.1 weeks. PFS was longer in patients with higher baseline K^trans values (P = .036, log rank). Baseline K^trans did not reach significance in a Cox proportional hazard model including clinical prognostic index and previous treatments (P = .083).

CONCLUSION

We show that K^trans is a pharmacodynamic biomarker for pazopanib therapy in metastatic renal cancer. Because of the small sample size, the predictive capacity of K^trans recovery could not be assessed, but baseline K^trans correlated with PFS.

Assessment of neovascular permeability in a pancreatic tumor model using dynamic contrast-enhanced (DCE) MRI with contrast agents of different molecular weights

OBJECT

We evaluated the relationship of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-derived pharmacokinetic parameters and contrast agents with different molecular weights (MW) in a pancreatic tumor mouse model.

MATERIALS AND METHODS

Panc02 tumors were induced in mice at the hind leg. DCE-MRI was performed using Gadolinium (Gd)-based contrast agents with different MW: Gd-DOTA (0.5 kDa), P846 (3.5 kDa), and P792 (6.47 kDa). Quantitative vascular parameters (AUC, K(trans), V(e), and V(p)) were calculated according to a modified Tofts two-compartment model. Values for all contrast groups were compared for tumor and control (muscle) tissues.

RESULTS

Values for K(trans) and V(e) were significantly higher in tumor tissue than in muscle tissue. When comparing contrast agents, lowest absolute K(trans) values were observed using P792. The relative increase in K(trans) in tumor tissue compared with normal tissue was highest after the use of P792. In both tumor and normal tissues, K(trans) decreased with increasing molecular weight of the contrast agent used.

CONCLUSION

It was demonstrated that values for the different DCE-MRI vascular (permeability) parameters are highly dependent on the contrast agent used. Due to their potential to better differentiate tumor from muscle tissue, higher molecular weight contrast agents show promise when evaluating tumors using DCE-MRI.

Noninvasive monitoring of therapy-induced microvascular changes in a pancreatic cancer model using dynamic contrast-enhanced magnetic resonance imaging with P846, a new low-diffusible gadolinium-based contrast agent

A predictive technique in the management of patients with cancer could improve the therapeutic index by allowing better individualization of treatment. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a noninvasive technique that can provide anatomical and physiological information on the tumor and its microenvironment. We studied the effect of chemotherapy (gemcitabine), anti-angiogenesis therapy (sunitinib) and radiotherapy on the kinetics of DCE-MRI parameters in a preclinical model of pancreatic cancer using P846, a new low-diffusible contrast agent. Mice underwent DCE-MRI before treatment (MRI1), after 1 week of treatment (MRI2), and after 1 additional week (MRI3). Combined treatment with radiotherapy and sunitinib had a synergistic effect on tumor growth. In radiotherapy/sunitinib-treated mice, a decrease in K(trans) at MRI2 predicted its superior antivascular and antitumor effect at an early time. An increased K(trans) at MRI2, as seen in gemcitabine- and gemcitabine/sunitinib-treated mice, reflects increased permeability for P846 and might predict a smaller therapeutic effect at this early time. This study shows that the kinetics of DCE-MRI parameters depends on the contrast agent used. P846 appears to be a promising low-diffusible agent to monitor therapeutic effects in this preclinical cancer model, but further studies are needed to compare its behavior with Gd-DTPA and macromolecular-weight contrast agents. Sunitinib as a radiosensitizer is promising for future clinical trials in human pancreatic cancer.

A new approach to analysis of the impulse response function (IRF) in dynamic contrast-enhanced MRI (DCEMRI): a simulation study

The purpose of this research was to develop a novel numerical procedure to deconvolute the arterial input function (AIF) from contrast concentration vs. time curves and to obtain the impulse response functions (IRFs) from dynamic contrast-enhanced MRI (DCEMRI) data. Numerical simulations were performed to study variations of contrast concentration vs. time curves and the corresponding IRFs. The simulated contrast media concentration curves were generated by varying the parameters of an empirical mathematical model (EMM) within reasonable ranges based on a previous experimental study. The AIF was calculated from plots of contrast media concentration vs. time in muscle under assumption that they are well approximated by the "two-compartment model" (TCM). A general simple mathematical model of the IRF was developed, and the physiological meaning of the model parameters was determined by comparing them with the widely accepted TCM. The results demonstrate that the deconvolution procedure developed in this research is a simple, robust, and useful technique. In addition, "impulse response analysis" leads to the derivation of novel parameters relating to tumor vascular architecture, and these new parameters may have clinical utility.

Diagnosis of suspicious breast lesions using an empirical mathematical model for dynamic contrast-enhanced MRI

The purpose of this study was to test whether an empirical mathematical model (EMM) of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can distinguish between benign and malignant breast lesions. A modified clinical protocol was used to improve the sampling of contrast medium uptake and washout. T(1)-weighted DCE magnetic resonance images were acquired at 1.5 T for 22 patients before and after injection of Gd-DTPA. Contrast medium concentration as a function of time was calculated over a small region of interest containing the most rapidly enhancing pixels. Then the curves were fitted with the EMM, which accurately described contrast agent uptake and washout. Results demonstrate that benign lesions had uptake (P<2.0 x 10(-5)) and washout (P<.01) rates of contrast agent significantly slower than those of malignant lesions. In addition, secondary diagnostic parameters, such as time to peak of enhancement, enhancement slope at the peak and curvature at the peak of enhancement, were derived mathematically from the EMM and expressed in terms of primary parameters. These diagnostic parameters also effectively differentiated benign from malignant lesions (P<.03). Conventional analysis of contrast medium dynamics, using a subjective classification of contrast medium kinetics in lesions as "washout," "plateau" or "persistent" (sensitivity=83%, specificity=50% and diagnostic accuracy=72%), was less effective than the EMM (sensitivity=100%, specificity=83% and diagnostic accuracy=94%) for the separation of benign and malignant lesions. In summary, the present research suggests that the EMM is a promising alternative method for evaluating DCE-MRI data with improved diagnostic accuracy.