Kinetic Curve Type Assessment for Classification of Breast Lesions Using Dynamic Contrast-Enhanced MR Imaging

<div><p>Objective</p><p>The aim of this study was to employ a kinetic model with dynamic contrast enhancement-magnetic resonance imaging to develop an approach that can efficiently distinguish malignant from benign lesions.</p><p>Materials and Methods</p><p>A total of 43 patients with 46 lesions who underwent breast dynamic contrast enhancement-magnetic resonance imaging were included in this retrospective study. The distribution of malignant to benign lesions was 31/15 based on histological results. This study integrated a single-compartment kinetic model and dynamic contrast enhancement-magnetic resonance imaging to generate a kinetic modeling curve for improving the accuracy of diagnosis of breast lesions. Kinetic modeling curves of all different lesions were analyzed by three experienced radiologists and classified into one of three given types. Receiver operating characteristic and Kappa statistics were used for the qualitative method. The findings of the three radiologists based on the time-signal intensity curve and the kinetic curve were compared.</p><p>Results</p><p>An average sensitivity of 82%, a specificity of 65%, an area under the receiver operating characteristic curve of 0.76, and a positive predictive value of 82% and negative predictive value of 63% was shown with the kinetic model (p = 0.017, 0.052, 0.068), as compared to an average sensitivity of 80%, a specificity of 55%, an area under the receiver operating characteristic of 0.69, and a positive predictive value of 79% and negative predictive value of 57% with the time-signal intensity curve method (p = 0.003, 0.004, 0.008). The diagnostic consistency of the three radiologists was shown by the κ-value, 0.857 (p<0.001) with the method based on the time-signal intensity curve and 0.826 (p<0.001) with the method of the kinetic model.</p><p>Conclusions</p><p>According to the statistic results based on the 46 lesions, the kinetic modeling curve method showed higher sensitivity, specificity, positive and negative predictive values as compared with the time-signal intensity curve method in lesion classification.</p></div

Percentages of benign and malignant Type 1 (persistent enhancing), Type 2 (plateau) and Type 3 (washout) lesions diagnosed by three radiologists based on the time-signal intensity curve and the kinetic curve.

<p>Percentages of benign and malignant Type 1 (persistent enhancing), Type 2 (plateau) and Type 3 (washout) lesions diagnosed by three radiologists based on the time-signal intensity curve and the kinetic curve.</p

Application of the time-signal intensity curve and the kinetic curve in breast MRI analysis.

<p>(a) MRI of a 58-year-old female showed an invasive ductal carcinoma on the upper outer quadrant of the left breast. The green circle indicates the region of interest (ROI) that was used for estimating the change in the C1 value, and the red circle represents the ROI for measuring the ΔM. (b) The blue line is the time-signal intensity curve, and the orange line was generated from a plot of the C2 values to form a kinetic modeling curve.</p

Comparison of the area under the ROC obtained by three radiologists using two different methods.

<p>Comparison of the area under the ROC obtained by three radiologists using two different methods.</p

The findings of the breast lesions from three radiologists based on the time-signal intensity curve and the kinetic curve.

<p>The sensitivity (SN), specificity (SP), area under the receiver operating characteristic curve (AUC), positive predictive value (PPV), negative predictive value (NPV), and kappa (κ) value are presented.</p

Influence of substrates on the <i>in vitro</i> kinetics of steviol glucuronidation and interaction between steviol glycosides metabolites and UGT2B7

<p>Steviol glycosides, a natural sweetener, may perform bioactivities via steviol, their main metabolite in human digestion. The metabolising kinetics, i.e. glucuronidation kinetics and interaction between steviol glycosides or their metabolites and metabolising enzyme, are important for understanding the bioactivity and cytotoxicity. The present study investigated kinetics of steviol glucuronidation in human liver microsome and a recombinant human UDP-glucuronosyltransferases isomer, UGT2B7, along with molecular docking to analyse interaction between UGT2B7 and steviol or glucose. The active pocket of UGT2B7 is consisted of Arg352, Leu347, Lys343, Phe339, Tyr354, Lys355 and Leu353. The influence of stevioside, rebaudioside A, glucose and some chemotherapy reagents on the glucuronidation was also studied. The predicted hepatic clearence suggested that steviol could be classified as high-clearence drug. The steviol glycosides did not affect the glucuronidation of steviol notably.</p