The purpose of this study is to evaluate perfusion indices and pharmacokinetic parameters in solitary pulmonary nodules (SPNs). Thirty patients of 34 enrolled with SPNs (15-30 mm) were evaluated in this study. T1 and T2-weighted structural images and 2D turbo FLASH perfusion images were acquired with shallow free breathing. B-spline nonrigid image registration and optimization by chi(2) test against pharmacokinetic model curve were performed on dynamic contrast-enhanced MRI. This allowed voxel-by-voxel calculation of k(ep) , the rate constant for tracer transport to and from plasma and the extravascular extracellular space. Mean transit time, time-to-peak, initial slope, and maximum enhancement (E(max) ) were calculated from time-intensity curves fitted to a gamma variate function. After blinded data analysis, correlation with tissue histology from surgical resection or biopsy samples was performed. Histologic evaluation revealed 25 malignant and five benign SPNs. All benign SPNs had k(ep) < 1.0 min(-1) . Nineteen of 25 (76%) malignant SPNs showed k(ep) > 1.0 min(-1) . Sensitivity to diagnose malignant SPNs at a cutoff of k(ep) = 1.0 min(-1) was 76%, specificity was 100%, positive predictive value was 100%, negative predictive value was 45%, and accuracy was 80%. Of all indices studied, k(ep) was the most significant in differentiating malignant from benign SPNs. Magn Reson Med, 2012. (c) 2012 Wiley Periodicals, Inc.
This paper focuses on the analysis of persistence properties of copula-based time series. We obtain theoretical results that demonstrate that Gaussian and Eyraud-Farlie-Gumbel-Mongenstern copulas always produce short memory stationary Markov processes. We further show via simulations that, on the other hand, Clayton copula-based stationary Markov processes can behave as long memory time series on the level of copulas in finite samples exhibiting high persistence important for financial and economic applications. This long memory-like behavior is indicated by a slow decay of copula-based dependence measures between lagged values of the processes for commonly used lag numbers. Application of copula-based Markov processes to volatility modeling captures both non-linear conditional heteroskedasticity as well as long memory-like behavior, thus providing an attractive generalization of GARCH models. Among other conclusions, the results in the paper indicate non-robustness of the copula-level analogues of standard procedures for detecting long-memory on the level of copulas and emphasize the necessity of developing alternative inference methods.