scholarly journals Structural and Practical Identifiability of Dual-input Kinetic Modeling in Dynamic PET of Liver Inflammation

2018 ◽  
Author(s):  
Yang Zuo ◽  
Souvik Sarkar ◽  
Michael T. Corwin ◽  
Kristin Olson ◽  
Ramsey D. Badawi ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Kinetic Model ◽  
Portal Vein ◽  
Kinetic Modeling ◽  
Input Function ◽  
Model Parameters ◽  
Dynamic Pet ◽  
Liver Inflammation ◽  
Practical Identifiability

AbstractDynamic 18F-FDG PET with tracer kinetic modeling has the potential to noninvasively evaluate human liver inflammation using the FDG blood-to-tissue transport rate K1. Accurate kinetic modeling of dynamic liver PET data and K1 quantification requires the knowledge of dual-blood input function from the hepatic artery and portal vein. While the arterial input function can be derived from the aortic region on dynamic PET images, it is difficult to extract the portal vein input function accurately from PET. The optimization-derived dual-input kinetic modeling approach has been proposed to overcome this problem by jointly estimating the portal vein input function and FDG tracer kinetics from time activity curve fitting. In this paper, we further characterize the model properties by analyzing the structural identifiability of the model parameters using the Laplace transform and practical identifiability using Monte Carlo simulation based on fourteen patient datasets. The theoretical analysis has indicated that all the kinetic parameters of the dual-input kinetic model are structurally identifiable, though subject to local solutions. The Monte Carlo simulation results have shown that FDG K1 can be estimated reliably in the whole-liver region of interest with reasonable bias, standard deviation, and high correlation between estimated and original values, indicating of practical identifiability of K1. The result has also demonstrated the correlation between K1 and histological liver inflammation scores is reliable. FDG K1 quantification by the optimization-derived dual-input kinetic model is promising for assessing liver inflammation.

scholarly journals Structural and practical identifiability of dual-input kinetic modeling in dynamic PET of liver inflammation

2019 ◽  
Vol 64 (17) ◽  
pp. 175023 ◽  
Author(s):  
Yang Zuo ◽  
Souvik Sarkar ◽  
Michael T Corwin ◽  
Kristin Olson ◽  
Ramsey D Badawi ◽  
...  
Keyword(s):  
Kinetic Modeling ◽  
Dynamic Pet ◽  
Liver Inflammation ◽  
Practical Identifiability

scholarly journals Dynamic PET of human liver inflammation: impact of kinetic modeling with optimization-derived dual-blood input function

2018 ◽  
Vol 63 (15) ◽  
pp. 155004 ◽  
Author(s):  
Guobao Wang ◽  
Michael T Corwin ◽  
Kristin A Olson ◽  
Ramsey D Badawi ◽  
Souvik Sarkar
Keyword(s):  
Kinetic Modeling ◽  
Human Liver ◽  
Input Function ◽  
Dynamic Pet ◽  
Liver Inflammation

scholarly journals The McBurr XII and Log-McBurr XII Models with Applications to Lifetime Data

2015 ◽  
Vol 5 (1) ◽  
pp. 1 ◽  
Author(s):  
Gauss M. Cordeiro ◽  
Edwin M. M. Ortega ◽  
G. G. Hamedani ◽  
Diogo A. Garcia
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Regression Models ◽  
Real Data ◽  
Model Parameters ◽  
Lifetime Data ◽  
Fatigue Lifetime ◽  
Burr Xii Distribution ◽  
New Models ◽  
Lifetime Model

A new six-parameter extended fatigue lifetime model named the McDonald-Burr XII distribution is introduced, which generalizes the Burr XII, beta Burr XII (Parana\'iba {\it et al.}, 2011) and Kumaraswamy-Burr XII (Parana\'iba {\it et al.}, 2012) distributions. The proposed distribution is characterized in terms of truncated moments. We obtain the ordinary and incomplete moments and quantile and ge\-ne\-ra\-ting functions. We estimate the model parameters by maximum likelihood. A Monte Carlo simulation is performed to study the asymptotic normality of the estimates. We also propose an extended regression model based on the logarithm of the McDonald-Burr XII distribution that can be more realistic in the analysis of real data than other special regression models. Two applications to real data validate the importance of the new models.

scholarly journals Dynamic PET of Human Liver Inflammation: Impact of Kinetic Modeling with Optimization-Derived Dual-Blood Input Function

2018 ◽  
Author(s):  
Guobao Wang ◽  
Michael T. Corwin ◽  
Kristin A. Olson ◽  
Ramsey D. Badawi ◽  
Souvik Sarkar
Keyword(s):  
Kinetic Analysis ◽  
Kinetic Modeling ◽  
Fdg Pet ◽  
Human Liver ◽  
Input Function ◽  
Population Based ◽  
Joint Estimation ◽  
Human Studies ◽  
Liver Inflammation ◽  
Time Activity

ABSTRACTThe hallmark of nonalcoholic steatohepatitis is hepatocellular inflammation and injury in the setting of hepatic steatosis. Recent work has indicated that dynamic 18F-FDG PET with kinetic modeling has the potential to assess hepatic inflammation noninvasively, while static FDG-PET did not show a promise. Because the liver has dual blood supplies, kinetic modeling of dynamic liver PET data is challenging in human studies. This paper aims to identify the optimal dual-input kinetic modeling approach for dynamic FDG-PET of human liver inflammation. Fourteen patients with nonalcoholic fatty liver disease were included. Each patient underwent 1-hour dynamic FDG-PET/CT scan and had liver biopsy within six weeks. Three models were tested for kinetic analysis: traditional two-tissue compartmental model with an image-derived single-blood input function (SBIF), model with population-based dual-blood input function (DBIF), and new model with optimization-derived DBIF through a joint estimation framework. The three models were compared using Akaike information criterion (AIC), F test and histopathologic inflammation score. Results showed that the optimization-derived DBIF model improved liver time activity curve fitting and achieved lower AIC values and higher F values than the SBIF and population-based DBIF models in all patients. The optimization-derived model significantly increased FDG K1 estimates by 101% and 27% as compared with traditional SBIF and population-based DBIF. K1 by the optimization-derived model was significantly associated with histopathologic grades of liver inflammation while the other two models did not provide a statistical significance. In conclusion, modeling of DBIF is critical for dynamic liver FDG-PET kinetic analysis in human studies. The optimization-derived DBIF model is more appropriate than SBIF and population-based DBIF for dynamic FDG-PET of liver inflammation.

Variation of Beremin Model Parameters With Temperature by Monte Carlo Simulation

2019 ◽  
Vol 141 (2) ◽  
Author(s):  
K. Bhattacharyya ◽  
S. Acharyya ◽  
S. Dhar ◽  
J. Chattopadhyay
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Master Curve ◽  
Model Parameters ◽  
Three Point Bending ◽  
Brittle Transition ◽  
Different Temperatures ◽  
Ductile To Brittle Transition ◽  
Vessel Material ◽  
Reactor Pressure

In this work, variation of the Beremin parameters with temperature for reactor pressure vessel material 20MnMoNi55 steel is studied. Beremin model is used, including the effect of plastic strain as originally formulated in the Beremin model. A set of six tests are performed at a temperature of −110 °C in order to determine reference temperature (T0) and master curve for the entire ductile-to-brittle transition (DBT) region as per the ASTM Standard E1921. Monte Carlo simulation is employed to produce a large number of 1 T three-point bending specimen (TPB) fracture toughness data randomly drawn from the scatter band obtained from the master curve, at different temperatures of interest in the brittle dominated portion of DBT region to determine Beremin model parameters variation with temperatures.

scholarly journals Cost-effectiveness of adding Sativex® spray to spasticity care in Belgium: using bootstrapping instead of Monte Carlo simulation for probabilistic sensitivity analyses

2021 ◽  
Author(s):  
Mark Oppe ◽  
Daniela Ortín-Sulbarán ◽  
Carlos Vila Silván ◽  
Anabel Estévez-Carrillo ◽  
Juan M. Ramos-Goñi
Keyword(s):  
Clinical Trial ◽  
Sensitivity Analysis ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Probabilistic Sensitivity Analysis ◽  
Standard Care ◽  
Clinical Trial Data ◽  
Cost Utility ◽  
Trial Data ◽  
Model Parameters

Abstract Background Uncertainty in model-based cost-utility analyses is commonly assessed in a probabilistic sensitivity analysis. Model parameters are implemented as distributions and values are sampled from these distributions in a Monte Carlo simulation. Bootstrapping is an alternative method that requires fewer assumptions and incorporates correlations between model parameters. Methods A Markov model-based cost–utility analysis comparing oromucosal spray containing delta-9-tetrahidrocannabinol + cannabidiol (Sativex®, nabiximols) plus standard care versus standard spasticity care alone in the management of multiple sclerosis spasticity was performed over a 5-year time horizon from the Belgian healthcare payer perspective. The probabilistic sensitivity analysis was implemented using a bootstrap approach to ensure that the correlations present in the source clinical trial data were incorporated in the uncertainty estimates. Results Adding Sativex® spray to standard care was found to dominate standard spasticity care alone, with cost savings of €6,068 and a quality-adjusted life year gain of 0.145 per patient over the 5-year analysis. The probability of dominance increased from 29% in the first year to 94% in the fifth year, with the probability of QALY gains in excess of 99% for all years considered. Conclusions Adding Sativex® spray to spasticity care was found to dominate standard spasticity care alone in the Belgian healthcare setting. This study showed the use of bootstrapping techniques in a Markov model probabilistic sensitivity analysis instead of Monte Carlo simulations. Bootstrapping avoided the need to make distributional assumptions and allowed the incorporation of correlating structures present in the original clinical trial data in the uncertainty assessment.

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