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Dual-input tracer kinetic modeling of dynamic contrast-enhanced MRI in thoracic malignancies.

ABSTRACT: Pulmonary perfusion with dynamic contrast-enhanced (DCE-) MRI is typically assessed using a single-input tracer kinetic model. Preliminary studies based on perfusion CT are indicating that dual-input perfusion modeling of lung tumors may be clinically valuable as lung tumors have a dual blood supply from the pulmonary and aortic system. This study aimed to investigate the feasibility of fitting dual-input tracer kinetic models to DCE-MRI datasets of thoracic malignancies, including malignant pleural mesothelioma (MPM) and nonsmall cell lung cancer (NSCLC), by comparing them to single-input (pulmonary or systemic arterial input) tracer kinetic models for the voxel-level analysis within the tumor with respect to goodness-of-fit statistics. Fifteen patients (five MPM, ten NSCLC) underwent DCE-MRI prior to radiotherapy. DCE-MRI data were analyzed using five different single- or dual-input tracer kinetic models: Tofts-Kety (TK), extended TK (ETK), two compartment exchange (2CX), adiabatic approximation to the tissue homogeneity (AATH) and distributed parameter (DP) models. The pulmonary blood flow (BF), blood volume (BV), mean transit time (MTT), permeability-surface area product (PS), fractional interstitial volume (v_I ), and volume transfer constant (K^Trans ) were calculated for both single- and dual-input models. The pulmonary arterial flow fraction (?), pulmonary arterial blood flow (BF_PA ) and systemic arterial blood flow (BF_A ) were additionally calculated for only dual-input models. The competing models were ranked and their Akaike weights were calculated for each voxel according to corrected Akaike information criterion (cAIC). The optimal model was chosen based on the lowest cAIC value. In both types of tumors, all five dual-input models yielded lower cAIC values than their corresponding single-input models. The 2CX model was the best-fitted model and most optimal in describing tracer kinetic behavior to assess microvascular properties in both MPM and NSCLC. The dual-input 2CX-model-derived BF_A was the most significant parameter in differentiating adenocarcinoma from squamous cell carcinoma histology for NSCLC patients.

SUBMITTER: Lee SH

PROVIDER: S-EPMC6839367 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Dual-input tracer kinetic modeling of dynamic contrast-enhanced MRI in thoracic malignancies.

Lee Sang Ho SH Rimner Andreas A Deasy Joseph O JO Hunt Margie A MA Tyagi Neelam N

Journal of applied clinical medical physics 20191011 11

Pulmonary perfusion with dynamic contrast-enhanced (DCE-) MRI is typically assessed using a single-input tracer kinetic model. Preliminary studies based on perfusion CT are indicating that dual-input perfusion modeling of lung tumors may be clinically valuable as lung tumors have a dual blood supply from the pulmonary and aortic system. This study aimed to investigate the feasibility of fitting dual-input tracer kinetic models to DCE-MRI datasets of thoracic malignancies, including malignant ple ...[more]

PMID: 31602789

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Project description:BackgroundStable Isotope Resolved Metabolomics (SIRM) is a new biological approach that uses stable isotope tracers such as uniformly [Formula: see text]-enriched glucose ([Formula: see text]-Glc) to trace metabolic pathways or networks at the atomic level in complex biological systems. Non-steady-state kinetic modeling based on SIRM data uses sets of simultaneous ordinary differential equations (ODEs) to quantitatively characterize the dynamic behavior of metabolic networks. It has been increasingly used to understand the regulation of normal metabolism and dysregulation in the development of diseases. However, fitting a kinetic model is challenging because there are usually multiple sets of parameter values that fit the data equally well, especially for large-scale kinetic models. In addition, there is a lack of statistically rigorous methods to compare kinetic model parameters between different experimental groups.ResultsWe propose a new Bayesian statistical framework to enhance parameter estimation and hypothesis testing for non-steady-state kinetic modeling of SIRM data. For estimating kinetic model parameters, we leverage the prior distribution not only to allow incorporation of experts' knowledge but also to provide robust parameter estimation. We also introduce a shrinkage approach for borrowing information across the ensemble of metabolites to stably estimate the variance of an individual isotopomer. In addition, we use a component-wise adaptive Metropolis algorithm with delayed rejection to perform efficient Monte Carlo sampling of the posterior distribution over high-dimensional parameter space. For comparing kinetic model parameters between experimental groups, we propose a new reparameterization method that converts the complex hypothesis testing problem into a more tractable parameter estimation problem. We also propose an inference procedure based on credible interval and credible value. Our method is freely available for academic use at https://github.com/xuzhang0131/MCMCFlux .ConclusionsOur new Bayesian framework provides robust estimation of kinetic model parameters and enables rigorous comparison of model parameters between experimental groups. Simulation studies and application to a lung cancer study demonstrate that our framework performs well for non-steady-state kinetic modeling of SIRM data.

Dataset Information

Dual-input tracer kinetic modeling of dynamic contrast-enhanced MRI in thoracic malignancies.

Publications

Dual-input tracer kinetic modeling of dynamic contrast-enhanced MRI in thoracic malignancies.

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