Evaluation of a phosphate kinetics model in hemodialysis therapy-Assessment of the temporal robustness of model predictions

In-depth understanding of intra- and postdialytic phosphate kinetics is important to adjust treatment regimens in hemodialysis. We aimed to modify and validate a three-compartment phosphate kinetic model to individual patient data and assess the temporal robustness. Intradialytic phosphate samples were collected from the plasma and dialysate of 12 patients during two treatments (HD1 and HD2). 2-h postdialytic plasma samples were collected in four of the patients. First, the model was fitted to HD1 samples from each patient to estimate the mass transfer coefficients. Second, the best fitted model in each patient case was validated on HD2 samples. The best model fits were determined from the coefficient of determination (R2 ) values. When fitted to intradialytic samples only, the median (interquartile range) R2 values were 0.985 (0.959-0.997) and 0.992 (0.984-0.994) for HD1 and HD2, respectively. When fitted to both intra- and postdialytic samples, the results were 0.882 (0.838-0.929) and 0.963 (0.951-0.976) for HD1 and HD2, respectively. Eight patients demonstrated a higher R2 value for HD2 than for HD1. The model seems promising to predict individual plasma phosphate in hemodialysis patients. The results also show good temporal robustness of the model. Further modifications and validation on a larger sample are needed.

Fractal Kinetic Implementation in Population Pharmacokinetic Modeling

Compartment modeling is a widely accepted technique in the field of pharmacokinetic analysis. However, conventional compartment modeling is performed under a homogeneity assumption that is not a naturally occurring condition. Since the assumption lacks physiological considerations, the respective modeling approach has been questioned, as novel drugs are increasingly characterized by physiological or physical features. Alternative approaches have focused on fractal kinetics, but evaluations of their application are lacking. Thus, in this study, a simulation was performed to identify desirable fractal-kinetics applications in conventional modeling. Visible changes in the profiles were then investigated. Five cases of finalized population models were collected for implementation. For model diagnosis, the objective function value (OFV), Akaike's information criterion (AIC), and corrected Akaike's information criterion (AICc) were used as performance metrics, and the goodness of fit (GOF), visual predictive check (VPC), and normalized prediction distribution error (NPDE) were used as visual diagnostics. In most cases, model performance was enhanced by the fractal rate, as shown in a simulation study. The necessary parameters of the fractal rate in the model varied and were successfully estimated between 0 and 1. GOF, VPC, and NPDE diagnostics show that models with the fractal rate described the data well and were robust. In the simulation study, the fractal absorption process was, therefore, chosen for testing. In the estimation study, the rate application yielded improved performance and good prediction-observation agreement in early sampling points, and did not cause a large shift in the original estimation results. Thus, the fractal rate yielded explainable parameters by setting only the heterogeneity exponent, which reflects true physiological behavior well. This approach can be expected to provide useful insights in pharmacological decision making.

Estimation of fractional myocardial blood volume and water exchange using ferumoxytol-enhanced magnetic resonance imaging

Fractional myocardial blood volume (fMBV) estimated using ferumoxytol-enhanced magnetic resonance imaging (MRI) (FE-MRI) has the potential to capture a hemodynamic response to myocardial hypoperfusion during contrast steady state without reliance on gadolinium chelates. Ferumoxytol has a long intravascular half-life and its use for steady-state MRI is off-label. The aim of this prospective study was to optimize and evaluate a two-compartment model for estimation of fMBV based on FE-MRI. Nine healthy swine and one swine with artificially induced single-vessel coronary stenosis underwent MRI on a 3.0 T clinical magnet. Myocardial longitudinal spin-lattice relaxation rate (R1) was measured using the 5(3)3(3)3 modified Look-Locker inversion recovery (MOLLI) sequence before and at contrast steady state following seven ferumoxytol infusions (0.125-4.0 mg/kg). fMBV and water exchange were estimated using a two-compartment model. Model-fitted fMBV was compared to simple fast-exchange fMBV approximation and percent change in pre- and postferumoxytol R1. Dose undersampling schemes were investigated to reduce acquisition duration. Variation in fMBV was assessed using one-way analysis of variance. Fast-exchange fMBV and ferumoxytol dose undersampling were evaluated using Bland-Altman analysis. Healthy normal swine showed a mean mid-ventricular fMBV of 7.2 ± 1.4% and water exchange rate of 11.3 ± 5.1 s-1 . There was intersubject variation in fMBV (p < 0.05) without segmental variation (p = 0.387). fMBV derived from eight-dose and four-dose sampling schemes had no significant bias (mean difference = 0.07, p = 0.541, limits of agreement -1.04% [-1.45, -0.62%] to 1.18% [0.77, 1.59%]). Pixel-wise fMBV in one swine model with coronary artery stenosis showed elevated fMBV in ischemic segments (apical anterior: 11.90 ± 4.00%, apical septum: 16.10 ± 5.71%) relative to remote segments (apical inferior: 9.59 ± 3.35%, apical lateral: 9.38 ± 2.35%). A two-compartment model based on FE-MRI using the MOLLI sequence may enable estimation of fMBV in studies of ischemic heart disease. LEVEL OF EVIDENCE: 2. TECHNICAL EFFICACY STAGE: 2.

Quantitative Dynamic 18F-FDG PET/CT in Survival Prediction of Metastatic Melanoma under PD-1 Inhibitors

Simple Summary

The reliable and early during-the-course-of-treatment assessment of tumor response to the novel immunotherapeutic agents is a matter of debate, posing relevant challenges to conventional imaging modalities. In this prospective study, including 25 metastatic melanoma patients, we explored the prognostic significance of quantitative, dynamic ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) performed early during programmed cell death protein 1 (PD-1) blockade. At a median follow-up of 24.2 months, several semiquantitative and quantitative PET/CT parameters derived from tumor lesions and reference tissues had an impact on progression-free survival (PFS). In particular, ¹⁸F-FDG standardized uptake value (SUV_mean, SUV_max) and fractal dimension (FD) of melanoma lesions adversely affected PFS, while FD of the thyroid, as well as SUV_max and k₃ of the bone marrow, positively affected PFS. These findings underline the potential predictive role of quantitative, dynamic, interim PET/CT—performed in combination with conventional, static, whole-body PET/CT—in metastatic melanoma patients under PD-1 blockade.

Abstract

The advent of novel immune checkpoint inhibitors has led to unprecedented survival rates in advanced melanoma. At the same time, it has raised relevant challenges in the interpretation of treatment response by conventional imaging approaches. In the present prospective study, we explored the predictive role of quantitative, dynamic ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) performed early during immunotherapy in metastatic melanoma patients receiving treatment with programmed cell death protein 1 (PD-1) inhibitors. Twenty-five patients under PD-1 blockade underwent dynamic and static ¹⁸F-FDG PET/CT before the start of treatment (baseline PET/CT) and after the initial two cycles of therapy (interim PET/CT). The impact of semiquantitatively (standardized uptake value, SUV) and quantitatively (based on compartment modeling and fractal analysis) derived PET/CT parameters, both from melanoma lesions and different reference tissues, on progression-free survival (PFS) was analyzed. At a median follow-up of 24.2 months, survival analysis revealed that the interim PET/CT parameters SUV_mean, SUV_max and fractal dimension (FD) of the hottest melanoma lesions adversely affected PFS, while the parameters FD of the thyroid, as well as SUV_max and k₃ of the bone marrow positively affected PFS. The herein presented findings highlight the potential predictive role of quantitative, dynamic, interim PET/CT in metastatic melanoma under PD-1 blockade. Therefore, dynamic PET/CT could be performed in selected oncological cases in combination with static, whole-body PET/CT in order to enhance the diagnostic certainty offered by conventional imaging and yield additional information regarding specific molecular and pathophysiological mechanisms involved in tumor biology and response to treatment.

Task-based evaluation of fluorescent-guided cancer surgery as a means of identifying optimal imaging agent properties in the context of variability in tumor- and healthy-tissue physiology

Fluorescent molecular-guided surgery (FGS) is at a tipping point in terms of clinical approval and adoption in a number cancer applications, with ongoing phase 0 and phase 1 clinical trials being carried out in a wide range of cancers using a wide range of agents. The pharmacokinetics of each of these agents and the physiology of these cancers can differ vastly on a patient-to-patient basis, bringing to question: how can one fairly compare different methodologies (defined as the combination of imaging agent, system, and protocol) and how can existing methodologies be further optimized? To this point, little methodology comparison has been carried out, and the majority of FGS optimization has concerned system development-on the level of maximizing signal-to-noise, dynamic detection range, and sensitivity-independently from traditional agent development-in terms of fluorophore brightness, toxicity, solubility, and binding affinity and specificity. Here we propose an inclusion of tumor and healthy tissue physiology (blood flow, vascular permeability, specific and nonspecific binding sites, extracellular matrix, interstitial pressure, etc…) variability into the optimization process and re-establish well-described task-based metrics for methodology optimization and comparing quality of one methodology to another. Two salient conclusions were identified: (1) contrast-to-background variability is a simple metric that correlates with difficult-to-carry-out task-based metrics for comparing methodologies, and (2) paired-agent imaging protocols offer unique advantages over single-imaging-agent studies for mitigating confounding tumor and background physiology variability.

Effect of nonspecific binding of imaging agents to plasma protein in the paired-agent imaging for resection during surgery (PAIRS)

Long-term survival of head and neck squamous cell carcinoma (HNSCC) patients have proven to be correlated with negative surgical margins. Paired-Agent Imaging for Resection during Surgery (PAIRS) is capable of drawing the fine line between tumor and normal tissue by employing a control imaging-agent, which is co-administered with the targeted imaging agent to account for nonspecific signal. PAI is highly dependent on the parallel paired-agent delivery and static quantum yield of the agent to trace the molecular concentration. However, it is well known that nonspecific binding of fluorescence probes to plasma proteins can change its delivery, dissociation constant, and quantum yield. A thorough evaluation of the effect of plasma protein binding in the estimation of receptor concentration was performed for the paired-agents in this study. We are planning to evaluate ABY-029, an anti-epithelial growth factor receptor (EGFR) Affibody, and IRDye 700DX as a control agent. The plasma-dependent change in fluorescence intensity, percent binding, and in vivo distribution kinetics will be studied for each agent alone, and in combination. In this proceeding, the absorption, emission patterns for the targeted agent, ABY-029, measured by UV-Vis, fluorometer, and Pearl were shown. Initial studies indicate that binding to Bovine serum albumin (BSA), human serum albumin (HSA) and EGFR can introduce the Solvatochromic shift, which will change the absorption and emission pattern for ABY-029. Computational modeling will be performed to determine how each of these changes will affect the determined BP, and thus detection of tumors from normal tissue.

Diffusion-relaxation correlation spectroscopic imaging: A multidimensional approach for probing microstructure

PURPOSE

To propose and evaluate a novel multidimensional approach for imaging subvoxel tissue compartments called Diffusion-Relaxation Correlation Spectroscopic Imaging.

THEORY AND METHODS

Multiexponential modeling of MR diffusion or relaxation data is commonly used to infer the many different microscopic tissue compartments that contribute signal to macroscopic MR imaging voxels. However, multiexponential estimation is known to be difficult and ill-posed. Observing that this ill-posedness is theoretically reduced in higher dimensions, diffusion-relaxation correlation spectroscopic imaging uses a novel multidimensional imaging experiment that jointly encodes diffusion and relaxation information, and then uses a novel constrained reconstruction technique to generate a multidimensional diffusion-relaxation correlation spectrum for every voxel. The peaks of the multidimensional spectrum are expected to correspond to the distinct tissue microenvironments that are present within each macroscopic imaging voxel.

RESULTS

Using numerical simulations, experiment data from a custom-built phantom, and experiment data from a mouse model of traumatic spinal cord injury, diffusion-relaxation correlation spectroscopic imaging is demonstrated to provide substantially better multicompartment resolving power compared to conventional diffusion- and relaxation-based methods.

CONCLUSION

The diffusion-relaxation correlation spectroscopic imaging approach provides powerful new capabilities for resolving the different components of multicompartment tissue models, and can be leveraged to significantly expand the insights provided by MRI in studies of tissue microstructure. Magn Reson Med 78:2236-2249, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

18 F-fluoromisonidazole uptake in advanced stage non-small cell lung cancer: A voxel-by-voxel PET kinetics study

PURPOSE

The aim of this study was to determine the relative abilities of compartment models to describe time-courses of 18 F-fluoromisonidazole (FMISO) uptake in tumor voxels of patients with non-small cell lung cancer (NSCLC) imaged using dynamic positron emission tomography. Also to use fits of the best-performing model to investigate changes in fitted rate-constants with distance from the tumor edge.

METHODS

Reversible and irreversible two- and three-tissue compartment models were fitted to 24 662 individual voxel time activity curves (TACs) obtained from tumors in nine patients, each imaged twice. Descriptions of the TACs provided by the models were compared using the Akaike and Bayesian information criteria (AIC and BIC). Two different models (two- and three-tissue) were fitted to 30 measured voxel TACs to provide ground-truth TACs for a statistical simulation study. Appropriately scaled noise was added to each of the resulting ground-truth TACs, generating 1000 simulated noisy TACs for each ground-truth TAC. The simulation study was carried out to provide estimates of the accuracy and precision with which parameter values are determined, the estimates being obtained for both assumptions about the ground-truth kinetics. A BIC clustering technique was used to group the fitted rate-constants, taking into consideration the underlying uncertainties on the fitted rate-constants. Voxels were also categorized according to their distance from the tumor edge.

RESULTS

For uptake time-courses of individual voxels an irreversible two-tissue compartment model was found to be most precise. The simulation study indicated that this model had a one standard deviation precision of 39% for tumor fractional blood volumes and 37% for the FMISO binding rate-constant. Weighted means of fitted FMISO binding rate-constants of voxels in all tumors rose significantly with increasing distance from the tumor edge, whereas fitted fractional blood volumes fell significantly. When grouped using the BIC clustering, many centrally located voxels had high-fitted FMISO binding rate-constants and low rate-constants for tracer flow between the vasculature and tumor, both indicative of hypoxia. Nevertheless, many of these voxels had tumor-to-blood (TBR) values lower than the 1.4 level commonly expected for hypoxic tissues, possibly due to the low rate-constants for tracer flow between the vasculature and tumor cells in these voxels.

CONCLUSIONS

Time-courses of FMISO uptake in NSCLC tumor voxels are best analyzed using an irreversible two-tissue compartment model, fits of which provide more precise parameter values than those of a three-tissue model. Changes in fitted model parameter values indicate that levels of hypoxia rise with increasing distance from tumor edges. The average FMISO binding rate-constant is higher for voxels in tumor centers than in the next tumor layer out, but the average value of the more simplistic TBR metric is lower in tumor centers. For both metrics, higher values might be considered indicative of hypoxia, and the mismatch in this case is likely to be due to poor perfusion at the tumor center. Kinetics analysis of dynamic PET images may therefore provide more accurate measures of the hypoxic status of such regions than the simpler TBR metric, a hypothesis we are presently exploring in a study of tumor imaging versus histopathology.

Application of separable parameter space techniques to multi-tracer PET compartment modeling

Multi-tracer positron emission tomography (PET) can image two or more tracers in a single scan, characterizing multiple aspects of biological functions to provide new insights into many diseases. The technique uses dynamic imaging, resulting in time-activity curves that contain contributions from each tracer present. The process of separating and recovering separate images and/or imaging measures for each tracer requires the application of kinetic constraints, which are most commonly applied by fitting parallel compartment models for all tracers. Such multi-tracer compartment modeling presents challenging nonlinear fits in multiple dimensions. This work extends separable parameter space kinetic modeling techniques, previously developed for fitting single-tracer compartment models, to fitting multi-tracer compartment models. The multi-tracer compartment model solution equations were reformulated to maximally separate the linear and nonlinear aspects of the fitting problem, and separable least-squares techniques were applied to effectively reduce the dimensionality of the nonlinear fit. The benefits of the approach are then explored through a number of illustrative examples, including characterization of separable parameter space multi-tracer objective functions and demonstration of exhaustive search fits which guarantee the true global minimum to within arbitrary search precision. Iterative gradient-descent algorithms using Levenberg-Marquardt were also tested, demonstrating improved fitting speed and robustness as compared to corresponding fits using conventional model formulations. The proposed technique overcomes many of the challenges in fitting simultaneous multi-tracer PET compartment models.

A Monte Carlo study of the dependence of early frame sampling on uncertainty and bias in pharmacokinetic parameters from dynamic PET

Compartmental modeling of dynamic PET data enables quantification of tracer kinetics in vivo, through the calculated model parameters. In this study, we aimed to investigate the effect of early frame sampling and reconstruction method on pharmacokinetic parameters obtained from a 2-tissue model, in terms of bias and uncertainty (SD).

METHODS

The GATE Monte Carlo software was used to simulate 2 × 15 dynamic 3'-deoxy-3'-(18)F-fluorothymidine ((18)F-FLT) brain PET studies, typical in terms of noise level and kinetic parameters. The data were reconstructed by both 3-dimensional (3D) filtered backprojection with reprojection (3DRP) and 3D ordered-subset expectation maximization (OSEM) into 6 dynamic image sets with different early frame durations of 1, 2, 4, 6, 10, and 15 s. Bias and SD were evaluated for fitted parameter estimates, calculated from regions of interest.

RESULTS

The 2-tissue-model parameter estimates K1, k2, and fraction of arterial blood in tissue depended on early frame sampling, and a sampling of 6-15 s generally minimized bias and SD. The shortest sampling of 1 s yielded a 25% and 42% larger bias than the other schemes, for 3DRP and OSEM, respectively, and a parameter uncertainty that was 10%-70% higher. The schemes from 4 to 15 s were generally not significantly different in regards to bias and SD. Typically, the reconstruction method 3DRP yielded less frame-sampling dependence and less uncertain results, compared with OSEM, but was on average more biased.

CONCLUSION

Of the 6 sampling schemes investigated in this study, an early frame duration of 6-15 s generally kept both bias and uncertainty to a minimum, for both 3DRP and OSEM reconstructions. Very-short frames of 1 s should be avoided because they typically resulted in the largest parameter bias and uncertainty. Furthermore, 3DRP may be preferred over OSEM for short frames with poor statistics.

Simulation environment to evaluate closed-loop insulin delivery systems in type 1 diabetes

BACKGROUND

Closed-loop insulin delivery systems linking subcutaneous insulin infusion to real-time continuous glucose monitoring need to be evaluated in humans, but progress can be accelerated with the use of in silico testing. We present a simulation environment designed to support the development and testing of closed-loop insulin delivery systems in type 1 diabetes mellitus (T1DM).

METHODS

The principal components of the simulation environment include a mathematical model of glucose regulation representing a virtual population with T1DM, the glucose measurement model, and the insulin delivery model. The simulation environment is highly flexible. The user can specify an experimental protocol, define a population of virtual subjects, choose glucose measurement and insulin delivery models, and specify outcome measures. The environment provides graphical as well as numerical outputs to enable a comprehensive analysis of in silico study results. The simulation environment is validated by comparing its predictions against a clinical study evaluating overnight closed-loop insulin delivery in young people with T1DM using a model predictive controller.

RESULTS

The simulation model of glucose regulation is described, and population values of 18 synthetic subjects are provided. The validation study demonstrated that the simulation environment was able to reproduce the population results of the clinical study conducted in young people with T1DM.

CONCLUSIONS

Closed-loop trials in humans should be preceded and concurrently guided by highly efficient and resource-saving computer-based simulations. We demonstrate validity of population-based predictions obtained with our simulation environment.