Incorporation of tumor shape into an assessment of spatial heterogeneity for human sarcomas imaged with FDG-PET

We have been exploring techniques for evaluation of fluoro-deoxyglucose (FDG) utilization characteristics in human sarcomas measured with positron emission tomography. In previous work, a measure of spatial heterogeneity based on evaluating the deviation of the FDG utilization distribution within the tumor region from a unimodal elliptically contoured spatial pattern was developed. This measure was shown to be a strong prognostic indicator of time to death. The present work explores a more general measure of heterogeneity which incorporates tumor boundary information. The approach relies on the use of a non-parametric representation for the tumor boundary surface. A set of 179 sarcoma patients with follow-up are evaluated with this technique. The results are analyzed to obtain empirical insight into the factors explaining elliptical heterogeneity. In terms of patient survival, the incorporation of the more sophisticated measure of spatial heterogeneity shows some potential improvement in the prediction risk. Further data will enable us to obtain a clearer empirical understanding of the role of the surface information in the measurement of tumor heterogeneity.     

A heterogeneity measure for cluster identification with application to disease mapping

Mapping of disease incidence has long been of importance to epidemiology and public health. In this paper, we consider identification of clusters of spatial units with elevated disease rates and develop a new approach that estimates the relative disease risk in association with potential risk factors and simultaneously identifies clusters corresponding to elevated risks. A heterogeneity measure is proposed to enable the comparison of a candidate cluster and its complement under a pair of complementary models. A quasi-likelihood procedure is developed for estimating the model parameters and identifying the clusters. An advantage of our approach over traditional spatial clustering methods is the identification of clusters that can have arbitrary shapes due to abrupt or noncontiguous changes while accounting for risk factors and spatial correlation. Asymptotic properties of the proposed methodology are established and a simulation study shows empirically sound finite-sample properties. The mapping and clustering of enterovirus 71 infections in Taiwan are carried out for illustration.     

Segmentation improvement through denoising of PET images with 3D-context modelling in wavelet domain

Positron emission tomography (PET) images have been incorporated into the radiotherapy process as a powerful tool to assist in the contouring of lesions, leading to the emergence of a broad spectrum of automatic segmentation schemes for PET images (PET-AS). However, not all proposed PET-AS algorithms take into consideration the previous steps of image preparation. PET image noise has been shown to be one of the most relevant affecting factors in segmentation tasks. This study demonstrates a nonlinear filtering method based on spatially adaptive wavelet shrinkage using three-dimensional context modelling that considers the correlation of each voxel with its neighbours. Using this noise reduction method, excellent edge conservation properties are obtained. To evaluate the influence in the segmentation schemes of this filter, it was compared with a set of Gaussian filters (the most conventional) and with two previously optimised edge-preserving filters. Five segmentation schemes were used (most commonly implemented in commercial software): fixed thresholding, adaptive thresholding, watershed, adaptive region growing and affinity propagation clustering. Segmentation results were evaluated using the Dice similarity coefficient and classification error. A simple metric was also included to improve the characterisation of the filters used for induced blurring evaluation, based on the measurement of the average edge width. The proposed noise reduction procedure improves the results of segmentation throughout the performed settings and was shown to be more stable in low-contrast and high-noise conditions. Thus, the capacity of the segmentation method is reinforced by the denoising plan used.     

Benefits and risks of transforming data from dynamic positron emission tomography, with an application to hepatic encephalopathy

Transforming data sets to bring out expected model features can be valuable within limits and misleading outside them. Here we establish such limits for the widely used Gjedde-Patlak representation of dynamic PET data, with an application to hepatic encephalopathy.     

Synthesis and evaluation of novel potent TSPO PET ligands with 2-phenylpyrazolo[1,5-a]pyrimidin-3-yl acetamide

Translocator protein (TSPO) expression is closely related with neuroinflammation and neuronal damage which might cause several central nervous system diseases. Herein, a series of TSPO ligands (11a–c and 13a–d) with a 2-phenylpyrazolo[1,5-a]pyrimidin-3-yl acetamide structure were prepared and evaluated via an in vitro binding assay. Most of the novel ligands exhibited a nano-molar affinity for TSPO, which was better than that of DPA-714. Particularly, 11a exhibited a subnano-molar TSPO binding affinity with suitable lipophilicity for in vivo brain studies. After radiolabeling with fluorine-18, [¹⁸F]11a was used for a dynamic positron emission tomography (PET) study in a rat LPS-induced neuroinflammation model; the inflammatory lesion was clearly visualized with a superior target-to-background ratio compared to [¹⁸F]DPA-714. An immunohistochemical examination of the dissected brains confirmed that the uptake location of [¹⁸F]11a in the PET study was consistent with a positively activated microglia region. This study proved that [¹⁸F]11a could be employed as a potential PET tracer for detecting neuroinflammation and could give possibility for diagnosis of other diseases, such as cancers related with TSPO expression.     

Site-specifically C-labeled Sel-tagged annexin A5 and a size-matched control for dynamic in vivo PET imaging of protein distribution in tissues prior to and after induced cell death

Background

Radiolabeled annexin A5 (AnxA5) is widely used for detecting phosphatidylserine exposed on cell surfaces during apoptosis. We describe here a new method for labeling AnxA5 and a size-matched control protein with short-lived carbon-11, for probing the specificity of in vivo cell death monitoring using positron emission tomography (PET) imaging.

Methods

AnxA5 and the control protein were recombinantly expressed with a C-terminal “Sel-tag”, the tetrapeptide –Gly-Cys-Sec-Gly–COOH. The proteins were then labeled either fluorescently for in vitro corroborations of binding behaviors or with ¹¹C for dynamic in vivo PET studies.

Results

AnxA5 demonstrated retained calcium-dependent binding to apoptotic cells after the C-terminus modification. The control protein showed no functional binding. The ¹¹C-ligands demonstrated similar in vivo pharmacokinetic behavior in healthy mice except for higher uptake in kidney and higher intact elimination to urine of AnxA5. After inducing hepatic apoptosis, however, the uptake of labeled AnxA5 in the targeted tissue increased compared to baseline levels while that of the control protein tended to decrease.

Conclusions

These data suggest that the combined use of these two tracers can facilitate differentiating specific AnxA5 binding and its changes caused by induced cell death from uptake due to non-specific permeability and retention effects at baseline or after therapy.

General significance

The Sel-tag enables rapid and mild reactions with electrophilic agents giving site-specifically labeled proteins for multi-probe analyses. The combined use of ¹¹C-labeled AnxA5 and a size-matched control protein with dynamic PET can be useful for evaluating drug effects on target as well as off-target tissues.     

Gamma frailty model for linkage analysis with application to interval-censored migraine data

For many diseases, it seems that the age at onset is geneticallyinfluenced. Therefore, the age-at-onset data are often collectedin order to map the disease gene(s). The ages are often (right)censored or truncated, and therefore, many standard techniquesfor linkage analysis cannot be used. In this paper, we presenta correlated frailty model for censored survival data of siblings.The model is used for testing heritability for the age at onsetand linkage between the loci and the gene(s) that influence(s)the survival time. The model is applied to interval-censoredmigraine twin data. Heritability (obtained from the frailtiesrather than actual onset times) was estimated as 0.42; thisvalue was highly significant. The highest lod score, a scoreof 1.9, was found at the end of chromosome 19.     

Studies with Differentially Labeled [11C]Cocaine, [11C]Norcocaine, [11C]Benzoylecgonine,and [11C]-and 4′-[18F]Fluorococaine to Probe the Extent to Which [11C]Cocaine Metabolites Contribute to PET Images of the Baboon Brain

Abstract: The psychostimulant drug of abuse, cocaine (benzoylecgonine methyl ester), is rapidly metabolized by cleavage of its two ester groups, to give benzoylecgonine (BE) and ecgonine methyl ester, and by N-demethylation, to give N-norcocaine (NC). The recent use of [N-methyl-¹¹CH₃]cocaine to image brain cocaine binding sites with positron emission tomography (PET) raises the question of whether PET images partially reflect the distribution and kinetics of labeled cocaine metabolites. We prepared [O-metty/-¹¹CH₃]cocaine by methylation of the sodium salt of BE with [¹¹C]CH₃l, and showed that PET baboon brain scans, as well as regional brain kinetics and plasma time-activity curves corrected for the presence of labeled metabolites, are nearly identical to those seen with [N-methyl-¹¹CH₃]cocaine. This strongly suggests that ¹¹C metabolites do not significantly affect PET images, because the metabolite pattern is different for the two labeled forms of cocaine. In particular, nearly half the ¹¹C in blood plasma at 30 min was [¹¹C]CO₂ when [N-methy/-¹¹CH₃]cocaine was administered, whereas [¹¹C]CO₂ was not formed from [O-methy/-¹¹CH₃]cocaine. Only a trace of [¹¹C]NC was detected in plasma after [O-methyl-¹¹CH₃]cocaine administration. Nearly identical brain PET data were also obtained when 4′-[N-methy/-¹¹CH₃]fluorococaine and 4′-[¹⁸F]fluoro-cocaine (prepared by nucleophilic aromatic substitution from [¹⁸F]fluoride-and 4′-nitrococaine) were compared with [N-methy/-¹¹CH₃]cocaine. In vitro assays with rat brain membranes showed that cocaine and 4′-fluoroco-caine were equipotent at the dopamine reuptake site, but that 4′-fluorococaine was about 100 times more potent at the 5-hydroxytryptamine reuptake site. The studies with positron-emitting 4′-fluorococaines thus support the lack of significance of labeled metabolites or of binding to 5-hydroxytryptamine reuptake sites to PET images taken with [N-methy/-¹¹CH₃]cocaine. [¹¹C]NC prepared by O-methylation of norbenzoylecgonine gave PET images with preferential uptake in striatum, but slower clearance from all brain regions than [O-methy/-¹¹CH₃]cocaine. [¹¹C]BE prepared by N-methylation of norbenzoylecgonine did not show brain uptake.     

Analysis of longitudinal data in the presence of informative observational times and a dependent terminal event, with application to medical cost data

Summary .   In longitudinal observational studies, repeated measures are often taken at informative observation times. Also, there may exist a dependent terminal event such as death that stops the follow-up. For example, patients in poorer health are more likely to seek medical treatment and their medical cost for each visit tends to be higher. They are also subject to a higher mortality rate. In this article, we propose a random effects model of repeated measures in the presence of both informative observation times and a dependent terminal event. Three submodels are used, respectively, for (1) the intensity of recurrent observation times, (2) the amount of repeated measure at each observation time, and (3) the hazard of death. Correlated random effects are incorporated to join the three submodels. The estimation can be conveniently accomplished by Gaussian quadrature techniques, e.g., SAS Proc NLMIXED . An analysis of the cost-accrual process of chronic heart failure patients from the clinical data repository at the University of Virginia Health System is presented to illustrate the proposed method.     

A nonparametric approach to the analysis of longitudinal data via a set of level crossing problems with application to the analysis of microarray time course experiments

Here we develop a completely nonparametric method for comparing two groups on a set of longitudinal measurements. No assumptions are made about the form of the mean response function, the covariance structure or the distributional form of disturbances around the mean response function. The solution proposed here is based on the realization that every longitudinal data set can also be thought of as a collection of survival data sets where the events of interest are level crossings. The method for testing for differences in the longitudinal measurements then is as follows: for an arbitrarily large set of levels, for each subject determine the first time the subject has an upcrossing and a downcrossing for each level. For each level one then computes the log rank statistic and uses the maximum in absolute value of all these statistics as the test statistic. By permuting group labels we obtain a permutation test of the hypothesis that the joint distribution of the measurements over time does not depend on group membership. Simulations are performed to investigate the power and it is applied to the area that motivated the method-the analysis of microarrays. In this area small sample sizes, few time points and far too many genes to consider genuine gene level longitudinal modeling have created a need for a simple, model free test to screen for interesting features in the data.     

Bayesian hierarchical spatially correlated functional data analysis with application to colon carcinogenesis

Summary .   In this article, we present new methods to analyze data from an experiment using rodent models to investigate the role of p27, an important cell-cycle mediator, in early colon carcinogenesis. The responses modeled here are essentially functions nested within a two-stage hierarchy. Standard functional data analysis literature focuses on a single stage of hierarchy and conditionally independent functions with near white noise. However, in our experiment, there is substantial biological motivation for the existence of spatial correlation among the functions, which arise from the locations of biological structures called colonic crypts: this possible functional correlation is a phenomenon we term crypt signaling . Thus, as a point of general methodology, we require an analysis that allows for functions to be correlated at the deepest level of the hierarchy. Our approach is fully Bayesian and uses Markov chain Monte Carlo methods for inference and estimation. Analysis of this data set gives new insights into the structure of p27 expression in early colon carcinogenesis and suggests the existence of significant crypt signaling. Our methodology uses regression splines, and because of the hierarchical nature of the data, dimension reduction of the covariance matrix of the spline coefficients is important: we suggest simple methods for overcoming this problem.     

Efficient computation of subset selection probabilities with application to Cox regression

An efficient method is presented to compute the probabilityof selection of a specified subset from the set of all subsetsof a fixed size where the subsets are taken from a populationwhose units have varying individual probabilities of selection.The problem is motivated by the computation of the exact marginallikelihood for the Cox proportional hazards model.     

Joint analysis of longitudinal data and recurrent episodes data with application to medical cost analysis

This paper discusses regression analysis of longitudinal data in which the observation process may be related to the longitudinal process of interest. Such data have recently attracted a great deal of attention and some methods have been developed. However, most of those methods treat the observation process as a recurrent event process, which assumes that one observation can immediately follow another. Sometimes, this is not the case, as there may be some delay or observation duration. Such a process is often referred to as a recurrent episode process. One example is the medical cost related to hospitalization, where each hospitalization serves as a single observation. For the problem, we present a joint analysis approach for regression analysis of both longitudinal and observation processes and a simulation study is conducted that assesses the finite sample performance of the approach. The asymptotic properties of the proposed estimates are also given and the method is applied to the medical cost data that motivated this study.     

A composite likelihood approach to latent multivariate Gaussian modeling of SNP data with application to genetic association testing

Many statistical tests have been proposed for case-control data to detect disease association with multiple single nucleotide polymorphisms (SNPs) in linkage disequilibrium. The main reason for the existence of so many tests is that each test aims to detect one or two aspects of many possible distributional differences between cases and controls, largely due to the lack of a general and yet simple model for discrete genotype data. Here we propose a latent variable model to represent SNP data: the observed SNP data are assumed to be obtained by discretizing a latent multivariate Gaussian variate. Because the latent variate is multivariate Gaussian, its distribution is completely characterized by its mean vector and covariance matrix, in contrast to much more complex forms of a general distribution for discrete multivariate SNP data. We propose a composite likelihood approach for parameter estimation. A direct application of this latent variable model is to association testing with multiple SNPs in a candidate gene or region. In contrast to many existing tests that aim to detect only one or two aspects of many possible distributional differences of discrete SNP data, we can exclusively focus on testing the mean and covariance parameters of the latent Gaussian distributions for cases and controls. Our simulation results demonstrate potential power gains of the proposed approach over some existing methods.     

A mixture model for longitudinal data with application to assessment of noncompliance

In clinical trials of a self-administered drug, repeated measures of a laboratory marker, which is affected by study medication and collected in all treatment arms, can provide valuable information on population and individual summaries of compliance. In this paper, we introduce a general finite mixture of nonlinear hierarchical models that allows estimates of component membership probabilities and random effect distributions for longitudinal data arising from multiple subpopulations, such as from noncomplying and complying subgroups in clinical trials. We outline a sampling strategy for fitting these models, which consists of a sequence of Gibbs, Metropolis-Hastings, and reversible jump steps, where the latter is required for switching between component models of different dimensions. Our model is applied to identify noncomplying subjects in the placebo arm of a clinical trial assessing the effectiveness of zidovudine (AZT) in the treatment of patients with HIV, where noncompliance was defined as initiation of AZT during the trial without the investigators' knowledge. We fit a hierarchical nonlinear change-point model for increases in the marker MCV (mean corpuscular volume of erythrocytes) for subjects who noncomply and a constant mean random effects model for those who comply. As part of our fully Bayesian analysis, we assess the sensitivity of conclusions to prior and modeling assumptions and demonstrate how external information and covariates can be incorporated to distinguish subgroups.     

Multilevel modeling analysis of dyadic network data with an application to Ye'kwana food sharing

Behavioral ecologists have recently begun using multilevel modeling for the analysis of social behavior. We present a multilevel modeling formulation of the Social Relations Model that is well suited for the analysis of dyadic network data. This model, which we adapt for count data and small datasets, can be fitted using standard multilevel modeling software packages. We illustrate this model with an analysis of meal sharing among Ye'kwana horticulturalists in Venezuela. In this setting, meal sharing among households is predicted by an association index, which reflects the amount of time that members of the households are interacting. This result replicates recent findings that interhousehold food sharing is especially prevalent among households that interact and cooperate in multiple ways. We discuss opportunities for human behavioral ecologists to expand their focus to the multiple currencies and cooperative behaviors that characterize interpersonal relationships in preindustrial societies. We discuss possible extensions to this statistical modeling approach and applications to research by human behavioral ecologists and primatologists. Am J Phys Anthropol 157:507–512, 2015. © 2015 Wiley Periodicals, Inc.     

In Vitro and In Vivo Evaluation of N‐{2‐[4‐(3‐Cyanopyridin‐2‐yl)piperazin‐1‐yl]ethyl}‐3‐[11C]methoxybenz­amide,a Positron Emission Tomography (PET) Radioligand for Dopamine D4 Receptors,in Rodents

The D₄ dopamine receptor belongs to the D₂‐like family of dopamine receptors, and its exact regional distribution in the central nervous system is still a matter of considerable debate. The availability of a selective radioligand for the D₄ receptor with suitable properties for positron emission tomography (PET) would help resolve issues of D₄ receptor localization in the brain, and the presumed diurnal change of expressed protein in the eye and pineal gland. We report here on in vitro and in vivo characteristics of the high‐affinity D₄ receptor‐selective ligand N‐{2‐[4‐(3‐cyanopyridin‐2‐yl)piperazin‐1‐yl]ethyl}‐3‐[¹¹C]methoxybenzamide ([¹¹C] 2 ) in rat. The results provide new insights on the in vitro properties that a brain PET dopamine D₄ radioligand should possess in order to have improved in vivo utility in rodents.     

A penalized likelihood approach for an illness-death model with interval-censored data: application to age-specific incidence of dementia

We consider the problem of estimating the intensity functions for a continuous time 'illness-death' model with intermittently observed data. In such a case, it may happen that a subject becomes diseased between two visits and dies without being observed. Consequently, there is an uncertainty about the precise number of transitions. Estimating the intensity of transition from health to illness by survival analysis (treating death as censoring) is biased downwards. Furthermore, the dates of transitions between states are not known exactly. We propose to estimate the intensity functions by maximizing a penalized likelihood. The method yields smooth estimates without parametric assumptions. This is illustrated using data from a large cohort study on cerebral ageing. The age-specific incidence of dementia is estimated using an illness-death approach and a survival approach.     

A statistical mechanical treatment of fatty acyl chain order in phospholipid bilayers and correlation with experimental data. B. Dipalmitoyl-3-sn-phosphatidylcholine

In order to help bridge the conceptual gap between experimental data on chains of phospholipid molecules and their microscopic organization, a theoretical model has been proposed in a preceding paper. The intentions associated with the new theory were to describe a model able to reproduce accurately the experimental data. This capability is essential to monitor some of the mechanisms behind the physical data. The results presented here show first that, provided a suitable fitting of the phenomenological parameters entailed in the model, the theory indeed gives good agreement with experimental data (²H-NMR, neutron scattering, calorimetry) obtained for a $\text{dipalmitoyl}\text{-3-sn-}\text{phosphatidylcholine}$ bilayer. This property of the model is then specifically used to describe the nature of the perturbing effects of local anaesthetics and cholesterol on the organization of the acyl chains and to correlate these effects with the experimental data. Finally the theoretical model is used to supplement experimental data by describing the acyl chain organization in terms of the most probable spectrum of chain conformations. Predictions are made about the one-, two- and three-dimensional mean spatial characteristics of the acyl chains.