Radiomics of NSCLC: Quantitative CT Image Feature Characterization and Tumor Shrinkage Prediction

Radiomics is the high-throughput extraction and analysis of quantitative image features. For non-small cell lung cancer (NSCLC) patients, radiomics can be applied to standard of care computed tomography (CT) images to improve tumor diagnosis, staging, and response assessment. The first objective of this work was to show that CT image features extracted from pre-treatment NSCLC tumors could be used to predict tumor shrinkage in response to therapy. This is important since tumor shrinkage is an important cancer treatment endpoint that is correlated with probability of disease progression and overall survival. Accurate prediction of tumor shrinkage could also lead to individually customized treatment plans. To accomplish this objective, 64 stage NSCLC patients with similar treatments were all imaged using the same CT scanner and protocol. Quantitative image features were extracted and principal component regression with simulated annealing subset selection was used to predict shrinkage. Cross validation and permutation tests were used to validate the results. The optimal model gave a strong correlation between the observed and predicted shrinkages with . The second objective of this work was to identify sets of NSCLC CT image features that are reproducible, non-redundant, and informative across multiple machines. Feature sets with these qualities are needed for NSCLC radiomics models to be robust to machine variation and spurious correlation. To accomplish this objective, test-retest CT image pairs were obtained from 56 NSCLC patients imaged on three CT machines from two institutions. For each machine, quantitative image features with concordance correlation coefficient values greater than 0.90 were considered reproducible. Multi-machine reproducible feature sets were created by taking the intersection of individual machine reproducible feature sets. Redundant features were removed through hierarchical clustering. The findings showed that image feature reproducibility and redundancy depended on both the CT machine and the CT image type (average cine 4D-CT imaging vs. end-exhale cine 4D-CT imaging vs. helical inspiratory breath-hold 3D CT). For each image type, a set of cross-machine reproducible, non-redundant, and informative image features was identified. Compared to end-exhale 4D-CT and breath-hold 3D-CT, average 4D-CT derived image features showed superior multi-machine reproducibility and are the best candidates for clinical correlation.

Design and analysis for three level hierarchical structures with count response

In numerous intervention studies and education field trials, random assignment to treatment occurs in clusters rather than at the level of observation. This departure of random assignment of units may be due to logistics, political feasibility, or ecological validity. Data within the same cluster or grouping are often correlated. Application of traditional regression techniques, which assume independence between observations, to clustered data produce consistent parameter estimates. However such estimators are often inefficient as compared to methods which incorporate the clustered nature of the data into the estimation procedure (Neuhaus 1993).1 Multilevel models, also known as random effects or random components models, can be used to account for the clustering of data by estimating higher level, or group, as well as lower level, or individual variation. Designing a study, in which the unit of observation is nested within higher level groupings, requires the determination of sample sizes at each level. This study investigates the design and analysis of various sampling strategies for a 3-level repeated measures design on the parameter estimates when the outcome variable of interest follows a Poisson distribution. ^ Results study suggest that second order PQL estimation produces the least biased estimates in the 3-level multilevel Poisson model followed by first order PQL and then second and first order MQL. The MQL estimates of both fixed and random parameters are generally satisfactory when the level 2 and level 3 variation is less than 0.10. However, as the higher level error variance increases, the MQL estimates become increasingly biased. If convergence of the estimation algorithm is not obtained by PQL procedure and higher level error variance is large, the estimates may be significantly biased. In this case bias correction techniques such as bootstrapping should be considered as an alternative procedure. For larger sample sizes, those structures with 20 or more units sampled at levels with normally distributed random errors produced more stable estimates with less sampling variance than structures with an increased number of level 1 units. For small sample sizes, sampling fewer units at the level with Poisson variation produces less sampling variation, however this criterion is no longer important when sample sizes are large. ^ 1Neuhaus J (1993). “Estimation efficiency and Tests of Covariate Effects with Clustered Binary Data”. Biometrics , 49, 989–996^

An Extended Hierarchical Task Analysis for Error Prediction in Medical Devices

This paper introduces an extended hierarchical task analysis (HTA) methodology devised to evaluate and compare user interfaces on volumetric infusion pumps. The pumps were studied along the dimensions of overall usability and propensity for generating human error. With HTA as our framework, we analyzed six pumps on a variety of common tasks using Norman’s Action theory. The introduced method of evaluation divides the problem space between the external world of the device interface and the user’s internal cognitive world, allowing for predictions of potential user errors at the human-device level. In this paper, one detailed analysis is provided as an example, comparing two different pumps on two separate tasks. The results demonstrate the inherent variation, often the cause of usage errors, found with infusion pumps being used in hospitals today. The reported methodology is a useful tool for evaluating human performance and predicting potential user errors with infusion pumps and other simple medical devices.

Generalized fuzzy clustering for segmentation of multi-spectral magnetic resonance images.

An integrated approach for multi-spectral segmentation of MR images is presented. This method is based on the fuzzy c-means (FCM) and includes bias field correction and contextual constraints over spatial intensity distribution and accounts for the non-spherical cluster's shape in the feature space. The bias field is modeled as a linear combination of smooth polynomial basis functions for fast computation in the clustering iterations. Regularization terms for the neighborhood continuity of intensity are added into the FCM cost functions. To reduce the computational complexity, the contextual regularizations are separated from the clustering iterations. Since the feature space is not isotropic, distance measure adopted in Gustafson-Kessel (G-K) algorithm is used instead of the Euclidean distance, to account for the non-spherical shape of the clusters in the feature space. These algorithms are quantitatively evaluated on MR brain images using the similarity measures.

Sequential updating via dynamic hierarchical models: A Bayesian approach to longitudinal data analysis

Most statistical analysis, theory and practice, is concerned with static models; models with a proposed set of parameters whose values are fixed across observational units. Static models implicitly assume that the quantified relationships remain the same across the design space of the data. While this is reasonable under many circumstances this can be a dangerous assumption when dealing with sequentially ordered data. The mere passage of time always brings fresh considerations and the interrelationships among parameters, or subsets of parameters, may need to be continually revised. ^ When data are gathered sequentially dynamic interim monitoring may be useful as new subject-specific parameters are introduced with each new observational unit. Sequential imputation via dynamic hierarchical models is an efficient strategy for handling missing data and analyzing longitudinal studies. Dynamic conditional independence models offers a flexible framework that exploits the Bayesian updating scheme for capturing the evolution of both the population and individual effects over time. While static models often describe aggregate information well they often do not reflect conflicts in the information at the individual level. Dynamic models prove advantageous over static models in capturing both individual and aggregate trends. Computations for such models can be carried out via the Gibbs sampler. An application using a small sample repeated measures normally distributed growth curve data is presented. ^

T cell adhesion regulation from clustering GM1 lipid rafts

Lipid rafts are small laterally mobile cell membrane structures that are highly enriched in lymphocyte signaling molecules. Lipid rafts can form from the assembly of specialized lipids and proteins through hydrophobic associations from saturated acyl chains. GM1 gangliosides are a common lipid raft component and have been shown to be essential in many T cell functions. Current lipid raft theory hypothesizes that certain aspects of T cell signaling can be initiated from the coalescence of these signaling-enriched lipid rafts to sites of receptor engagement. We have described how the specific aggregation of GM1 lipid rafts can cause a reorganization of cell surface molecular associations which include dynamic associations of β1 integrins with GM1 lipid rafts. These associations had pronounced effects on T cell adhesive and migratory states. We show that GM1 lipid raft aggregation can dramatically inhibit T cell migration and chemotaxis on the extracellular matrix constituent fibronectin. This inhibition of migration function was shown to be dependent on the src kinase Lck and PKC-regulated F-actin polymerization to extending pseudopods. Furthermore, GM1 lipid raft clustering could activate T cell adhesion-strengthening mechanisms. These include an increase in cellular rigidity, the creation of polymerized cortical F-actin structures, the induction of high affinity integrin states, an increase in surface area and symmetry of the contact plane, and resistance to shear flow detachment while adherent to fibronectin. This indicates that GM1 lipid raft aggregation defines a novel stimulus to regulate lymphocyte motility and cellular adhesion which could have important implications in T cell homing mechanisms. ^

Robust effect sizes and their confidence intervals for group difference between trajectories in hierarchical linear growth model

Hierarchical linear growth model (HLGM), as a flexible and powerful analytic method, has played an increased important role in psychology, public health and medical sciences in recent decades. Mostly, researchers who conduct HLGM are interested in the treatment effect on individual trajectories, which can be indicated by the cross-level interaction effects. However, the statistical hypothesis test for the effect of cross-level interaction in HLGM only show us whether there is a significant group difference in the average rate of change, rate of acceleration or higher polynomial effect; it fails to convey information about the magnitude of the difference between the group trajectories at specific time point. Thus, reporting and interpreting effect sizes have been increased emphases in HLGM in recent years, due to the limitations and increased criticisms for statistical hypothesis testing. However, most researchers fail to report these model-implied effect sizes for group trajectories comparison and their corresponding confidence intervals in HLGM analysis, since lack of appropriate and standard functions to estimate effect sizes associated with the model-implied difference between grouping trajectories in HLGM, and also lack of computing packages in the popular statistical software to automatically calculate them. ^ The present project is the first to establish the appropriate computing functions to assess the standard difference between grouping trajectories in HLGM. We proposed the two functions to estimate effect sizes on model-based grouping trajectories difference at specific time, we also suggested the robust effect sizes to reduce the bias of estimated effect sizes. Then, we applied the proposed functions to estimate the population effect sizes (d ) and robust effect sizes (du) on the cross-level interaction in HLGM by using the three simulated datasets, and also we compared the three methods of constructing confidence intervals around d and du recommended the best one for application. At the end, we constructed 95% confidence intervals with the suitable method for the effect sizes what we obtained with the three simulated datasets. ^ The effect sizes between grouping trajectories for the three simulated longitudinal datasets indicated that even though the statistical hypothesis test shows no significant difference between grouping trajectories, effect sizes between these grouping trajectories can still be large at some time points. Therefore, effect sizes between grouping trajectories in HLGM analysis provide us additional and meaningful information to assess group effect on individual trajectories. In addition, we also compared the three methods to construct 95% confident intervals around corresponding effect sizes in this project, which handled with the uncertainty of effect sizes to population parameter. We suggested the noncentral t-distribution based method when the assumptions held, and the bootstrap bias-corrected and accelerated method when the assumptions are not met.^

Gene clustering of the small leucine -rich repeat gene family

The small leucine-rich repeat proteoglycans (or SLRPs) are a group of extracellular proteins (ECM) that belong to the leucine-rich repeat (LRR) superfamily of proteins. The LRR is a protein folding motif composed of 20–30 amino acids with leucines in conserved positions. LRR-containing proteins are present in a broad spectrum of organisms and possess diverse cellular functions and localization. In mammals, the SLRPs are abundant in connective tissues, such as bones, cartilage, tendons, skin, and blood vessels. We have discovered a new member of the class I small leucine rich repeat proteoglycan (SLRP) family which is distinct from the other class I SLRPs since it possesses a unique stretch of aspartate residues at its N-terminus. For this reason, we called the molecule asporin. The deduced amino acid sequence is about 50% identical (and 70% similar) to decorin and biglycan. However, asporin does not contain a serine/glycine dipeptide sequence required for the assembly of O-linked glycosaminoglycans and is probably not a proteoglycan. The tissue expression of asporin partially overlaps with the expression of decorin and biglycan. During mouse embryonic development, asporin mRNA expression was detected primarily in the skeleton and other specialized connective tissues; very little asporin message was detected in the major parenchymal organs. The mouse asporin gene structure is similar to that of biglycan and decorin with 8 exons. The asporin gene is localized to human chromosome 9q22-9g21.3 where asporin is part of a SLRP gene cluster that includes ECM2, osteoadherin, and osteoglycin. This gene cluster of four LRR-encoding genes is embedded in a 238 kilobase intron of another novel gene named Tes9orf that is expressed primarily in the testes of the adult mouse. The SLRP genes are not present in Drosophila or C. elegans , but reside in three separate gene clusters in the puffer fish, mice and humans. Targeted disruption of individual mouse SLRP genes display minor connective tissue defects such as skin fragility, tendon laxity, minor growth plate defects, and mild osteoporosis. However, double and triple knockouts of SLRP genes exacerbate these phenotypes. Both the double epiphycan/biglycan and the triple PRELP/fibromodulin/biglycan knockout mice exhibit premature osteoarthritis. ^