Purpose: This study was designed to seek associations between positron emission tomography/computed tomography (PET/CT) parameters, contrast enhanced neck computed tomography (CECT) and pathological findings, and to determine the potential prognostic value of PET/CT and CECT parameters in oral cavity squamous cell carcinoma (OCSCC).
Materials and method: 36 OCSCC patients underwent staging PET/CT and 30/36 of patients had CECT. PET/CT parameters were measured for the primary tumor and the hottest involved node, including maximum, mean, and peak standardized uptake values (SUV max, SUV mean, and SUV peak), metabolic tumor volume (MTV), total lesion glycolysis (TLG), standardized added metabolic activity (SAM), and normalized standardized added metabolic activity (N SAM). Qualitative assessment of PET/CT and CECT were also performed. Pathological outcomes included: perineural invasion, lymphovascular invasion, nodal extracapsular spread, grade, pathologic T and N stages. Multivariable logistic regression models were fit for each parameter and outcome adjusting for potentially confounding variables. Multivariable Cox proportional hazards models were used for progression free survival (PFS), locoregional recurrence free survival (LRFS), overall survival (OS) and distant metastasis free survival (DMFS).
Results: In multivariable analysis, patients with high (≥ median) tumor SUV max (OR 6.3), SUV mean (OR 6.3), MTV (OR 19.0), TLG (OR 19.0), SAM (OR 11.7) and N SAM (OR 19.0) had high pathological T-stage (T3/T4) (p < 0.05). Ring/heterogeneous pattern on CECT qualitative assessment was associated with worse DMFS and OS.
Conclusion: High PET/CT parameters were associated with pathologically advanced T stage (T3/T4). Qualitative assessment of CECT has prognostic value. PET/CT parameters did not predict clinical outcome.
Background: The biological network is highly dynamic. Functional relations between genes can be activated or deactivated depending on the biological conditions. On the genome-scale network, subnetworks that gain or lose local expression consistency may shed light on the regulatory mechanisms related to the changing biological conditions, such as disease status or tissue developmental stages.
Results: In this study, we develop a new method to select genes and modules on the existing biological network, in which local expression consistency changes significantly between clinical conditions. The method is called DNLC: Differential Network Local Consistency. In simulations, our algorithm detected artificially created local consistency changes effectively. We applied the method on two publicly available datasets, and the method detected novel genes and network modules that were biologically plausible.
Conclusions: The new method is effective in finding modules in which the gene expression consistency change between clinical conditions. It is a useful tool that complements traditional differential expression analyses to make discoveries from gene expression data.
High-throughput expression technologies, including gene expression array and liquid chromatography-mass spectrometry (LC-MS) and so on, measure thousands of features, i.e., genes or metabolites, on a continuous scale. In such data, both linear and nonlinear relations exist between features. Nonlinear relations can reflect critical regulation patterns in the biological system. However, they are not identified and utilized by traditional clustering methods based on linear associations. Clustering based on general dependences, i.e., both linear and nonlinear relations, is hampered by the high dimensionality and high noise level of the data. We developed a sensitive nonparametric measure of general dependence between (groups of) random variables in high dimensions. Based on this dependence measure, we developed a hierarchical clustering method. In simulation studies, the method outperformed correlation-and mutual information (MI)-based hierarchical clustering methods in clustering features with nonlinear dependences. We applied the method to a microarray data set measuring the gene expression in cell-cycle time series to show it generates biologically relevant results.
by
Edward Castillo;
Richard Castillo;
Yevgeniy Vinogradskiy;
Michele Dougherty;
David Solis;
Nicholas Myziuk;
Andrew Thompson;
Rudy Guerra;
Girish Nair;
Thomas Guerrero
Computed tomography (CT) derived ventilation algorithms estimate the apparent voxel volume changes within an inhale/exhale CT image pair. Transformation-based methods compute these estimates solely from the spatial transformation acquired by applying a deformable image registration (DIR) algorithm to the image pair. However, approaches based on finite difference approximations of the transformation's Jacobian have been shown to be numerically unstable. As a result, transformation-based CT ventilation is poorly reproducible with respect to both DIR algorithm and CT acquisition method. Purpose: We introduce a novel Integrated Jacobian Formulation (IJF) method for estimating voxel volume changes under a DIR-recovered spatial transformation. The method is based on computing volume estimates of DIR mapped subregions using the hit-or-miss sampling algorithm for integral approximation. The novel approach allows for regional volume change estimates that (a) respect the resolution of the digital grid and (b) are based on approximations with quantitatively characterized and controllable levels of uncertainty. As such, the IJF method is designed to be robust to variations in DIR solutions and thus overall more reproducible. Methods: Numerically, Jacobian estimates are recovered by solving a simple constrained linear least squares problem that guarantees the recovered global volume change is equal to the global volume change obtained from the inhale and exhale lung segmentation masks. Reproducibility of the IJF method with respect to DIR solution was assessed using the expert-determined landmark point pairs and inhale/exhale phases from 10 four-dimensional computed tomographies (4DCTs) available on www.dir-lab.com. Reproducibility with respect to CT acquisition was assessed on the 4DCT and 4D cone beam CT (4DCBCT) images acquired for five lung cancer patients prior to radiotherapy. Results: The ten Dir-Lab 4DCT cases were registered twice with the same DIR algorithm, but with different smoothing parameter. Finite difference Jacobian (FDJ) and IFJ images were computed for both solutions. The average spatial errors (300 landmarks per case) for the two DIR solution methods were 0.98 (1.10) and 1.02 (1.11). The average Pearson correlation between the FDJ images computed from the two DIR solutions was 0.83 (0.03), while for the IJF images it was 1.00 (0.00). For intermodality assessment, the IJF and FDJ images were computed from the 4DCT and 4DCBCT of five patients. The average Pearson correlation of the spatially aligned FDJ images was 0.27 (0.11), while it was 0.77 (0.13) for the IFJ method. Conclusion: The mathematical theory underpinning the IJF method allows for the generation of ventilation images that are (a) computed with respect to DIR spatial accuracy on the digital voxel grid and (b) based on DIR-measured subregional volume change estimates acquired with quantifiable and controllable levels of uncertainty. Analyses of the experiments are consistent with the mathematical theory and indicate that IJF ventilation imaging has a higher reproducibility with respect to both DIR algorithm and CT acquisition method, in comparison to the standard finite difference approach.
In many observational longitudinal studies, the outcome of interest presents a skewed distribution, is subject to censoring due to detection limit or other reasons, and is observed at irregular times that may follow a outcome-dependent pattern. In this work, we consider quantile regression modeling of such longitudinal data, because quantile regression is generally robust in handling skewed and censored outcomes and is flexible to accommodate dynamic covariate-outcome relationships. Specifically, we study a longitudinal quantile regression model that specifies covariate effects on the marginal quantiles of the longitudinal outcome. Such a model is easy to interpret and can accommodate dynamic outcome profile changes over time. We propose estimation and inference procedures that can appropriately account for censoring and irregular outcome-dependent follow-up. Our proposals can be readily implemented based on existing software for quantile regression. We establish the asymptotic properties of the proposed estimator, including uniform consistency and weak convergence. Extensive simulations suggest good finite-sample performance of the new method. We also present an analysis of data from a long-term study of a population exposed to polybrominated biphenyls (PBB), which uncovers an inhomogeneous PBB elimination pattern that would not be detected by traditional longitudinal data analysis.
Background: We describe a suite of tools and methods that form a core set of capabilities for researchers and clinical investigators to evaluate multiple analytical pipelines and quantify sensitivity and variability of the results while conducting large-scale studies in investigative pathology and oncology. The overarching objective of the current investigation is to address the challenges of large data sizes and high computational demands. Results: The proposed tools and methods take advantage of state-of-the-art parallel machines and efficient content-based image searching strategies. The content based image retrieval (CBIR) algorithms can quickly detect and retrieve image patches similar to a query patch using a hierarchical analysis approach. The analysis component based on high performance computing can carry out consensus clustering on 500,000 data points using a large shared memory system. Conclusions: Our work demonstrates efficient CBIR algorithms and high performance computing can be leveraged for efficient analysis of large microscopy images to meet the challenges of clinically salient applications in pathology. These technologies enable researchers and clinical investigators to make more effective use of the rich informational content contained within digitized microscopy specimens.
Purpose We prospectively evaluated the amino acid analogue positron emission tomography radiotracer anti-3-[18F]FACBC compared to ProstaScint® (111In-capromab pendetide) single photon emission computerized tomography-computerized tomography to detect recurrent prostate carcinoma. Materials and Methods A total of 93 patients met study inclusion criteria who underwent anti-3-[18F]FACBC positron emission tomography-computerized tomography plus 111In-capromab pendetide single photon emission computerized tomography-computerized tomography for suspected recurrent prostate carcinoma within 90 days. Reference standards were applied by a multidisciplinary board. We calculated diagnostic performance for detecting disease. Results In the 91 of 93 patients with sufficient data for a consensus on the presence or absence of prostate/bed disease anti-3-[ 18F]FACBC had 90.2% sensitivity, 40.0% specificity, 73.6% accuracy, 75.3% positive predictive value and 66.7% negative predictive value compared to 111In-capromab pendetide with 67.2%, 56.7%, 63.7%, 75.9% and 45.9%, respectively. In the 70 of 93 patients with a consensus on the presence or absence of extraprostatic disease anti-3-[18F]FACBC had 55.0% sensitivity, 96.7% specificity, 72.9% accuracy, 95.7% positive predictive value and 61.7% negative predictive value compared to 111In-capromab pendetide with 10.0%, 86.7%, 42.9%, 50.0% and 41.9%, respectively. Of 77 index lesions used to prove positivity histological proof was obtained in 74 (96.1%). Anti-3-[18F]FACBC identified 14 more positive prostate bed recurrences (55 vs 41) and 18 more patients with extraprostatic involvement (22 vs 4). Anti-3-[18F]FACBC positron emission tomography-computerized tomography correctly up-staged 18 of 70 cases (25.7%) in which there was a consensus on the presence or absence of extraprostatic involvement. Conclusions Better diagnostic performance was noted for anti-3-[18F]FACBC positron emission tomography-computerized tomography than for 111In-capromab pendetide single photon emission computerized tomography-computerized tomography for prostate carcinoma recurrence. The former method detected significantly more prostatic and extraprostatic disease.
Functional aspects of network integration in the cerebellar cortex have been studied experimentally and modeled in much detail ever since the early work by theoreticians such as Marr, Albus and Braitenberg more than 40years ago. In contrast, much less is known about cerebellar processing at the output stage, namely in the cerebellar nuclei (CN). Here, input from Purkinje cells converges to control CN neuron spiking via GABAergic inhibition, before the output from the CN reaches cerebellar targets such as the brainstem and the motor thalamus. In this article we review modeling studies that address how the CN may integrate cerebellar cortical inputs, and what kind of signals may be transmitted. Specific hypotheses in the literature contrast rate coding and temporal coding of information in the spiking output from the CN. One popular hypothesis states that post-inhibitory rebound spiking may be an important mechanism by which Purkinje cell inhibition is turned into CN output spiking, but this hypothesis remains controversial. Rate coding clearly does take place, but in what way it may be augmented by temporal codes remains to be more clearly established. Several candidate mechanisms distinct from rebound spiking are discussed, such as the significance of spike time correlations between Purkinje cell pools to determine CN spike timing, irregularity of Purkinje cell spiking as a determinant of CN firing rate, and shared brief pauses between Purkinje cell pools that may trigger individual CN spikes precisely.
Phytochelatins (PyCs) are a diverse set of plant compounds that chelate metals, protect against metal toxicity and function in metal homeostasis. PyCs are present in plants consumed as food by humans and could, in principle, impact absorption and utilization of essential and toxic metals such as selenium and cadmium, respectively. PyCs vary in terminal amino acid composition and chain length, exist in multiple oxidation states and reversibly bind multiple metals; consequently, PyCs include a large set of possible structures. Although individual PyC-metal complexes have been studied, no resource exists to characterize the diversity of PyCs and PyC-metal complexes. We used the scientific literature to develop a database of elemental formulas for polymer forms varying in chain length from 2 to 11 glutamyl-cysteine repeats. Using elemental formulas, we calculated monoisotopic masses using the most abundant isotopes of each element and calculated masses for complexes with 13 metals of nutritional and toxicological significance. The resulting phytochelatin database (PyCDB) contains 46 260 unique elemental formulas for PyC and PyC-metal complexes. The database is available online for download as well as for direct mass queries for mass spectrometry using an accurate mass annotation tool for user-selected PyC types, metals and adducts of interest. We performed studies of a commonly consumed food-onion-to validate the database and test utility of the tool. Onion samples were analyzed using ultra-high resolution mass spectrometry-based metabolomics. Mass spectral features were annotated using the PyCDB web tool and the R package, xMSannotator; annotated features were further validated by collision-induced dissociation mass spectrometry. The results establish use and a workflow for PyCDB as a resource for characterization of PyCs and PyC-metal complexes.
by
Sarah T. Plummer;
Christoph P. Hornik;
Hamilton Baker;
Gregory A. Fleming;
Susan Foerster;
Matthew Ferguson;
Andrew C. Glatz;
Russel Hirsch;
Jeffrey P. Jacobs;
Kyong-Jin Lee;
Alan B. Lewis;
Jennifer S. Li;
Mary Martin;
Diego Porras;
Wolfgang A. K. Radtke;
John F. Rhodes;
Julie A. Vincent;
Jeffrey D. Zampi;
Kevin D. Hill
Objectives: Aortic arch reconstruction in children with single ventricle lesions may predispose to circulatory inefficiency and maladaptive physiology leading to increased myocardial workload. We sought to describe neoaortic anatomy and physiology, risk factors for abnormalities, and impact on right ventricular function in patients with single right ventricle lesions after arch reconstruction. Methods: Prestage II aortic angiograms from the Pediatric Heart Network Single Ventricle Reconstruction trial were analyzed to define arch geometry (Romanesque [normal], crenel [elongated] , or gothic [angular]), indexed neoaortic dimensions, and distensibility. Comparisons were made with 50 single-ventricle controls without prior arch reconstruction. Factors associated with ascending neoaortic dilation, reduced distensibility, and decreased ventricular function on the 14-month echocardiogram were evaluated using univariate and multivariable logistic regression. Results: Interpretable angiograms were available for 326 of 389 subjects (84%). Compared with controls, study subjects more often demonstrated abnormal arch geometry (67% vs 22%, P < .01) and had increased ascending neoaortic dilation (Z score 3.8 ± 2.2 vs 2.6 ± 2.0, P < .01) and reduced distensibility index (2.2 ± 1.9 vs 8.0 ± 3.8, P < .01). Adjusted odds of neoaortic dilation were increased in subjects with gothic arch geometry (odds ratio [OR], 3.2 vs crenel geometry, P < .01) and a right ventricle-pulmonary artery shunt (OR, 3.4 vs Blalock–Taussig shunt, P < .01) but were decreased in subjects with aortic atresia (OR, 0.7 vs stenosis, P < .01) and those with recoarctation (OR, 0.3 vs no recoarctation, P = .04). No demographic, anatomic, or surgical factors predicted reduced distensibility. Neither dilation nor distensibility predicted reduced right ventricular function. Conclusions: After Norwood surgery, the reconstructed neoaorta demonstrates abnormal anatomy and physiology. Further study is needed to evaluate the longer-term impact of these features.