Albumin concentrations are primarily determined by the body cell mass and the systemic inflammatory response in cancer patients with weight loss

The association between hypoalbuminemia and poor prognosis in patients with cancer is well recognized. However, the factors that contribute to the fall in albumin concentrations are not well understood. In the present study, we examined the relationship between circulating albumin concentrations, weight loss, the body cell mass (measured using total body potassium), and the presence of an inflammatory response (measured using C- reactive protein) in male patients (n=40) with advanced lung or gastrointestinal cancer. Albumin concentrations were significantly correlated with the percent ideal body weight (r=0.390, p lt 0.05), extent of reported weight loss (r=-0.492, p lt 0.01), percent predicted total body potassium (adjusted for age, height, and weight, r=0.686, p lt 0.001), and logo C-reactive protein concentrations (r=-0.545, p lt 0.001). On multiple regression analysis, the percent predicted total body potassium and log(10) C-reactive protein concentrations accounted for 63% of the variation in albumin concentrations (r(2) = 0.626, p lt 0.001). The interrelationship between albumin, body cell mass, and the inflammatory response is consistent with the concept that the presence of an ongoing inflammatory response contributes to the progressive loss of these vital protein components of the body and the subsequent death of patients with advanced cancer

Scan-rescan reproducibility of neurite microstructure estimates using NODDI

In this work we provide a preliminary assessment of the reproducibility of the Neurite Orientation Dispersion and Density Imaging (NODDI), a recent diffusion MRI technique for directly quantifying microstructural indices of neurites in vivo, in the human brain. It is important to assess the reproducibility of such a technique to verify the precision of the method, which has implications for translation to clinical studies. NODDI outputs indices which reflect the functional and computational complexity of various regions of the brain and thus can provide useful information, non-invasively, for understanding pathology of the brain. We compare the parameter maps derived from diffusion MRI data acquired using the NODDI protocol from a normal subject, at two separate imaging sessions. We show that the NODDI indices have reproducibility comparable to that of the DTI indices. We additionally show that the clinically feasible NODDI protocol maintains good reproducibility of parameter estimates, comparable to that of a more comprehensive protocol

Response Surface Regressions for Critical Value Bounds and Approximate p‐values in Equilibrium Correction Models

This is the final version. Available on open access from Wiley via the DOI in this record. We consider the popular ‘bounds test’ for the existence of a level relationship in conditional equilibrium correction models. By estimating response surface models based on about 95 billion simulated F‐statistics and 57 billion t‐statistics, we improve upon and substantially extend the set of available critical values, covering the full range of possible sample sizes and lag orders, and allowing for any number of long‐run forcing variables. By computing approximate P‐values, we find that the bounds test can be easily oversized by more than 5 percentage points in small samples when using asymptotic critical values

ardl: Estimating autoregressive distributed lag and equilibrium correction models

This is the final version. Available on open access from SAGE Publications via the DOI in this recordWe present a command, ardl, for the estimation of autoregressive distributed lag (ARDL) models in a time-series context. The ardl command can be used to fit an ARDL model with the optimal number of autoregressive and distributed lags based on the Akaike or Bayesian (Schwarz) information criterion. The regression results can be displayed in the ARDL levels form or in the error-correction representation of the model. The latter separates long-run and short-run effects and is available in two different parameterizations of the long-run (cointegrating) relationship. The popular bounds-testing procedure for the existence of a long-run levels relationship is implemented as a postestimation feature. Comprehensive critical values and approximate p-values obtained from response-surface regressions facilitate statistical inference

Ranking diffusion-MRI models with in-vivo human brain data

Diffusion MRI microstructure imaging provides a unique non-invasive probe into the microstructure of biological tissue. Its analysis relies on mathematical models relating microscopic tissue features to the MR signal. This work aims to determine which compartment models of diffusion MRI are best at describing the signal from in-vivo brain white matter. Recent work shows that three compartment models, including restricted intra-axonal, glial compartments and hindered extra-cellular diffusion, explain best multi b-value data sets from fixed rat brain tissue. Here, we perform a similar experiment using in-vivo human data. We compare one, two and three compartment models, ranking them with standard model selection criteria. Results show that, as with fixed tissue, three compartment models explain the data best, although simpler models emerge for the in-vivo data. We also find that splitting the scanning into shorter sessions has little effect on the models fitting and that the results are reproducible. The full ranking assists the choice of model and imaging protocol for future microstructure imaging applications in the brain

Deep residual networks for automatic segmentation of laparoscopic videos of the liver

MOTIVATION: For primary and metastatic liver cancer patients undergoing liver resection, a laparoscopic approach can reduce recovery times and morbidity while offering equivalent curative results; however, only about 10% of tumours reside in anatomical locations that are currently accessible for laparoscopic resection. Augmenting laparoscopic video with registered vascular anatomical models from pre-procedure imaging could support using laparoscopy in a wider population. Segmentation of liver tissue on laparoscopic video supports the robust registration of anatomical liver models by filtering out false anatomical correspondences between pre-procedure and intra-procedure images. In this paper, we present a convolutional neural network (CNN) approach to liver segmentation in laparoscopic liver procedure videos. METHOD: We defined a CNN architecture comprising fully-convolutional deep residual networks with multi-resolution loss functions. The CNN was trained in a leave-one-patient-out cross-validation on 2050 video frames from 6 liver resections and 7 laparoscopic staging procedures, and evaluated using the Dice score. RESULTS: The CNN yielded segmentations with Dice scores ≥0.95 for the majority of images; however, the inter-patient variability in median Dice score was substantial. Four failure modes were identified from low scoring segmentations: minimal visible liver tissue, inter-patient variability in liver appearance, automatic exposure correction, and pathological liver tissue that mimics non-liver tissue appearance. CONCLUSION: CNNs offer a feasible approach for accurately segmenting liver from other anatomy on laparoscopic video, but additional data or computational advances are necessary to address challenges due to the high inter-patient variability in liver appearance