A generative approach for image-based modeling of tumor growth

22nd International Conference, IPMI 2011, Kloster Irsee, Germany, July 3-8, 2011. ProceedingsExtensive imaging is routinely used in brain tumor patients to monitor the state of the disease and to evaluate therapeutic options. A large number of multi-modal and multi-temporal image volumes is acquired in standard clinical cases, requiring new approaches for comprehensive integration of information from different image sources and different time points. In this work we propose a joint generative model of tumor growth and of image observation that naturally handles multi-modal and longitudinal data. We use the model for analyzing imaging data in patients with glioma. The tumor growth model is based on a reaction-diffusion framework. Model personalization relies only on a forward model for the growth process and on image likelihood. We take advantage of an adaptive sparse grid approximation for efficient inference via Markov Chain Monte Carlo sampling. The approach can be used for integrating information from different multi-modal imaging protocols and can easily be adapted to other tumor growth models.German Academy of Sciences Leopoldina (Fellowship Programme LPDS 2009-10)Academy of Finland (133611)National Institutes of Health (U.S.) (NIBIB NAMIC U54-EB005149)National Institutes of Health (U.S.) (NCRR NAC P41- RR13218)National Institutes of Health (U.S.) (NINDS R01-NS051826)National Institutes of Health (U.S.) (NIH R01-NS052585)National Institutes of Health (U.S.) (NIH R01-EB006758)National Institutes of Health (U.S.) (NIH R01-EB009051)National Institutes of Health (U.S.) (NIH P41-RR014075)National Science Foundation (U.S.) (CAREER Award 0642971

Predictive model of biliocystic communication in liver hydatid cysts using classification and regression tree analysis

<p>Abstract</p> <p>Background</p> <p>Incidence of liver hydatid cyst (LHC) rupture ranged 15%-40% of all cases and most of them concern the bile duct tree. Patients with biliocystic communication (BCC) had specific clinic and therapeutic aspect. The purpose of this study was to determine witch patients with LHC may develop BCC using classification and regression tree (CART) analysis</p> <p>Methods</p> <p>A retrospective study of 672 patients with liver hydatid cyst treated at the surgery department "A" at Ibn Sina University Hospital, Rabat Morocco. Four-teen risk factors for BCC occurrence were entered into CART analysis to build an algorithm that can predict at the best way the occurrence of BCC.</p> <p>Results</p> <p><b>I</b>ncidence of BCC was 24.5%. Subgroups with high risk were patients with jaundice and thick pericyst risk at 73.2% and patients with thick pericyst, with no jaundice 36.5 years and younger with no past history of LHC risk at 40.5%. Our developed CART model has sensitivity at 39.6%, specificity at 93.3%, positive predictive value at 65.6%, a negative predictive value at 82.6% and accuracy of good classification at 80.1%. Discriminating ability of the model was good 82%.</p> <p>Conclusion</p> <p>we developed a simple classification tool to identify LHC patients with high risk BCC during a routine clinic visit (only on clinical history and examination followed by an ultrasonography). Predictive factors were based on pericyst aspect, jaundice, age, past history of liver hydatidosis and morphological Gharbi cyst aspect. We think that this classification can be useful with efficacy to direct patients at appropriated medical struct's.</p

Oral health and social and emotional well-being in a birth cohort of Aboriginal Australian young adults

Background: Social and emotional well-being is an important component of overall health. In the Indigenous Australian context, risk indicators of poor social and emotional well-being include social determinants such as poor education, employment, income and housing as well as substance use, racial discrimination and cultural knowledge. This study sought to investigate associations between oral health-related factors and social and emotional well-being in a birth cohort of young Aboriginal adults residing in the northern region of Australia's Northern Territory. Methods: Data were collected on five validated domains of social and emotional well-being: anxiety, resilience, depression, suicide and overall mental health. Independent variables included socio-demographics, dental health behaviour, dental disease experience, oral health-related quality of life, substance use, racial discrimination and cultural knowledge. Results: After adjusting for other covariates, poor oral health-related items were associated with each of the social and emotional well-being domains. Specifically, anxiety was associated with being female, having one or more decayed teeth and racial discrimination. Resilience was associated with being male, having a job, owning a toothbrush, having one or more filled teeth and knowing a lot about Indigenous culture; while being female, having experienced dental pain in the past year, use of alcohol, use of marijuana and racial discrimination were associated with depression. Suicide was associated with being female, having experience of untreated dental decay and racial discrimination; while being female, having experience of dental disease in one or more teeth, being dissatisfied about dental appearance and racial discrimination were associated with poor mental health. Conclusion: The results suggest there may be value in including oral health-related initiatives when exploring the role of physical conditions on Indigenous social and emotional well-being.Lisa M Jamieson, Yin C Paradies, Wendy Gunthorpe, Sheree J Cairney and Susan M Sayer

Fed-batch cultivation of bakers' yeast: Effect of nutrient depletion and heat stress on cell composition

The physiology of a commercial strain of bakers' yeast was studied in terms of the cell composition under different growth conditions and of its response to stress. The study comprised fed-batch experiments since this is the system used in bakers' yeast industry. The classical fed-batch fermentation procedure was modified in that the yeast cells were continuously grown to a steady-state at a dilution rate of 0.1/h in order to achieve more or less the same initial starting point in terms of cell composition. This steady-state culture was then switched to fed-batch concomitantly with exposure to stress. The highest amount of trehalose accumulation was achieved when nutrient depletion and heat stress were applied concomitantly. The highest amount of trehalose, 12 %, was attained in cells stressed by both nitrogen depletion and heat stress. The protein content remained constant, although with some oscillations, at a value of 30 % throughout this dual stress experiment

Thorium(IV) and uranium(VI) sorption studies on octacarboxymethyl-C-methylcalix[4]resorcinarene impregnated on a polymeric support

The impregnation of octacarboxymethyl-C-methylcalix[4]resorcinarene into a polymeric matrix, Amberlite XAD-4, is reported and was characterized by infrared spectroscopy. The sorption capacity of the impregnated resin is 0.34 x 10(-3) mol g(-1). The resin was used for the sorption of thorium(IV) and uranium(VI) from aqueous solution. The properties of capacity, pH effect, and breakthrough curves of the impregnated resin were investigated. The capacity of the resin for Th(IV) and U(VI) was found to be 0.29 and 0.27 x 10(-3) mol g(-1), respectively. The metal ions were eluted with 0.4-2 M HCl or HNO3. Chromatographic separation of Th(W) and U(VI) was accomplished by adjustment of pH to 3.0 and 6.0, respectively. The impregnated resin exhibits a high chemical stability, reusability and fast equilibration. Separation of Th(W) and U(VI) from other metal cations in synthetic solution was achieved. (C) 2003 Elsevier Science B.V. All rights reserved

Semi-Automatic Brain Tumor Segmentation By Constrained MRFs using Structural Trajectories

Abstract. Quantifying volume and growth of a brain tumor is a primary prognostic measure and hence has received much attention in the medical imaging community. Most methods have sought a fully automatic segmentation, but the variability in shape and appearance of brain tumor has limited their success and further adoption in the clinic. In reaction, we present a semi-automatic brain tumor segmentation framework for multi-channel magnetic resonance (MR) images. This framework does not require prior model construction and only requires manual labels on one automatically selected slice. All other slices are labeled by an iterative multi-label Markov random field optimization with hard constraints. Structural trajectories—the medical image analog to optical flow—and 3D image over-segmentation are used to capture pixel correspondences between consecutive slices for pixel labeling. We show robustness and effectiveness through an evaluation on the 2012 MICCAI BRATS Challenge Dataset; our results indicate superior performance to baselines and demonstrate the utility of the constrained MRF formulation.