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

GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging

Deep Learning (DL) has greatly highlighted the potential impact of optimized machine learning in both the scientific and clinical communities. The advent of open-source DL libraries from major industrial entities, such as TensorFlow (Google), PyTorch (Facebook), and MXNet (Apache), further contributes to DL promises on the democratization of computational analytics. However, increased technical and specialized background is required to develop DL algorithms, and the variability of implementation details hinders their reproducibility. Towards lowering the barrier and making the mechanism of DL development, training, and inference more stable, reproducible, and scalable, without requiring an extensive technical background, this manuscript proposes the Generally Nuanced Deep Learning Framework (GaNDLF). With built-in support for k-fold cross-validation, data augmentation, multiple modalities and output classes, and multi-GPU training, as well as the ability to work with both radiographic and histologic imaging, GaNDLF aims to provide an end-to-end solution for all DL-related tasks, to tackle problems in medical imaging and provide a robust application framework for deployment in clinical workflows

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

Cognitive group therapy for depressive students: The case study

The aims of this study were to assess whether a course of cognitive group therapy could help depressed students and to assess whether assimilation analysis offers a useful way of analysing students' progress through therapy. “Johanna” was a patient in a group that was designed for depressive students who had difficulties with their studies. The assimilation of Johanna's problematic experience progressed as the meetings continued from level one (unpleasant thoughts) to level six (solving the problem). Johanna's problematic experience manifested itself as severe and excessive criticism towards herself and her study performance. As the group meetings progressed, Johanna found a new kind of tolerance that increased her determination and assertiveness regarding the studies. The dialogical structure of Johanna's problematic experience changed: she found hope and she was more assertive after the process. The results indicated that this kind of psycho-educational group therapy was an effective method for treating depression. The assimilation analysis offered a useful way of analysing the therapy process

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

A kinetic investigation on the pyrolysis of Seguruk asphaltite

The pyrolysis of Seguruk asphaltite has been investigated using thermogravimetric analysis at atmospheric pressure between 293 to 1223 K at different linear heating rates of 5, 10 and 20 K min(-1) under nitrogen as ambient gas. There was a two-stage thermal decomposition. Thermal decomposition started around 630 K for stage 1 for the slowest heating rate. On the other hand, for the same heating rate and stage 2, thermal decomposition started around 950 K. These values were shifted to higher temperatures with increasing heating rate. In this study, two different Coats-Redfern methods were applied to thermal degradation of Seguruk asphaltite

Polymer supported humic acid for separation and preconcentration of thorium(IV)

The resin impregnating humic acid (HA) onto XAD-4 has been prepared to investigate adsorption behaviour of Th(IV). The characterization of the resulting resin has been carried out by infrared spectral data and sorption capacity. Maximum adsorption capacity of Th(IV) on the resin is found to be 1.51 X 10(-4) mol g(-1) at pH 4. The sorbent was found to possess a high selectivity for Th(IV) with an optimum extraction pH around 3-7. Recoveries for Th(IV) determined prior to breakthrough were found to be quantitative (96-99%). The resin exhibits good chemical stability, reuseability, and a faster rate of equilibration for Th(IV) determination. The influence of several ions as interferents is discussed. The method has been successfully applied for the separation of Th(IV) in synthetic mixtures