42 research outputs found
Anatomy, Biology and Concepts, Pertaining to Lung Cancer Stage Classification
AbstractThe proposed lung cancer stage classification system remains grounded in anatomic characteristics, although the large patient database contributing to this revision has dramatically expanded our body of knowledge. Predictably this has led to increased complexity due to the identification of an increasing number of subpopulations of patients. Patterns of clinical presentation characterizing these subgroups may provide clues about the propensity of tumors within a subgroup toward a particular pattern of biologic behavior. This article explores concepts regarding tumor biology that can be applied to the anatomically based new staging system
Thoroughness of Mediastinal Staging in Stage IIIA Non-small Cell Lung Cancer
IntroductionGuidelines recommend that patients with clinical stage IIIA non-small cell lung cancer (NSCLC) undergo histologic confirmation of pathologic lymph nodes. Studies have suggested that invasive mediastinal staging is underutilized, although practice patterns have not been rigorously evaluated.MethodsWe used the Surveillance, Epidemiology, and End Results-Medicare database to identify patients with stage IIIA NSCLC diagnosed from 1998 through 2005. Invasive staging and use of positron emission tomography (PET) scanning were assessed using Medicare claims. Multivariable logistic regression was used to identify patient characteristics associated with use of invasive staging.ResultsOf 7583 stage IIIA NSCLC patients, 1678 (22%) underwent invasive staging. Patients who received curative intent cancer treatment were more likely to undergo invasive staging than patients who did not receive cancer-specific therapy (30% versus 9.8%, adjusted odds ratio, 3.31; 95% confidence interval, 2.78–3.95). The oldest patients (age, 85–94 years) were less likely to receive invasive staging than the youngest (age, 67–69 years; 27.6% versus 11.9%; odds ratio, 0.46; 95% confidence interval, 0.34–0.61). Sex, marital status, income, and race were not associated with the use of the invasive staging. The use of invasive staging was stable throughout the study period, despite an increase in the use of PET scanning from less than 10% of patients before 2000 to almost 70% in 2005.ConclusionNearly 80% of Medicare beneficiaries with stage IIIA NSCLC do not receive guideline adherent mediastinal staging; this failure cannot be entirely explained by patient factors or a reliance on PET imaging. Incentives to encourage use of invasive staging may improve care
15. ゼミノームの放射線治療成績(第5回佐藤外科例会,第488回千葉医学会例会)
Performance of the MDSINE inference algorithms on simulated data with different sequencing depths. Simulations assumed an underlying dynamical systems model with ten species observed over 30Â days with 27 time points sampled and an invading species at day 10. Performance of the four MDSINE inference algorithms, maximum likelihood ridge regression (MLRR), maximum likelihood constrained ridge regression (MLCRR), Bayesian adaptive lasso (BAL), and Bayesian variable selection (BVS), were compared. Algorithm performance was assessed using four different metrics: (a) root mean-square error (RMSE) for microbial growth rates; (b) RMSE for microbial interaction parameters; (c) RMSE for prediction of microbe trajectories on held-out subjects given only initial microbe concentrations for the held-out subject; and (d) area under the receiver operator curve (AUC ROC) for the underlying microbial interaction network. Lower RMSE values indicate superior performance, whereas higher AUC ROC values indicate superior performance. (PDF 182 kb
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MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses
Predicting dynamics of host-microbial ecosystems is crucial for the rational design of bacteriotherapies. We present MDSINE, a suite of algorithms for inferring dynamical systems models from microbiome time-series data and predicting temporal behaviors. Using simulated data, we demonstrate that MDSINE significantly outperforms the existing inference method. We then show MDSINE’s utility on two new gnotobiotic mice datasets, investigating infection with Clostridium difficile and an immune-modulatory probiotic. Using these datasets, we demonstrate new capabilities, including accurate forecasting of microbial dynamics, prediction of stable sub-communities that inhibit pathogen growth, and identification of bacteria most crucial to community integrity in response to perturbations. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-0980-6) contains supplementary material, which is available to authorized users
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Year Book of Pulmonary Disease 2016
The Year Book of Pulmonary Disease brings you abstracts of the articles that reported the year's breakthrough developments in pulmonary disease carefully selected from more than 500 journals worldwide. Expert commentaries evaluate the clinical importance of each article and discuss its application to your practice. Topics such as Asthma and Cystic Fibrosis, Chronic Obstructive Pulmonary Disease, Lung Cancer, Community-Acquired Pneumonia, Lung Transplantation, Sleep Disorders, and Critical Care Medicine are represented highlighting the most current and relevant articles in the field