Lung cancer screening: national consensus is an important step toward implementation

non

Extraction of airway trees using multiple hypothesis tracking and template matching

Knowledge of airway tree morphology has important clinical applications in diagnosis of chronic obstructive pulmonary disease. We present an automatic tree extraction method based on multiple hypothesis tracking and template matching for this purpose and evaluate its performance on chest CT images. The method is adapted from a semi-automatic method devised for vessel segmentation. Idealized tubular templates are constructed that match airway probability obtained from a trained classifier and ranked based on their relative significance. Several such regularly spaced templates form the local hypotheses used in constructing a multiple hypothesis tree, which is then traversed to reach decisions. The proposed modifications remove the need for local thresholding of hypotheses as decisions are made entirely based on statistical comparisons involving the hypothesis tree. The results show improvements in performance when compared to the original method and region growing on intensity images. We also compare the method with region growing on the probability images, where the presented method does not show substantial improvement, but we expect it to be less sensitive to local anomalies in the data.Comment: 12 pages. Presented at the MICCAI Pulmonary Image Analysis Workshop, Athens, Greece, 201

Transfer learning for multicenter classification of chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a lung disease which can be quantified using chest computed tomography (CT) scans. Recent studies have shown that COPD can be automatically diagnosed using weakly supervised learning of intensity and texture distributions. However, up till now such classifiers have only been evaluated on scans from a single domain, and it is unclear whether they would generalize across domains, such as different scanners or scanning protocols. To address this problem, we investigate classification of COPD in a multi-center dataset with a total of 803 scans from three different centers, four different scanners, with heterogenous subject distributions. Our method is based on Gaussian texture features, and a weighted logistic classifier, which increases the weights of samples similar to the test data. We show that Gaussian texture features outperform intensity features previously used in multi-center classification tasks. We also show that a weighting strategy based on a classifier that is trained to discriminate between scans from different domains, can further improve the results. To encourage further research into transfer learning methods for classification of COPD, upon acceptance of the paper we will release two feature datasets used in this study on http://bigr.nl/research/projects/copdComment: Accepted at Journal of Biomedical and Health Informatic

Large Gliadin Peptides Detected in the Pancreas of NOD and Healthy Mice following Oral Administration

Gluten promotes type 1 diabetes in nonobese diabetic (NOD) mice and likely also in humans. In NOD mice and in non-diabetes-prone mice, it induces inflammation in the pancreatic lymph nodes, suggesting that gluten can initiate inflammation locally. Further, gliadin fragments stimulate insulin secretion from beta cells directly. We hypothesized that gluten fragments may cross the intestinal barrier to be distributed to organs other than the gut. If present in pancreas, gliadin could interact directly with the immune system and the beta cells to initiate diabetes development. We orally and intravenously administered 33-mer and 19-mer gliadin peptide to NOD, BALB/c, and C57BL/6 mice and found that the peptides readily crossed the intestinal barrier in all strains. Several degradation products were found in the pancreas by mass spectroscopy. Notably, the exocrine pancreas incorporated large amounts of radioactive label shortly after administration of the peptides. The study demonstrates that, even in normal animals, large gliadin fragments can reach the pancreas. If applicable to humans, the increased gut permeability in prediabetes and type 1 diabetes patients could expose beta cells directly to gliadin fragments. Here they could initiate inflammation and induce beta cell stress and thus contribute to the development of type 1 diabetes

EU Policy on Lung Cancer CT Screening 2017.

BackgroundLung cancer kills more Europeans than any other cancer. In 2013, 269,000 citizens of the EU-28 died from this disease. Lung cancer CT screening has the potential to detect lung cancer at an early stage and improve mortality. All of the randomised controlled trials and cohort low-dose CT (LDCT) screening trials across the world have identified very early stage disease (∼70%); the majority of these LDCT trial patients were suitable for surgical interventions and had a good clinical outcome. The 10-year survival in CT screen-detected cancer was shown to be even higher than the 5-year survival for early stage disease in clinical practice at 88%.MethodsSetting up of an EU Commission expert group can be done under Article 168(2) of the Treaty on the Functioning of the European Union, to develop policy and recommendation for Lung cancer CT screening. The Expert Group would undertake: (a) assist the Commission in the drawing up policy documents, including guidelines and recommendations; (b) advise the Commission in the implementation of Union actions on screening and suggest improvements to the measures taken; (c) advise the Commission in the monitoring, evaluation and dissemination of the results of measures taken at Union and national level.ResultsThis EU Expert Group on lung cancer screening should be set up by the EU Commission to support the implementation and suggest recommendations for the lung cancer screening policy by 2019/2020.ConclusionReduce lung cancer in Europe by undertaking a well-organised lung cancer CT screening programme