Active contour approach for accurate quantitative airway analysis

Chronic airway disease causes structural changes in the lungs including peribronchial thickening and airway dilatation. Multi-detector computed tomography (CT) yields detailed near-isotropic images of the lungs, and thus the potential to obtain quantitative measurements of lumen diameter and airway wall thickness. Such measurements would allow standardized assessment, and physicians to diagnose and locate airway abnormalities, adapt treatment, and monitor progress over time. However, due to the sheer number of airways per patient, systematic analysis is infeasible in routine clinical practice without automation. We have developed an automated and real-time method based on active contours to estimate both airway lumen and wall dimensions; the method does not require manual contour initialization but only a starting point on the targeted airway. While the lumen contour segmentation is purely region-based, the estimation of the outer diameter considers the inner wall segmentation as well as local intensity variation, in order anticipate the presence of nearby arteries and exclude them. These properties make the method more robust than the Full-Width Half Maximum (FWHM) approach. Results are demonstrated on a phantom dataset with known dimensions and on a human dataset where the automated measurements are compared against two human operators. The average error on the phantom measurements was 0.10mm and 0.14mm for inner and outer diameters, showing sub-voxel accuracy. Similarly, the mean variation from the average manual measurement was 0.14mm and 0.18mm for inner and outer diameters respectively

Variation within the Huntington's Disease Gene Influences Normal Brain Structure

Genetics of the variability of normal and diseased brain structure largely remains to be elucidated. Expansions of certain trinucleotide repeats cause neurodegenerative disorders of which Huntington's disease constitutes the most common example. Here, we test the hypothesis that variation within the IT15 gene on chromosome 4, whose expansion causes Huntington's disease, influences normal human brain structure. In 278 normal subjects, we determined CAG repeat length within the IT15 gene on chromosome 4 and analyzed high-resolution T1-weighted magnetic resonance images by the use of voxel-based morphometry. We found an increase of GM with increasing long CAG repeat and its interaction with age within the pallidum, which is involved in Huntington's disease. Our study demonstrates that a certain trinucleotide repeat influences normal brain structure in humans. This result may have important implications for the understanding of both the healthy and diseased brain

Identification of an autoantibody panel to separate lung cancer from smokers and nonsmokers

<p>Abstract</p> <p>Background</p> <p>Sera from lung cancer patients contain autoantibodies that react with tumor associated antigens (TAAs) that reflect genetic over-expression, mutation, or other anomalies of cell cycle, growth, signaling, and metabolism pathways.</p> <p>Methods</p> <p>We performed immunoassays to detect autoantibodies to ten tumor associated antigens (TAAs) selected on the basis of previous studies showing that they had preferential specificity for certain cancers. Sera examined were from lung cancer patients (22); smokers with ground-glass opacities (GGOs) (46), benign solid nodules (55), or normal CTs (35); and normal non-smokers (36). Logistic regression models based on the antibody biomarker levels among the high risk and lung cancer groups were developed to identify the combinations of biomarkers that predict lung cancer in these cohorts.</p> <p>Results</p> <p>Statistically significant differences in the distributions of each of the biomarkers were identified among all five groups. Using Receiver Operating Characteristic (ROC) curves based on age, c-myc, Cyclin A, Cyclin B1, Cyclin D1, CDK2, and survivin, we obtained a sensitivity = 81% and specificity = 97% for the classification of cancer vs smokers(no nodules, solid nodules, or GGO) and correctly predicted 31/36 healthy controls as noncancer.</p> <p>Conclusion</p> <p>A pattern of autoantibody reactivity to TAAs may distinguish patients with lung cancer versus smokers with normal CTs, stable solid nodules, ground glass opacities, or normal healthy never smokers.</p