Lung CAD systems require the ability to classify a variety of pulmonary structures as part of the diagnostic process. The purpose of this work was to develop a methodology for fully automated voxel-by-voxel classification of airways, fissures, nodules, and vessels from chest CT images using a single feature set and classification method. Twenty-nine thin section CT scans were obtained from the Lung Image Database Consortium (LIDC). Multiple radiologists labeled voxels corresponding to the following structures: airways (trachea to 6th generation), major and minor lobar fissures, nodules, and vessels (hilum to peripheral), and normal lung parenchyma. The labeled data was used in conjunction with a supervised machine learning approach (AdaBoost) to train a set of ensemble classifiers. Each ensemble classifier was trained to detect voxels part of a specific structure (either airway, fissure, nodule, vessel, or parenchyma). The feature set consisted of voxel attenuation and a small number of features based on the eigenvalues of the Hessian matrix (used to differentiate structures by shape). When each ensemble classifier was composed of 20 weak classifiers, the AUC values for the airway, fissure, nodule, vessel, and parenchyma classifiers were 0.984 ± 0.011, 0.949 ± 0.009, 0.945 ± 0.018, 0.953 ± 0.016, and 0.931± 0.015 respectively. The strong results suggest that this could be an effective input to higher-level anatomical based segmentation models with the potential to improve CAD performance.

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Rationale and Objectives To retrospectively investigate the prevalence of tracheal collapse in an emphysema cohort. The occurrence of a large degree of tracheal collapse might have important implications for the clinical management of respiratory symptoms and air trapping in emphysema patients. Material and Methods Paired full-inspiratory and end-expiratory thin-section volumetric CT scans were available for 1071 long term smokers with clinically and physiologically confirmed emphysema. The percent reduction in the cross-sectional tracheal lumen area from full inspiration to end-expiration was automatically computed at 2.5 mm intervals along the centerline of the trachea using customized software. Results The maximum tracheal collapse did not follow a normal distribution in the emphysema cohort (P<0.0001, skewness/kurtosis tests for normality); the median collapse was 18%, intraquartile range 11% to 30%. Statistically significant differences were found in the distribution of maximal collapse by gender (P<0.005, Wilcoxon rank-sum test). Overall, 10.5% of males and 17.1% of females showed evidence of tracheomalacia based on the criteria of a ≥50% reduction in the cross-sectional tracheal lumen area at end-expiration. Conclusion Our study offers insights into the prevalence of tracheal collapse in a cohort of emphysema patients; future work is needed to determine the possible relationship between tracheal collapse and air trapping in emphysema subjects.

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We investigated effects of prevalence and case distribution on radiologist diagnostic performance as measured by area under the receiver operating characteristic curve (AUC) and sensitivity-specificity in lab-based reader studies evaluating imaging devices. Our retrospective reader studies compared full-field digital mammography (FFDM) to screen-film mammography (SFM) for women with dense breasts. Mammograms were acquired from the prospective Digital Mammographic Imaging Screening Trial. We performed five reader studies that differed in terms of cancer prevalence and the distribution of noncancers. Twenty radiologists participated in each reader study. Using split-plot study designs, we collected recall decisions and multilevel scores from the radiologists for calculating sensitivity, specificity, and AUC. Differences in reader-averaged AUCs slightly favored SFM over FFDM (biggest AUC difference: 0.047, SE ¼ 0.023, p ¼ 0.047), where standard error accounts for reader and case variability. The differences were not significant at a level of 0.01 (0.05/5 reader studies). The differences in sensitivities and specificities were also indeterminate. Prevalence had little effect on AUC (largest difference: 0.02), whereas sensitivity increased and specificity decreased as prevalence increased. We found that AUC is robust to changes in prevalence, while radiologists were more aggressive with recall decisions as prevalence increased.