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Background: Interstitial lung abnormalities (ILA) are common in participants of lung cancer screening trials and broad population-based cohorts. They are associated with increased mortality, but less is known about disease specific morbidity and healthcare utilisation in individuals with ILA., Methods: We included all participants from the screening arm of the Danish Lung Cancer Screening Trial with available baseline CT scan data (n = 1990) in this cohort study. The baseline scan was scored for the presence of ILA and patients were followed for up to 12 years. Data about all hospital admissions, primary healthcare visits and medicine prescriptions were collected from the Danish National Health Registries and used to determine the participants' disease specific morbidity and healthcare utilisation using Cox proportional hazards models., Results: The 332 (16.7%) participants with ILA were more likely to be diagnosed with one of several respiratory diseases, including interstitial lung disease (HR: 4.9, 95% CI: 1.8-13.3, p = 0.008), COPD (HR: 1.7, 95% CI: 1.2-2.3, p = 0.01), pneumonia (HR: 2.0, 95% CI: 1.4-2.7, p <  0.001), lung cancer (HR: 2.7, 95% CI: 1.8-4.0, p <  0.001) and respiratory failure (HR: 1.8, 95% CI: 1.1-3.0, p = 0.03) compared with participants without ILA. These findings were confirmed by increased hospital admission rates with these diagnoses and more frequent prescriptions for inhalation medicine and antibiotics in participants with ILA., Conclusions: Individuals with ILA are more likely to receive a diagnosis and treatment for several respiratory diseases, including interstitial lung disease, COPD, pneumonia, lung cancer and respiratory failure during long-term follow-up.

Rationale: As of April 2015, participants in the Danish Lung Cancer Screening Trial had been followed for at least 5 years since their last screening., Objectives: Mortality, causes of death, and lung cancer findings are reported to explore the effect of computed tomography (CT) screening., Methods: A total of 4,104 participants aged 50-70 years at the time of inclusion and with a minimum 20 pack-years of smoking were randomized to have five annual low-dose CT scans (study group) or no screening (control group)., Measurements and Main Results: Follow-up information regarding date and cause of death, lung cancer diagnosis, cancer stage, and histology was obtained from national registries. No differences between the two groups in lung cancer mortality (hazard ratio, 1.03; 95% confidence interval, 0.66-1.6; P = 0.888) or all-cause mortality (hazard ratio, 1.02; 95% confidence interval, 0.82-1.27; P = 0.867) were observed. More cancers were found in the screening group than in the no-screening group (100 vs. 53, respectively; P < 0.001), particularly adenocarcinomas (58 vs. 18, respectively; P < 0.001). More early-stage cancers (stages I and II, 54 vs. 10, respectively; P < 0.001) and stage IIIa cancers (15 vs. 3, respectively; P = 0.009) were found in the screening group than in the control group. Stage IV cancers were nonsignificantly more frequent in the control group than in the screening group (32 vs. 23, respectively; P = 0.278). For the highest-stage cancers (T4N3M1, 21 vs. 8, respectively; P = 0.025), this difference was statistically significant, indicating an absolute stage shift. Older participants, those with chronic obstructive pulmonary disease, and those with more than 35 pack-years of smoking had a significantly increased risk of death due to lung cancer, with nonsignificantly fewer deaths in the screening group., Conclusions: No statistically significant effects of CT screening on lung cancer mortality were found, but the results of post hoc high-risk subgroup analyses showed nonsignificant trends that seem to be in good agreement with the results of the National Lung Screening Trial. Clinical trial registered with www.clinicaltrials.gov (NCT00496977).

This paper presents a method for airway tree segmentation that uses a combination of a trained airway appearance model, vessel and airway orientation information, and region growing. We propose a voxel classification approach for the appearance model, which uses a classifier that is trained to differentiate between airway and non-airway voxels. This is in contrast to previous works that use either intensity alone or hand crafted models of airway appearance. We show that the appearance model can be trained with a set of easily acquired, incomplete, airway tree segmentations. A vessel orientation similarity measure is introduced, which indicates how similar the orientation of an airway candidate is to the orientation of the neighboring vessel. We use this vessel orientation similarity measure to overcome regions in the airway tree that have a low response from the appearance model. The proposed method is evaluated on 250 low dose computed tomography images from a lung cancer screening trial. Our experiments showed that applying the region growing algorithm on the airway appearance model produces more complete airway segmentations, leading to on average 20% longer trees, and 50% less leakage. When combining the airway appearance model with vessel orientation similarity, the improvement is even more significant (p<0.01) than only using the airway appearance model, with on average 7% increase in the total length of branches extracted correctly., (Copyright 2010 Elsevier B.V. All rights reserved.)

The standard approaches to analyzing emphysema in computed tomography (CT) images are visual inspection and the relative area of voxels below a threshold (RA). The former approach is subjective and impractical in a large data set and the latter relies on a single threshold and independent voxel information, ignoring any spatial correlation in intensities. In recent years, supervised learning on texture features has been investigated as an alternative to these approaches, showing good results. However, supervised learning requires labeled samples, and these samples are often obtained via subjective and time consuming visual scoring done by human experts. In this work, we investigate the possibility of applying supervised learning using texture measures on random CT samples where the labels are based on external, non-CT measures. We are not targeting emphysema directly, instead we focus on learning textural differences that discriminate subjects with chronic obstructive pulmonary disease (COPD) from healthy smokers, and it is expected that emphysema plays a major part in this. The proposed texture based approach achieves an 69% classification accuracy which is significantly better than RA's 55% accuracy.

We present a new image registration based method for monitoring regional disease progression in longitudinal image studies of lung disease. A free-form image registration technique is used to match a baseline 3D CT lung scan onto a following scan. Areas with lower intensity in the following scan compared with intensities in the deformed baseline image indicate local loss of lung tissue that is associated with progression of emphysema. To account for differences in lung intensity owing to differences in the inspiration level in the two scans rather than disease progression, we propose to adjust the density of lung tissue with respect to local expansion or compression such that the total weight of the lungs is preserved during deformation. Our method provides a good estimation of regional destruction of lung tissue for subjects with a significant difference in inspiration level between CT scans and may result in a more sensitive measure of disease progression than standard quantitative CT measures.

This study presents a fully automatic, data-driven approach for texture-based quantitative analysis of chronic obstructive pulmonary disease (COPD) in pulmonary computed tomography (CT) images. The approach uses supervised learning where the class labels are, in contrast to previous work, based on measured lung function instead of on manually annotated regions of interest (ROIs). A quantitative measure of COPD is obtained by fusing COPD probabilities computed in ROIs within the lung fields where the individual ROI probabilities are computed using a k nearest neighbor (kNN) classifier. The distance between two ROIs in the kNN classifier is computed as the textural dissimilarity between the ROIs, where the ROI texture is described by histograms of filter responses from a multi-scale, rotation invariant Gaussian filter bank. The method was trained on 400 images from a lung cancer screening trial and subsequently applied to classify 200 independent images from the same screening trial. The texture-based measure was significantly better at discriminating between subjects with and without COPD than were the two most common quantitative measures of COPD in the literature, which are based on density. The proposed measure achieved an area under the receiver operating characteristic curve (AUC) of 0.713 whereas the best performing density measure achieved an AUC of 0.598. Further, the proposed measure is as reproducible as the density measures, and there were indications that it correlates better with lung function and is less influenced by inspiration level. [ABSTRACT FROM PUBLISHER]