Subtle interstitial changes in the lung parenchyma of smokers, known as Interstitial Lung Abnormalities (ILA), have been associated with clinical outcomes, including mortality, even in the absence of Interstitial Lung Disease (ILD). Although several methods have been proposed for the automatic identification of more advanced Interstitial Lung Disease (ILD) patterns, few have tackled ILA, which likely precedes the development ILD in some cases. In this context, we propose a novel methodology for automated identification and classification of ILA patterns in computed tomography (CT) images. The proposed method is an ensemble of deep convolutional neural networks (CNNs) that detect more discriminative features by incorporating two, two-and-a-half and three- dimensional architectures, thereby enabling more accurate classification. This technique is implemented by first training each individual CNN, and then combining its output responses to form the overall ensemble output. To train and test the system we used 37424 radiographic tissue samples corresponding to eight different parenchymal feature classes from 208 CT scans. The resulting ensemble performance including an average sensitivity of 91,41% and average specificity of 98,18% suggests it is potentially a viable method to identify radiographic patterns that precede the development of ILD.

BACKGROUND: Relative enlargement of the pulmonary artery (PA) on chest CT imaging is associated with respiratory exacerbations in patients with COPD or cystic fibrosis. We sought to determine whether similar findings were present in patients with asthma and whether these findings were explained by differences in ventricular size. METHODS: We measured the PA and aorta diameters in 233 individuals from the Severe Asthma Research Program III cohort. We also estimated right, left, and total epicardial cardiac ventricular volume indices (eERVVI, eELVVI, and eETVVI, respectively). Associations between the cardiac and PA measures (PA-to-aorta [PA/A] ratio, eERVVI-to-eELVVI [eRV/eLV] ratio, eERVVI, eELVVI, eETVVI) and clinical measures of asthma severity were assessed by Pearson correlation, and associations with asthma severity and exacerbation rate were evaluated by multivariable linear and zero-inflated negative binomial regression. RESULTS: Asthma severity was associated with smaller ventricular volumes. For example, those with severe asthma had 36.1 mL/m2 smaller eETVVI than healthy control subjects (P = .003) and 14.1 mL/m2 smaller eETVVI than those with mild/moderate disease (P = .011). Smaller ventricular volumes were also associated with a higher rate of asthma exacerbations, both retrospectively and prospectively. For example, those with an eETVVI less than the median had a 57% higher rate of exacerbations during follow-up than those with eETVVI greater than the median (P = .020). Neither PA/A nor eRV/eLV was associated with asthma severity or exacerbations. CONCLUSIONS: In patients with asthma, smaller cardiac ventricular size may be associated with more severe disease and a higher rate of asthma exacerbations. TRIAL REGISTRY: ClinicalTrials.gov; No.: NCT01761630; URL: www.clinicaltrials.gov.

BACKGROUND: Guidelines recommend invasive mediastinal staging for patients with non-small cell lung cancer and a "central" tumor. However, there is no consensus definition for central location. As such, the decision to perform invasive staging largely remains on an empirical foundation. RESEARCH QUESTION: Should patients with peripheral T1 lung tumors undergo invasive mediastinal staging? STUDY DESIGN AND METHODS: All participants with a screen-detected cancer with a solid component between 8 and 30 mm were identified from the National Lung Screening Trial. After translation of CT data, cancer location was identified and the X, Y, Z coordinates were determined as well as distance from the main carina. A multivariable logistic regression model was constructed to evaluate for predictors associated with lymph node metastasis. RESULTS: Three hundred thirty-two participants were identified, of which 69 had lymph node involvement (20.8%). Of those with lymph node metastasis, 39.1% were N2. There was no difference in rate of lymph node metastasis based on tumor size (OR, 1.03; P = .248). There was also no statistical difference in rate of lymph node metastasis based on location, either by distance from the carina (OR, 0.99; P = .156) or tumor coordinates (X: P = .180; Y: P = .311; Z: P = .292). When adjusted for age, sex, histology, and smoking history, there was no change in the magnitude of the risk, and tests of significance were not altered. INTERPRETATION: Our data indicate a high rate of N2 metastasis among T1 tumors and no significant relationship between tumor diameter or location. This suggests that patients with small, peripheral lung cancers may benefit from invasive mediastinal staging.

Accurately and precisely characterizing the morphology of small pulmonary structures from Computed Tomography (CT) images, such as airways and vessels, is becoming of great importance for diagnosis of pulmonary diseases. The smaller conducting airways are the major site of increased airflow resistance in chronic obstructive pulmonary disease (COPD), while accurately sizing vessels can help identify arterial and venous changes in lung regions that may determine future disorders. However, traditional methods are often limited due to image resolution and artifacts. We propose a Convolutional Neural Regressor (CNR) that provides cross-sectional measurement of airway lumen, airway wall thickness, and vessel radius. CNR is trained with data created by a generative model of synthetic structures which is used in combination with Simulated and Unsupervised Generative Adversarial Network (SimGAN) to create simulated and refined airways and vessels with known ground-truth. For validation, we first use synthetically generated airways and vessels produced by the proposed generative model to compute the relative error and directly evaluate the accuracy of CNR in comparison with traditional methods. Then, in-vivo validation is performed by analyzing the association between the percentage of the predicted forced expiratory volume in one second (FEV1%) and the value of the Pi10 parameter, two well-known measures of lung function and airway disease, for airways. For vessels, we assess the correlation between our estimate of the small-vessel blood volume and the lungs' diffusing capacity for carbon monoxide (DLCO). The results demonstrate that Convolutional Neural Networks (CNNs) provide a promising direction for accurately measuring vessels and airways on chest CT images with physiological correlates.

Individuals with the genetic disorder alpha-1 antitrypsin deficiency (AATD) are at risk of developing lung and liver disease. Patient induced pluripotent stem cells (iPSCs) have been found to model features of AATD pathogenesis but only a handful of AATD patient iPSC lines have been published. To capture the significant phenotypic diversity of the patient population, we describe here the establishment and characterization of a curated repository of AATD iPSCs with associated disease-relevant clinical data. To highlight the utility of the repository, we selected a subset of iPSC lines for functional characterization. Selected lines were differentiated to generate both hepatic and lung cell lineages and analyzed by RNA sequencing. In addition, two iPSC lines were targeted using CRISPR/Cas9 editing to accomplish scarless repair. Repository iPSCs are available to investigators for studies of disease pathogenesis and therapeutic discovery.

The term interstitial lung abnormalities refers to specific CT findings that are potentially compatible with interstitial lung disease in patients without clinical suspicion of the disease. Interstitial lung abnormalities are increasingly recognised as a common feature on CT of the lung in older individuals, occurring in 4-9% of smokers and 2-7% of non-smokers. Identification of interstitial lung abnormalities will increase with implementation of lung cancer screening, along with increased use of CT for other diagnostic purposes. These abnormalities are associated with radiological progression, increased mortality, and the risk of complications from medical interventions, such as chemotherapy and surgery. Management requires distinguishing interstitial lung abnormalities that represent clinically significant interstitial lung disease from those that are subclinical. In particular, it is important to identify the subpleural fibrotic subtype, which is more likely to progress and to be associated with mortality. This multidisciplinary Position Paper by the Fleischner Society addresses important issues regarding interstitial lung abnormalities, including standardisation of the definition and terminology; predisposing risk factors; clinical outcomes; options for initial evaluation, monitoring, and management; the role of quantitative evaluation; and future research needs.

BACKGROUND: Mucous exudates occluding the lumen of small airways are associated with reduced lung function and mortality in subjects with COPD; however, luminal plugs in large airways have not been widely studied. We aimed to examine the associations of chest CT scan-identified luminal plugging with lung function, health-related quality of life, and COPD phenotypes. METHODS: We randomly selected 100 smokers without COPD and 400 smokers with COPD from the COPDGene Study. Luminal plugging was visually identified on inspiratory CT scans at baseline and 5-year follow-up. The relationships of luminal plugging to FEV1, St. George's Respiratory Questionnaire (SGRQ) score, emphysema on CT scan (defined as the percentage of low attenuation area < 950 Hounsfield units [%LAA-950]), and chronic bronchitis were assessed using linear and logistic multivariable analyses. RESULTS: Overall, 111 subjects (22%) had luminal plugging. The prevalence of luminal plugging was higher in subjects with COPD than those without COPD (25% vs 10%, respectively; P = .001). In subjects with COPD, luminal plugging was significantly associated with FEV1 % predicted (estimate, -6.1; SE, 2.1; P = .004) and SGRQ score (estimate, 4.9; SE, 2.4; P = .04) in adjusted models. Although luminal plugging was associated with log %LAA-950 (estimate, 0.43; SE, 0.16; P = .007), its relationship with chronic bronchitis did not reach statistical significance (P = .07). Seventy-three percent of subjects with COPD with luminal plugging at baseline had it 5 years later. CONCLUSIONS: In subjects with COPD, CT-identified luminal plugging is associated with airflow obstruction, worse health-related quality of life, and emphysema phenotype. This imaging feature may supplement the current clinical assessment of chronic mucus hypersecretion in COPD.

BACKGROUND: COPD is a leading cause of mortality. RESEARCH QUESTION: We hypothesized that applying machine learning to clinical and quantitative CT imaging features would improve mortality prediction in COPD. STUDY DESIGN AND METHODS: We selected 30 clinical, spirometric, and imaging features as inputs for a random survival forest. We used top features in a Cox regression to create a machine learning mortality prediction (MLMP) in COPD model and also assessed the performance of other statistical and machine learning models. We trained the models in subjects with moderate to severe COPD from a subset of subjects in Genetic Epidemiology of COPD (COPDGene) and tested prediction performance in the remainder of individuals with moderate to severe COPD in COPDGene and Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE). We compared our model with the BMI, airflow obstruction, dyspnea, exercise capacity (BODE) index; BODE modifications; and the age, dyspnea, and airflow obstruction index. RESULTS: We included 2,632 participants from COPDGene and 1,268 participants from ECLIPSE. The top predictors of mortality were 6-min walk distance, FEV1 % predicted, and age. The top imaging predictor was pulmonary artery-to-aorta ratio. The MLMP-COPD model resulted in a C index ≥ 0.7 in both COPDGene and ECLIPSE (6.4- and 7.2-year median follow-ups, respectively), significantly better than all tested mortality indexes (P < .05). The MLMP-COPD model had fewer predictors but similar performance to that of other models. The group with the highest BODE scores (7-10) had 64% mortality, whereas the highest mortality group defined by the MLMP-COPD model had 77% mortality (P = .012). INTERPRETATION: An MLMP-COPD model outperformed four existing models for predicting all-cause mortality across two COPD cohorts. Performance of machine learning was similar to that of traditional statistical methods. The model is available online at: https://cdnm.shinyapps.io/cgmortalityapp/.

COPD is a heterogeneous syndrome. Many COPD subtypes have been proposed, but there is not yet consensus on how many COPD subtypes there are and how they should be defined. The COPD Genetic Epidemiology Study (COPDGene), which has generated 10-year longitudinal chest imaging, spirometry, and molecular data, is a rich resource for relating COPD phenotypes to underlying genetic and molecular mechanisms. In this article, we place COPDGene clustering studies in context with other highly cited COPD clustering studies, and summarize the main COPD subtype findings from COPDGene. First, most manifestations of COPD occur along a continuum, which explains why continuous aspects of COPD or disease axes may be more accurate and reproducible than subtypes identified through clustering methods. Second, continuous COPD-related measures can be used to create subgroups through the use of predictive models to define cut-points, and we review COPDGene research on blood eosinophil count thresholds as a specific example. Third, COPD phenotypes identified or prioritized through machine learning methods have led to novel biological discoveries, including novel emphysema genetic risk variants and systemic inflammatory subtypes of COPD. Fourth, trajectory-based COPD subtyping captures differences in the longitudinal evolution of COPD, addressing a major limitation of clustering analyses that are confounded by disease severity. Ongoing longitudinal characterization of subjects in COPDGene will provide useful insights about the relationship between lung imaging parameters, molecular markers, and COPD progression that will enable the identification of subtypes based on underlying disease processes and distinct patterns of disease progression, with the potential to improve the clinical relevance and reproducibility of COPD subtypes.

We present an open-source framework for pulmonary fissure completeness assessment. Fissure incompleteness has been shown to associate with emphysema treatment outcomes, motivating the development of tools that facilitate completeness estimation. Generally, the task of fissure completeness assessment requires accurate detection of fissures and definition of the boundary surfaces separating the lung lobes. The framework we describe acknowledges a) the modular nature of fissure detection and lung lobe segmentation (lobe boundary detection), and b) that methods to address these challenges are varied and continually developing. It is designed to be readily deployable on existing lung lobe segmentation and fissure detection data sets. The framework consists of multiple components: a flexible quality control module that enables rapid assessment of lung lobe segmentations, an interactive lobe segmentation tool exposed through 3D Slicer for handling challenging cases, a flexible fissure representation using particles-based sampling that can handle fissure feature-strength or binary fissure detection images, and a module that performs fissure completeness estimation using voxel counting and a novel surface area estimation approach. We demonstrate the usage of the proposed framework by deploying on 100 cases exhibiting various levels of fissure completeness. We compare the two completeness level approaches and also compare to visual reads. The code is available to the community via github as part of the Chest Imaging Platform and a 3D Slicer extension module.

The phenotypic characteristics of chronic obstructive pulmonary disease (COPD) in individuals younger than 50 years of age (early COPD) are not well defined. This prospective, multicentre, case-control study sought to describe these characteristics and compare them with those of smokers (≥10 pack-years) of similar age with normal spirometry (controls). We studied 92 cases (post-bronchodilator forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC) <0.7) and 197 controls. Results were contrasted with participants with similar inclusion criteria recruited into the ECLIPSE and COPDGene cohorts. Cases had moderate airflow limitation (FEV1 71.3±20.8%) but were often symptomatic, used healthcare resources frequently, had air trapping (residual volume 150.6±55.5% ref.), had reduced diffusing capacity (84.2±20.7% ref.) and had frequent evidence of computed tomography (CT) emphysema (61%). Of note, less than half of cases (46%) had been previously diagnosed with COPD. Interestingly, they also often reported a family history of respiratory diseases and had been hospitalised because of respiratory problems before the age of 5 years more frequently than controls (12% versus 3%, p=0.009). By and large, these observations were reproduced when available in the ECLIPSE and COPDGene cohorts. These results show that early COPD is associated with substantial health impact and significant structural and functional abnormalities, albeit it is often not diagnosed (hence, treated). The fact that a sizeable proportion of patients with early COPD report a family history of respiratory diseases and/or early-life events (including hospitalisations before the age of 5 years) renders further support to the possibility of early-life origin of COPD.

In this position paper, we provide a collection of views on the role of AI in the COVID-19 pandemic, from clinical requirements to the design of AI-based systems, to the translation of the developed tools to the clinic. We highlight key factors in designing system solutions - per specific task; as well as design issues in managing the disease at the national level. We focus on three specific use-cases for which AI systems can be built: early disease detection, management in a hospital setting, and building patient-specific predictive models that require the combination of imaging with additional clinical data. Infrastructure considerations and population modeling in two European countries will be described. This pandemic has made the practical and scientific challenges of making AI solutions very explicit. A discussion concludes this paper, with a list of challenges facing the community in the AI road ahead.

Pulmonary artery enlargement on chest CT imaging is independently associated with all-cause mortality in moderate-severe COPD, after adjustment for other known risk factors for COPD mortality and cardiovascular disease.

BACKGROUND: The most common abnormal spirometric pattern reported in WTC worker and volunteer cohorts has consistently been that of a nonobstructive reduced forced vital capacity (low FVC). Low FVC is associated with obesity, which is highly prevalent in these cohorts. We used quantitative CT (QCT) to investigate proximal and distal airway inflammation and emphysema in participants with stable low FVC pattern. METHODS: We selected study participants with at least two available longitudinal surveillance spirometries, and a chest CT with QCT measurements of proximal airway inflammation (wall area percent, WAP), end-expiratory air trapping, suggestive of distal airway obstruction (expiratory to inspiratory mean lung attenuation ratio, MLAEI), and emphysema (percentage of lung volume with attenuation below - 950 HU, LAV%). The comparison groups in multinomial logistic regression models were participants with consistently normal spirometries, and participants with stable fixed obstruction (COPD). RESULTS: Compared to normal spirometry participants, and after adjusting for age, sex, race/ethnicity, BMI, smoking, and early arrival at the WTC disaster site, low FVC participants had higher WAP (ORadj 1.24, 95% CI 1.06, 1.45, per 5% unit), suggestive of proximal airway inflammation, but did not differ in MLAEI, or LAV%. COPD participants did not differ in WAP with the low FVC ones and were more likely to have higher MLAEI or LAV% than the other two subgroups. DISCUSSION: WTC workers with spirometric low FVC have higher QCT-measured WAP compared to those with normal spirometries, but did not differ in distal airway and emphysema measurements, independently of obesity, smoking, and other covariates.

BACKGROUND: Studies have demonstrated an inverse relationship between body mass index (BMI) and the risk of developing lung cancer. We conducted a retrospective cohort study evaluating baseline quantitative computed tomography (CT) measurements of body composition, specifically muscle and fat area in a large CT lung screening cohort (CTLS). We hypothesized that quantitative measurements of baseline body composition may aid in risk stratification for lung cancer. METHODS: Patients who underwent baseline CTLS between January 1st, 2012 and September 30th, 2014 and who had an in-network primary care physician were included. All patients met NCCN Guidelines eligibility criteria for CTLS. Quantitative measurements of pectoralis muscle area (PMA) and subcutaneous fat area (SFA) were performed on a single axial slice of the CT above the aortic arch with the Chest Imaging Platform Workstation software. Cox multivariable proportional hazards model for cancer was adjusted for variables with a univariate p < 0.2. Data were dichotomized by sex and then combined to account for baseline differences between sexes. RESULTS: One thousand six hundred and ninety six patients were included in this study. A total of 79 (4.7%) patients developed lung cancer. There was an association between the 25th percentile of PMA and the development of lung cancer [HR 1.71 (1.07, 2.75), p < 0.025] after adjusting for age, BMI, qualitative emphysema, qualitative coronary artery calcification, and baseline Lung-RADS® score. CONCLUSIONS: Quantitative assessment of PMA on baseline CTLS was associated with the development of lung cancer. Quantitative PMA has the potential to be incorporated as a variable in future lung cancer risk models.

PURPOSE: We present SlicerDMRI, an open-source software suite that enables research using diffusion magnetic resonance imaging (dMRI), the only modality that can map the white matter connections of the living human brain. SlicerDMRI enables analysis and visualization of dMRI data and is aimed at the needs of clinical research users. SlicerDMRI is built upon and deeply integrated with 3D Slicer, a National Institutes of Health-supported open-source platform for medical image informatics, image processing, and three-dimensional visualization. Integration with 3D Slicer provides many features of interest to cancer researchers, such as real-time integration with neuronavigation equipment, intraoperative imaging modalities, and multimodal data fusion. One key application of SlicerDMRI is in neurosurgery research, where brain mapping using dMRI can provide patient-specific maps of critical brain connections as well as insight into the tissue microstructure that surrounds brain tumors. PATIENTS AND METHODS: In this article, we focus on a demonstration of SlicerDMRI as an informatics tool to enable end-to-end dMRI analyses in two retrospective imaging data sets from patients with high-grade glioma. Analyses demonstrated here include conventional diffusion tensor analysis, advanced multifiber tractography, automated identification of critical fiber tracts, and integration of multimodal imagery with dMRI. RESULTS: We illustrate the ability of SlicerDMRI to perform both conventional and advanced dMRI analyses as well as to enable multimodal image analysis and visualization. We provide an overview of the clinical rationale for each analysis along with pointers to the SlicerDMRI tools used in each. CONCLUSION: SlicerDMRI provides open-source and clinician-accessible research software tools for dMRI analysis. SlicerDMRI is available for easy automated installation through the 3D Slicer Extension Manager.

Background Smokers with chronic obstructive pulmonary disease (COPD) have smaller left ventricles (LVs) due to reduced preload. Skeletal muscle wasting is also common in COPD, but less is known about its contribution to LV size. Purpose To explore the relationships between CT metrics of emphysema, venous vascular volume, and sarcopenia with the LV epicardial volume (LVEV) (myocardium and chamber) estimated from chest CT images in participants with COPD and then to determine the clinical relevance of the LVEV in multivariable models, including sex and anthropomorphic metrics. Materials and Methods The COPDGene study (ClinicalTrials.gov identifier: NCT00608764) is an ongoing prospective longitudinal observational investigation that began in 2006. LVEV, distal pulmonary venous blood volume for vessels smaller than 5 mm2 in cross section (BV5), CT emphysema, and pectoralis muscle area were retrospectively extracted from 3318 nongated, unenhanced COPDGene CT scans. Multivariable linear and Cox regression models were used to explore the association between emphysema, venous BV5, pectoralis muscle area, and LVEV as well as the association of LVEV with health status using the St George's Respiratory Questionnaire, 6-minute walk distance, and all-cause mortality. Results The median age of the cohort was 64 years (interquartile range, 57-70 years). Of the 2423 participants, 1806 were men and 617 were African American. The median LVEV between Global Initiative for Chronic Obstructive Lung Disease (GOLD) 1 and GOLD 4 COPD was reduced by 13.9% in women and 17.7% in men (P < .001 for both). In fully adjusted models, higher emphysema percentage (β = -4.2; 95% confidence interval [CI]: -5.0, -3.4; P < .001), venous BV5 (β = 7.0; 95% CI: 5.7, 8.2; P < .001), and pectoralis muscle area (β = 2.7; 95% CI: 1.2, 4.1; P < .001) were independently associated with reduced LVEV. Reductions in LVEV were associated with improved health status (β = 0.3; 95% CI: 0.1, 0.4) and 6-minute walk distance (β = -12.2; 95% CI: -15.2, -9.3). These effects were greater in women than in men. The effect of reduced LVEV on mortality (hazard ratio: 1.07; 95% CI: 1.05, 1.09) did not vary by sex. Conclusion In women more than men with chronic obstructive pulmonary disease, a reduction in the estimated left ventricle epicardial volume correlated with a loss of pulmonary venous vasculature, greater pectoralis muscle sarcopenia, and lower all-cause mortality. © RSNA, 2020 Online supplemental material is available for this article.

PURPOSE: To provide a unifying statistical model that characterizes the integrated x-ray intensity at the detector after logarithmic transformation and can be extended to the characterization of computed tomography (CT) numbers in the reconstructed image. METHODS: We study the statistical characteristics of polyenergetic x-ray beams in the detector. Firstly, we consider the characterization of the integrated x-ray intensity at the detector through a probabilistic model (compound Poisson) that describes its statistics. We analyze its properties and derive the probabilistic distribution after the logarithmic transformation analytically. Finally, we propose a more tractable probabilistic distribution with the same features observed in the characterization, the noncentral Gamma (nc-Gamma). This distribution exhibits desirable properties for the statistical characterization across the reconstruction process. We assess the assumptions adopted in the derivation of the statistical models throughout Monte Carlo simulations and validate them with a water phantom and a lung phantom acquired in a Siemens clinical CT scan. We evaluate the statistical similarities between the theoretical distribution and the nc-Gamma using a power analysis with a Kolmogorov-Smirnov test for a 95% confidence level. RESULTS: The Kolmogorov-Smirnov goodness-of-fit test obtained for the Monte Carlo simulation shows an extremely high agreement between the empirical distribution of the post-logarithmic-integrated x-ray intensity and the nc-Gamma. The experimental validation performed with both phantoms confirmed the excellent match between the theoretical distribution, the proposed nc-Gamma, and sample distributions in all situations. CONCLUSION: We derive an analytical model describing the post-log distribution of the linear attenuation coefficient in the sensor for polychromatic CT scans. We also demonstrate that the nc-Gamma distribution approximates well the theoretical distribution. This distribution also approximates well the CT numbers after reconstruction since it naturally extends across linear operations involved in filtered back projection reconstructions. This probabilistic model may provide the analytical foundation to define new likelihood-based reconstruction methodologies for polychromatic scans.

Although there is no universal definition for texture, the concept in various forms is nevertheless widely used and a key element of visual perception to analyze images in different fields. The present work's main idea relies on the assumption that there exist representative samples, which we refer to as references as well, i.e., "good or bad" samples that represent a given dataset investigated in a particular data analysis problem. These representative samples need to be accounted for when designing predictive models with the aim of improving their performance. In particular, based on a selected subset of texture gray-level co-occurrence matrices (GLCMs) from the training cohort, we propose new representative spatial texture features, which we incorporate into a supervised image classification pipeline. The pipeline relies on the support vector machine (SVM) algorithm along with Bayesian optimization and the Wasserstein metric from optimal mass transport (OMT) theory. The selection of the best, "good and bad," GLCM references is considered for each classification label and performed during the training phase of the SVM classifier using a Bayesian optimizer. We assume that sample fitness is defined based on closeness (in the sense of the Wasserstein metric) and high correlation (Spearman's rank sense) with other samples in the same class. Moreover, the newly defined spatial texture features consist of the Wasserstein distance between the optimally selected references and the remaining samples. We assessed the performance of the proposed classification pipeline in diagnosing the corona virus disease 2019 (COVID-19) from computed tomographic (CT) images.

PURPOSE: To investigate if the presence and severity of traction bronchiectasis/bronchiolectasis are associated with poorer survival in subjects with ILA. METHOD: The study included 3,594 subjects (378 subjects with ILA and 3,216 subjects without ILA) in AGES-Reykjavik Study. Chest CT scans of 378 subjects with ILA were evaluated for traction bronchiectasis/bronchiolectasis, defined as dilatation of bronchi/bronchioles within areas demonstrating ILA. Traction bronchiectasis/bronchiolectasis Index (TBI) was assigned as: TBI = 0, ILA without traction bronchiectasis/bronchiolectasis: TBI = 1, ILA with bronchiolectasis but without bronchiectasis or architectural distortion: TBI = 2, ILA with mild to moderate traction bronchiectasis: TBI = 3, ILA and severe traction bronchiectasis and/or honeycombing. Overall survival (OS) was compared among the subjects in different TBI groups and those without ILA. RESULTS: The median OS was 12.93 years (95%CI; 12.67 - 13.43) in the subjects without ILA; 11.95 years (10.03 - not reached) in TBI-0 group; 8.52 years (7.57 - 9.30) in TBI-1 group; 7.63 years (6.09 - 9.10) in TBI-2 group; 5.40 years (1.85 - 5.98) in TBI-3 group. The multivariable Cox models demonstrated significantly shorter OS of TBI-1, TBI-2, and TBI-3 groups compared to subjects without ILA (P < 0.0001), whereas TBI-0 group had no significant OS difference compared to subjects without ILA, after adjusting for age, sex, and smoking status. CONCLUSIONS: The presence and severity of traction bronchiectasis/bronchiolectasis are associated with shorter survival. The traction bronchiectasis/bronchiolectasis is an important contributor to increased mortality among subjects with ILA.