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Esther Pompe,1 Pim A de Jong,2 Eva M van Rikxoort,3 Leticia Gallardo Estrella,3 Werner U de Jong,2 Rozemarijn Vliegenthart,4,5 Matthijs Oudkerk,5 Carlijn M van der Aalst,6 Bram van Ginneken,3 Jan-Willem J Lammers,1 Firdaus AA Mohamed Hoesein2 1Department of Pulmonology, 2Department of Radiology, University Medical Center Utrecht, Utrecht, 3Department of Radiology, Radboud University Medical Center, Nijmegen, 4Center for Medical Imaging-North East Netherlands, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; 5Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, 6Department of Public Health, Erasmus Medical Center, Rotterdam, the Netherlands Abstract: Osteoporosis is more common in patients with COPD and in smokers. The aim of this study was to assess whether measures of emphysema and airway disease on computed tomography (CT) were associated with lower bone density or vertebral fractures in smokers with and without COPD. For this purpose, we included participants from the NELSON lung cancer screening trial. Bone density was measured as Hounsfield Units in the first lumbar vertebra, and vertebral fractures were assessed semiquantitatively. The 15th percentile method (Perc15) was used to assess emphysema, and the airway lumen perimeter (Pi10) was used for airway wall thickness. Expiratory/inspiratory-ratiomean lung density (E/I-ratioMLD) was used as a measure for air trapping and tracheal index to assess tracheal deformity. Linear regression models and logistic regression models were used to assess associations between CT biomarkers, bone density, and presence of fractures. Exactly 1,093 male participants were eligible for analysis. Lower Perc15 and higher E/I-ratioMLD were significantly associated with lower bone density (b=-1.27, P=0.02 and b=-0.37, P=0.02, respectively). Pi10 and tracheal index were not associated with bone density changes. CT-derived biomarkers were not associated with fracture prevalence. Bone density is lower with increasing extent of emphysema and small airway disease but is not associated with large airway disease and tracheal deformity. This may indicate the necessity to measure bone density early in smokers with emphysema and air trapping to prevent vertebral fractures. Keywords: phenotyping, computed tomography, air trapping, emphysema, COPD, bone density

Background: Little is known about factors associated with emphysema progression in cigarette smokers. We evaluated factors associated with change in emphysema and forced expiratory volume in 1 second (FEV 1 ) in participants with and without chronic obstructive pulmonary disease (COPD)., Methods: This retrospective study included individuals participating in the COPD Genetic Epidemiology study who completed the 5-year follow-up, including inspiratory and expiratory computed tomography (CT) and spirometry. All paired CT scans were analyzed using micro-mapping, which classifies individual voxels as emphysema or functional small airway disease (fSAD). Presence and progression of emphysema and FEV 1 were determined based on comparison to nonsmoker values. Logistic regression analyses were used to identify clinical parameters associated with disease progression., Results: A total of 3088 participants were included with a mean ± SD age of 60.7±8.9 years, including 72 nonsmokers. In all Global initiative for chronic Obstructive Lung Disease (GOLD) stages, the presence of emphysema at baseline was associated with emphysema progression (odds ratio [OR]: GOLD 0: 4.32; preserved ratio-impaired spirometry [PRISm]; 5.73; GOLD 1: 5.16; GOLD 2: 5.69; GOLD 3/4: 5.55; all p ≤0.01). If there was no emphysema at baseline, the amount of fSAD at baseline was associated with emphysema progression (OR for 1% increase: GOLD 0: 1.06; PRISm: 1.20; GOLD 1: 1.7; GOLD 3/4: 1.08; all p ≤ 0.03).In 1735 participants without spirometric COPD, progression in emphysema occurred in 105 (6.1%) participants and only 21 (1.2%) had progression in both emphysema and FEV 1 ., Conclusions: The presence of emphysema is an important predictor of emphysema progression. In patients without emphysema, fSAD is associated with the development of emphysema. In participants without spirometric COPD, emphysema progression occurred independently of FEV 1 decline., (JCOPDF © 2021.)

Purpose: For a successful bronchoscopic lung volume reduction coil treatment it is important to place the coils in the most emphysematous lobes. Therefore assessment of the lobe with greatest destruction is essential. Our aims were to investigate the level of agreement among expert reviewers of HRCT-scans in emphysema patients and the comparison with QCT (quantitative computed tomography) software., Method: Five experienced CT-assessors, conducted a visual assessment of the baseline HRCT-scans of emphysema patients who participated in the RENEW bronchoscopic lung volume reduction coil study. On the same HRCT-scans, a QCT analysis was performed., Results: In total 134 HRCT-scans were rated by all 5 experts. All 5 CT-assessors agreed on which was the most destructed lobe in 61 % of the left lungs (ƙ:0.459) and 60 % of the right lungs (ƙ:0.370). The consensus of the 5 assessors matched the QCT in the left lung for 77 % of the patients (ƙ:0.425) and in the right lung for 82 % (ƙ:0.524)., Conclusions: Our results show that the interobserver agreement between five expert CT-assessors was only fair to moderate when evaluating the most destructed lobe. CT-assessor consensus improved matching with QCT determination of lobar destruction compared to individual assessor determinations. Because some CT-features are associated with treatment outcomes and important for optimal patient selection of bronchoscopic lung volume reduction treatment, we recommend including more than one CT-reviewer and supported by QCT measurements., (Copyright © 2021 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background The correlation between visual emphysema patterns and subsequent progression of disease may provide a way to enrich a study population for treatment trials of emphysema. Purpose To evaluate the potential relationship between emphysema visual subtypes and progression of emphysema and gas trapping. Materials and Methods Current and former smokers with and without chronic obstructive pulmonary disease (COPD) enrolled in the prospective Genetic Epidemiology of COPD (COPDGene) study (ClinicalTrials.gov identifier: NCT02445183) between 2008 and 2011 had their Fleischner Society visual CT scores assessed at baseline, quantitative inspiratory, and expiratory CT and at 5 years. They also underwent pulmonary function testing at baseline CT and at 5 years. The dependent variables were inspiratory lung density at 15th percentile (adjusted for lung volume) as a measure of emphysema and percentage of lung volume with attenuation less than -856 HU at expiratory CT as a measure of air trapping. Statistical analysis used a linear mixed model, adjusted for age, height, sex, race, smoking status, and scanner make. Results A total of 4166 participants (mean age, 60 years ± 9 [standard deviation]; 2091 [50%] men) were evaluated. In participants with COPD (1655 participants, 40%), those with visual presence of mild, moderate, and confluent emphysema at baseline CT showed a mean decline in lung density of 4.6 g/L ± 1.1 ( P < .001), 6.7 g/L ± 1.1 ( P < .001), and 6.4 g/L ± 1.2 ( P < .001), respectively, compared with 2.4 g/L ± 1.3 ( P < .001) for those with trace emphysema. For participants without COPD, those with visual presence of mild and moderate emphysema at baseline CT showed a mean decline in lung density of 3.6 g/L ± 1.0 ( P < .001) and 3.1 g/L ± 1.6 ( P < .001), respectively, compared with 1.8 g/L ± 1.0 ( P < .001) for those with trace emphysema. Conclusion The pattern of parenchymal emphysema at baseline CT was an independent predictor of subsequent progression of emphysema in participants who are current or former cigarette smokers with and without chronic obstructive pulmonary disease. © RSNA, 2020 Online supplemental material is available for this article.

Background The coronavirus disease 2019 (COVID-19) pandemic has spread across the globe with alarming speed, morbidity, and mortality. Immediate triage of patients with chest infections suspected to be caused by COVID-19 using chest CT may be of assistance when results from definitive viral testing are delayed. Purpose To develop and validate an artificial intelligence (AI) system to score the likelihood and extent of pulmonary COVID-19 on chest CT scans using the COVID-19 Reporting and Data System (CO-RADS) and CT severity scoring systems. Materials and Methods The CO-RADS AI system consists of three deep-learning algorithms that automatically segment the five pulmonary lobes, assign a CO-RADS score for the suspicion of COVID-19, and assign a CT severity score for the degree of parenchymal involvement per lobe. This study retrospectively included patients who underwent a nonenhanced chest CT examination because of clinical suspicion of COVID-19 at two medical centers. The system was trained, validated, and tested with data from one of the centers. Data from the second center served as an external test set. Diagnostic performance and agreement with scores assigned by eight independent observers were measured using receiver operating characteristic analysis, linearly weighted κ values, and classification accuracy. Results A total of 105 patients (mean age, 62 years ± 16 [standard deviation]; 61 men) and 262 patients (mean age, 64 years ± 16; 154 men) were evaluated in the internal and external test sets, respectively. The system discriminated between patients with COVID-19 and those without COVID-19, with areas under the receiver operating characteristic curve of 0.95 (95% CI: 0.91, 0.98) and 0.88 (95% CI: 0.84, 0.93), for the internal and external test sets, respectively. Agreement with the eight human observers was moderate to substantial, with mean linearly weighted κ values of 0.60 ± 0.01 for CO-RADS scores and 0.54 ± 0.01 for CT severity scores. Conclusion With high diagnostic performance, the CO-RADS AI system correctly identified patients with COVID-19 using chest CT scans and assigned standardized CO-RADS and CT severity scores that demonstrated good agreement with findings from eight independent observers and generalized well to external data. © RSNA, 2020 Supplemental material is available for this article.

Background CT is used to quantify abnormal changes in the lung parenchyma of smokers that might overlap chronic obstructive pulmonary disease (COPD), but studies on the progression of expiratory air trapping in smokers are scarce. Purpose To evaluate the relationship between longitudinal changes in forced expiratory volume in 1 second (FEV 1 ) and CT-quantified emphysema and air trapping in smokers. Materials and Methods Cigarette smokers with and those without COPD participating in the multicenter observational COPDGene study were evaluated. Subjects underwent inspiratory and expiratory chest CT and spirometry at baseline and 5-year follow-up. Emphysema was quantified by using adjusted lung density (ALD). Air trapping was quantified by using mean lung density at expiratory CT and CT-measured functional residual capacity-to-total lung volume ratio. Linear models were used to regress quantitative CT measurements taken 5 years apart, and models were fit with and without adding FEV 1 as a predictor. Analyses were stratified by Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage (GOLD 0, no COPD; GOLD 1, mild COPD; GOLD 2, moderate COPD; GOLD 3, severe COPD; GOLD 4, very severe COPD). Subjects with preserved FEV 1 -to-forced vital capacity ratio and reduced FEV 1 percentage predicted were categorized as having preserved ratio impaired spirometry (PRISm). Results A total of 4211 subjects (503 with PRISm; 2034 with GOLD 0, 388 with GOLD 1, 816 with GOLD 2, 381 with GOLD 3, 89 with GOLD 4) were evaluated. ALD decreased by 1.7 g/L (95% confidence interval [CI]: -2.5, -0.9) in subjects with GOLD 0 at baseline and by 5.3 g/L (95% CI: -6.2, -4.4) in those with GOLD 1-4 ( P < .001 for both). When adjusted for changes in FEV 1 , corresponding numbers were -2.2 (95% CI: -3.0, -1.3) and -4.6 g/L (95% CI: -5.6, -3.4) ( P < .001 for both). Progression in air trapping was identified only in GOLD stage 2-4. Approximately 33%-50% of changes in air trapping in GOLD stages 2-4 were accounted for by changes in FEV 1 . Conclusion CT measures of emphysema and air trapping increased over 5 years in smokers. Forced expiratory volume in one second accounted for less than 10% of emphysema progression and less than 50% of air trapping progression detected at CT. © RSNA, 2020 Online supplemental material is available for this article.

The results of the randomised controlled trials investigating the bronchoscopic lung volume reduction treatment using endobronchial valves (EBV) are promising, and have led to their inclusion in treatment guidelines, US Food and Drug Administration approval and inclusion in routine care in an increasing number of countries. The one-way valve treatment has advanced and is now a regular treatment option. However, this new phase will lead to new challenges in terms of implementation. We believe that key issues in future research concern advanced patient selection, improved methods for target lobe selection, increased knowledge on the predictive risk of a pneumothorax, positioning of pulmonary rehabilitation in conjunction with the EBV treatment, the positioning of lung volume reduction surgery versus EBV treatment, and the long-term efficacy, adverse events, impact on exacerbations and hospitalisations, costs and survival. Hopefully, the increasing number of patients treated, the setup of (inter)national registries and future research efforts will further optimise all aspects of this treatment., Competing Interests: Conflict of interest: J.E. Hartman has nothing to disclose. Conflict of interest: L.E.G.W. Vanfleteren reports personal fees from PulmonX, outside the submitted work. Conflict of interest: E.M. van Rikxoort reports other funding from Thirona, during the conduct of the study. Conflict of interest: K. Klooster has nothing to disclose. Conflict of interest: D-J. Slebos reports grants, personal fees, non-financial support and other funding from PulmonX Inc., CA, USA, during the conduct of the study; and grants, personal fees, non-financial support and other funding from PneumRx/BTG, CA, USA, outside the submitted work., (Copyright ©ERS 2019.)

Airway wall thickening in cigarette smokers is thought to be a result of inflammatory changes and airway remodeling. This study investigates if CT-derived airway wall thickening associates to disease severity in smokers with and without COPD and if airway wall thickening is reversible by smoking cessation. We examined 2000 smokers and 46 never-smokers who returned for a 5-year follow-up visit in the COPDGene-study. Multivariable regression analyses were performed at visit 1 to associate airway wall thickness (expressed as Pi10) with percent predicted forced expiratory volume in 1 s (FEV 1 %-predicted), 6-min walking distance (6MWD), and St. George Respiratory Questionnaire (SGRQ). Longitudinal analyses were performed to assess the effect of smoking cessation on Pi10 using linear mixed models. A higher Pi10 was significantly associated with worse FEV 1 %-predicted, 6MWD, and SGRQ in all GOLD-stages. Longitudinal analyses showed that subjects that quit smoking significantly decreased in Pi10 (ΔPi10 = -0.18 mm, p < 0.001). Subjects that started smoking had a significant increase in Pi10 (ΔPi10 = 0.14 mm, p < 0.001). Pi10 is a clinically relevant biomarker of smoking-related airway injury in smokers with and without COPD. The change in Pi10 with change in smoking status suggests that it can quantify a reversible component of smoking-related airway inflammation., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Background: Adequate target lobe selection for endobronchial valve (EBV) treatment in patients with severe emphysema is essential for treatment success and can be based on emphysema destruction, lobar perfusion, lobar volume, and collateral ventilation. As some patients have >1 target lobe for EBV treatment, we were interested whether we could identify the least functional lobe., Objectives: The objective of this study was to investigate the relationship between endoscopic lobar measurement of oxygen uptake, lobar destruction, and vascular volume, and whether this could help in identifying the least functional lobe and thus optimal target for EBV treatment., Method: We prospectively included patients who were scheduled for EBV treatment in our hospital. A customized gas analysis setup was used to measure lobar O2 uptake after lobar balloon occlusion. Quantitative CT analysis was performed to assess the degree of emphysematous destruction and lobar arterial and venous volumes., Results: Twenty-one (5 male/16 female) patients with emphysema (median age 63 years, FEV1 25% of predicted, residual volume 234% of predicted) were included, and 49 endoscopic lobar measurements were performed. A lower O2 uptake significantly correlated with a higher degree of emphysematous lobar destruction (Spearman's ρ: 0.39, p < 0.01), and lower arterial and venous vascular volumes of the lobes (-0.46 and -0.47, respectively; both p < 0.001)., Conclusions: Endoscopic measurement of lobar O2 uptake is feasible in patients with emphysema. Measurement of lobar O2 uptake helped to identify the least functional lobe and can be used as additional tool for EBV target lobe selection., (© 2019 S. Karger AG, Basel.)

Background and Objective: Target lobar volume reduction (TLVR) is an important efficacy outcome measure for bronchoscopic lung volume reduction (BLVR) treatment using one-way endobronchial valves (EBV) in patients with severe emphysema. The commonly used cut-off value for TLVR that expresses a perceivable clinical benefit is -350 mL. However, a scientifically determined minimal important difference (MID) for TLVR never has been published. The objective of the present study was to determine the MID for TLVR on HRCT in patients who were treated with EBV., Methods: A total of 318 patients with severe emphysema from two BLVR trials were analysed. Anchor-based methods were used to define the TLVR MID at 6 months follow-up. Forced expiratory volume in 1 s (FEV 1 ), residual volume (RV) and St. George's Respiratory Questionnaire (SGRQ) were used as anchors., Results: The calculated TLVR MID with each anchor was: FEV 1 -587 mL, RV -534 mL and SGRQ -560 mL. The combined MID (average of the three anchor-based MIDs) was -563 mL., Conclusion: Using the anchor-based method, we established a TLVR MID of -563 mL in patients with severe emphysema at 6 months follow-up after EBV treatment. This value can be useful for both interpreting the results from trials and clinical practice, as well as for designing future studies on lung volume reduction., (© 2017 Asian Pacific Society of Respirology.)

Background: In smokers, the lung parenchyma is characterized by inflammation and emphysema, processes that can result in local gain and loss of lung tissue. CT measures of lung density might reflect lung tissue changes; however, longitudinal data regarding the effects of CT lung tissue on FEV 1 in smokers with and without COPD are scarce., Methods: The 15th percentile of CT lung density was obtained from the scans of 3,390 smokers who completed baseline and 5-year follow-up of the Genetic Epidemiology of COPD (COPDGene) study visits. The longitudinal relationship between total lung capacity-adjusted lung density (TLC-PD15) and FEV 1 was assessed by using multivariable mixed models. Separate models were performed in smokers at risk, smokers with preserved ratio and impaired spirometry (PRISm), and smokers with COPD according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) staging system., Results: The direction of the relationship between lung density and lung function was GOLD stage dependent. In smokers with PRISm, a 1-g/L decrease in TLC-PD15 was associated with an increase of 2.8 mL FEV 1 (P = .02). In contrast, among smokers with GOLD III to IV COPD, a 1-g/L decrease in TLC-PD15 was associated with a decrease of 4.1 mL FEV 1 (P = .002)., Conclusions: A decline in TLC-PD15 was associated with an increase or decrease in FEV 1 depending on disease severity. The associations are GOLD stage specific, and their presence might influence the interpretation of future studies that use CT lung density as an intermediate study end point for a decline in lung function., Trial Registry: ClinicalTrials.gov; No.: NCT00608764; URL: www.clinicaltrials.gov., (Copyright © 2017 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.)

Subsolid pulmonary nodules are commonly encountered in lung cancer screening and clinical routine. Compared to other nodule types, subsolid nodules are associated with a higher malignancy probability for which the size and mass of the nodule and solid core are important indicators. However, reliably measuring these characteristics on computed tomography (CT) can be hampered by the presence of vessels encompassed by the nodule, since vessels have similar CT attenuation as solid cores. This can affect treatment decisions and patient management. We present a method based on voxel classification to automatically identify vessels and solid cores in given subsolid nodules on CT. Three experts validated our method on 170 screen-detected subsolid nodules from the Multicentric Italian Lung Disease trial. The agreement between the proposed method and the observers was substantial for vessel detection and moderate for solid core detection, which was similar to the inter-observer agreement. We found a relatively high variability in the inter-observer agreement and low method-observer agreements for delineating the borders of vessels and solid cores, illustrating the difficulty of this task. However, 92.4% of the proposed vessel and 80.6% of the proposed solid core segmentations were labeled as usable in clinical practice by the majority of experts.

Background: Absence of interlobar collateral ventilation using the Chartis measurement is the key predictor for successful endobronchial valve treatment in severe emphysema. Chartis was originally validated in spontaneous breathing patients under conscious sedation (CS); however, this can be challenging due to cough, mucus secretion, mucosal swelling, and bronchoconstriction. Performing Chartis under general anesthesia (GA) avoids these problems and may result in an easier procedure with a higher success rate. However, using Chartis under GA with positive pressure ventilation has not been validated., Objectives: In this study we investigated the impact of anesthesia technique, CS versus GA, on the feasibility and outcomes of Chartis measurement., Methods: We retrospectively analyzed all Chartis measurements performed at our hospital from October 2010 until December 2017., Results: We analyzed 250 emphysema patients (median forced expiratory volume in 1 s 26%, range 12-52% predicted). In 121 patients (48%) the measurement was performed using CS, in 124 (50%) using GA, and in 5 (2%) both anesthesia techniques were used. In total, 746 Chartis readings were analyzed (432 CS, 277 GA, and 37 combination). Testing under CS took significantly longer than GA (median 19 min [range 5-65] vs. 11 min [3-35], p < 0.001) and required more measurements (3 [1-13] vs. 2 [1-6], p < 0.001). There was no significant difference in target lobe volume reduction after treatment (-1,123 mL [-3,604 to 332] in CS vs. -1,251 mL [-3,333 to -1] in GA, p = 0.35)., Conclusions: In conclusion, Chartis measurement under CS took significantly longer and required more measurements than under GA, without a difference in treatment outcome. We recommend a prospective trial comparing both techniques within the same patients to validate this approach., (© 2018 The Author(s) Published by S. Karger AG, Basel.)

The purpose of this study is to develop a computed tomography (CT) biomarker of emphysema that is robust across reconstruction settings, and evaluate its ability to predict mortality in patients at high risk for lung cancer. Data included baseline CT scans acquired between August 2002 and April 2004 from 1737 deceased subjects and 5740 surviving controls taken from the National Lung Screening Trial. Emphysema scores were computed in the original scans (origES) and after applying resampling, normalization and bullae analysis (normES). We compared the prognostic value of normES versus origES for lung cancer and all-cause mortality by computing the area under the receiver operator characteristic curve (AUC) and the net reclassification improvement (NRI) for follow-up times of 1-7 years. normES was a better predictor of mortality than origES. The 95% confidence intervals for the differences in AUC values indicated a significant difference for all-cause mortality for 2 through 6 years of follow-up, and for lung cancer mortality for 1 through 7 years of follow-up. 95% confidence intervals in NRI values showed a statistically significant improvement in classification for all-cause mortality for 2 through 7 years of follow-up, and for lung cancer mortality for 3 through 7 years of follow-up. Contrary to conventional emphysema score, our normalized emphysema score is a good predictor of all-cause and lung cancer mortality in settings where multiple CT scanners and protocols are used.

Automated lung lobe segmentation methods often fail for challenging and clinically relevant cases with incomplete fissures or substantial amounts of pathology. We present a fast and intuitive method to interactively correct a given lung lobe segmentation or to quickly create a lobe segmentation from scratch based on a lung mask. A given lobar boundary is converted into a mesh by principal component analysis of 3D lobar boundary markers to obtain a plane where nodes correspond to the position of the markers. An observer can modify the mesh by drawing on 2D slices in arbitrary orientations. After each drawing, the mesh is immediately adapted in a 3D region around the user interaction. For evaluation we participated in the international lung lobe segmentation challenge LObe and lung analysis 2011 (LOLA11). Two observers applied the method to correct a given lung lobe segmentation obtained by a fully automatic method for all 55 CT scans of LOLA11. On average observer 1/2 required 8  ±  4/25  ±  12 interactions per case and took 1:30  ±  0:34/3:19  ±  1:29 min. The average distances to the reference segmentation were improved from an initial 2.68  ±  14.71 mm to 0.89  ±  1.63/0.74  ±  1.51 mm. In addition, one observer applied the proposed method to create a segmentation from scratch. This took 3:44  ±  0:58 minutes on average per case, applying an average of 20  ±  3 interactions to reach an average distance to the reference of 0.77  ±  1.14 mm. Thus, both the interactive corrections and the creation of a segmentation from scratch were feasible in a short time with excellent results and only little interaction. Since the mesh adaptation is independent of image features, the method can successfully handle patients with severe pathologies, provided that the human operator is capable of correctly indicating the lobar boundaries.

Purpose: To present a method to automatically quantify tracheal morphology changes during breathing and investigate its contribution to airflow impairment when adding CT measures of emphysema, airway wall thickness, air trapping and ventilation., Methods: Because tracheal abnormalities often occur localized, a method is presented that automatically determines the most abnormal trachea section based on automatically computed sagittal and coronal lengths. In this most abnormal section, trachea morphology is encoded using four equiangular rays from the center of the trachea and the normalized lengths of these rays are used as features in a classification scheme. Consequently, trachea measurements are used as input for classification into GOLD stages in addition to emphysema, air trapping and ventilation. A database of 200 subjects distributed across all GOLD stages is used to evaluate the classification with a k nearest neighbour algorithm. Performance is assessed in two experimental settings: (a) when only inspiratory scans are taken; (b) when both inspiratory and expiratory scans are available., Results: Given only an inspiratory CT scan, measuring tracheal shape provides complementary information only to emphysema measurements. The best performing set in the inspiratory setting was a combination of emphysema and bronchial measurements. The best performing feature set in the inspiratory-expiratory setting includes measurements of emphysema, ventilation, air trapping, and trachea. Inspiratory and inspiratory-expiratory settings showed similar performance., Conclusions: The fully automated system presented in this study provides information on trachea shape at inspiratory and expiratory CT. Addition of tracheal morphology features improves the ability of emphysema and air trapping CT-derived measurements to classify COPD patients into GOLD stages and may be relevant when investigating different aspects of COPD., (© 2017 American Association of Physicists in Medicine.)

Objectives: Airway wall thickness (AWT) is affected by changes in lung volume. This study evaluated whether correcting AWT on computed tomography (CT) for differences in inspiration level improves measurement agreement, reliability, and power to detect changes over time., Methods: Participants of the Dutch-Belgian lung cancer screening trial who underwent 3-month repeat CT for an indeterminate pulmonary nodule were included. AWT on CT was calculated by the square root of the wall area at a theoretical airway with an internal perimeter of 10mm (Pi10). The scan with the highest lung volume was labelled as the reference scan and the scan with the lowest lung volume was labelled as the comparison scan. Pi10 derived from the comparison scan was corrected by multiplying it with the ratio of CT lung volume of the comparison scan to CT lung volume on the reference scan. Agreement of uncorrected and corrected Pi10 was studied with the Bland-Altman method, reliability with intra-class correlation coefficients (ICC), and power to detect changes over time was calculated., Results: 315 male participants were included. Limit of agreement and reliability for Pi10 was -0.61 to 0.57mm (ICC=0.87), which improved to -0.38 to 0.37mm (ICC=0.94) after correction for inspiration level. To detect a 15% change over 3 months, 71 subjects are needed for Pi10 and 26 subjects for Pi10 adjusted for inspiration level., Conclusions: Correcting Pi10 for differences in inspiration level improves reliability, agreement, and power to detect changes over time., (Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.)

Objectives: To benchmark the performance of state-of-the-art computer-aided detection (CAD) of pulmonary nodules using the largest publicly available annotated CT database (LIDC/IDRI), and to show that CAD finds lesions not identified by the LIDC's four-fold double reading process., Methods: The LIDC/IDRI database contains 888 thoracic CT scans with a section thickness of 2.5 mm or lower. We report performance of two commercial and one academic CAD system. The influence of presence of contrast, section thickness, and reconstruction kernel on CAD performance was assessed. Four radiologists independently analyzed the false positive CAD marks of the best CAD system., Results: The updated commercial CAD system showed the best performance with a sensitivity of 82 % at an average of 3.1 false positive detections per scan. Forty-five false positive CAD marks were scored as nodules by all four radiologists in our study., Conclusions: On the largest publicly available reference database for lung nodule detection in chest CT, the updated commercial CAD system locates the vast majority of pulmonary nodules at a low false positive rate. Potential for CAD is substantiated by the fact that it identifies pulmonary nodules that were not marked during the extensive four-fold LIDC annotation process., Key Points: • CAD systems should be validated on public, heterogeneous databases. • The LIDC/IDRI database is an excellent database for benchmarking nodule CAD. • CAD can identify the majority of pulmonary nodules at a low false positive rate. • CAD can identify nodules missed by an extensive two-stage annotation process.

We present a method for automatic separation and classification of pulmonary arteries and veins in computed tomography. Our method takes advantage of local information to separate segmented vessels, and global information to perform the artery-vein classification. Given a vessel segmentation, a geometric graph is constructed that represents both the topology and the spatial distribution of the vessels. All nodes in the geometric graph where arteries and veins are potentially merged are identified based on graph pruning and individual branching patterns. At the identified nodes, the graph is split into subgraphs that each contain only arteries or veins. Based on the anatomical information that arteries and veins approach a common alveolar sag, an arterial subgraph is expected to be intertwined with a venous subgraph in the periphery of the lung. This relationship is quantified using periphery matching and is used to group subgraphs of the same artery-vein class. Artery-vein classification is performed on these grouped subgraphs based on the volumetric difference between arteries and veins. A quantitative evaluation was performed on 55 publicly available non-contrast CT scans. In all scans, two observers manually annotated randomly selected vessels as artery or vein. Our method was able to separate and classify arteries and veins with a median accuracy of 89%, closely approximating the inter-observer agreement. All CT scans used in this study, including all results of our system and all manual annotations, are publicly available at "http://www.w3.org/1999/xlink">http://arteryvein.grand-challenge.org".

Objectives: To propose and evaluate a method to reduce variability in emphysema quantification among different computed tomography (CT) reconstructions by normalizing CT data reconstructed with varying kernels., Methods: We included 369 subjects from the COPDGene study. For each subject, spirometry and a chest CT reconstructed with two kernels were obtained using two different scanners. Normalization was performed by frequency band decomposition with hierarchical unsharp masking to standardize the energy in each band to a reference value. Emphysema scores (ES), the percentage of lung voxels below -950 HU, were computed before and after normalization. Bland-Altman analysis and correlation between ES and spirometry before and after normalization were compared. Two mixed cohorts, containing data from all scanners and kernels, were created to simulate heterogeneous acquisition parameters., Results: The average difference in ES between kernels decreased for the scans obtained with both scanners after normalization (7.7 ± 2.7 to 0.3 ± 0.7; 7.2 ± 3.8 to -0.1 ± 0.5). Correlation coefficients between ES and FEV1, and FEV1/FVC increased significantly for the mixed cohorts., Conclusions: Normalization of chest CT data reduces variation in emphysema quantification due to reconstruction filters and improves correlation between ES and spirometry., Key Points: • Emphysema quantification is sensitive to the reconstruction kernel used. • Normalization allows comparison of emphysema quantification from images reconstructed with varying kernels. • Normalization allows comparison of emphysema quantification obtained with scanners from different manufacturers. • Normalization improves correlation of emphysema quantification with spirometry. • Normalization can be used to compare data from different studies and centers.

Background: Bronchoscopic lung volume reduction using one-way endobronchial valves (EBVs) has been proven to be effective in patients with severe emphysema. However, the selection of patients without collateral ventilation prior to treatment is critical for procedural success. Collateral ventilation can be assessed directly with the Chartis system or indirectly using computed tomography (CT) fissure analysis., Objectives: We retrospectively evaluated the diagnostic value of a combination of the quantitative CT interlobar fissure completeness score (FCS) and Chartis in predicting responders to EBV therapy., Methods: CT data from four prospective studies were pooled and analyzed using semiautomated software to quantify the completeness of interlobar fissures. These FCSs were compared to a reference standard of achieving ≥350 ml of target lobe volume reduction after EBV treatment. Using a receiver operating characteristic curve, optimal thresholds predictive of complete fissures (responders) and incomplete fissures (non-responders) were determined. A subgroup of patients with partially complete fissures was identified, where software had lower accuracy. The complementary value of Chartis was investigated in this group., Results: A fissure was defined as complete (FCS >95%), incomplete (FCS <80%), or partially complete (80% < FCS < 95%). The positive predictive value (PPV) of complete fissures is 88.1%, and the negative predictive value (NPV) is 92.9%, with an overall accuracy of 89.2%. Chartis was utilized in patients with partially complete fissures, with a PPV of 82.3%, an NPV of 84.6%, and an accuracy of 83.3%., Conclusion: Combining diagnostic tools could reduce the burden on patients and the healthcare system while providing clinicians with a better means for patient selection for EBV therapy., (© 2016 S. Karger AG, Basel.)

Background: Bronchoscopic lung-volume reduction with the use of one-way endobronchial valves is a potential treatment for patients with severe emphysema. To date, the benefits have been modest but have been hypothesized to be much larger in patients without interlobar collateral ventilation than in those with collateral ventilation., Methods: We randomly assigned patients with severe emphysema and a confirmed absence of collateral ventilation to bronchoscopic endobronchial-valve treatment (EBV group) or to continued standard medical care (control group). Primary outcomes were changes from baseline to 6 months in forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), and 6-minute walk distance., Results: Eighty-four patients were recruited, of whom 16 were excluded because they had collateral ventilation (13 patients) or because lobar segments were inaccessible to the endobronchial valves (3 patients). The remaining 68 patients (mean [±SD] age, 59±9 years; 46 were women) were randomly assigned to the EBV group (34 patients) or the control group (34). At baseline, the FEV1 and FVC were 29±7% and 77±18% of the predicted values, respectively, and the 6-minute walk distance was 374±86 m. Intention-to-treat analyses showed significantly greater improvements in the EBV group than in the control group from baseline to 6 months: the increase in FEV1 was greater in the EBV group than in the control group by 140 ml (95% confidence interval [CI], 55 to 225), the increase in FVC was greater by 347 ml (95% CI, 107 to 588), and the increase in the 6-minute walk distance was greater by 74 m (95% CI, 47 to 100) (P<0.01 for all comparisons). By 6 months, 23 serious adverse events had been reported in the EBV group, as compared with 5 in the control group (P<0.001). One patient in the EBV group died. Serious treatment-related adverse events in this group included pneumothorax (18% of patients) and events requiring valve replacement (12%) or removal (15%)., Conclusions: Endobronchial-valve treatment significantly improved pulmonary function and exercise capacity in patients with severe emphysema characterized by an absence of interlobar collateral ventilation. (Funded by the Netherlands Organization for Health Research and Development and the University Medical Center Groningen; Netherlands Trial Register number, NTR2876.).

Objectives: The purpose of this study was to develop and validate a computer-aided diagnosis (CAD) tool for automatic classification of pulmonary nodules seen on low-dose computed tomography into solid, part-solid, and non-solid., Materials and Methods: Study lesions were randomly selected from 2 sites participating in the Dutch-Belgian NELSON lung cancer screening trial. On the basis of the annotations made by the screening radiologists, 50 part-solid and 50 non-solid pulmonary nodules with a diameter between 5 and 30 mm were randomly selected from the 2 sites. For each unique nodule, 1 low-dose chest computed tomographic scan was randomly selected, in which the nodule was visible. In addition, 50 solid nodules in the same size range were randomly selected. A completely automatic 3-dimensional segmentation-based classification system was developed, which analyzes the pulmonary nodule, extracting intensity-, texture-, and segmentation-based features to perform a statistical classification. In addition to the nodule classification by the screening radiologists, an independent rating of all nodules by 3 experienced thoracic radiologists was performed. Performance of CAD was evaluated by comparing the agreement between CAD and human experts and among human experts using the Cohen κ statistics., Results: Pairwise agreement for the differentiation between solid, part-solid, and non-solid nodules between CAD and each of the human experts had a κ range between 0.54 and 0.72. The interobserver agreement among the human experts was in the same range (κ range, 0.56-0.81)., Conclusions: A novel automated classification tool for pulmonary nodules achieved good agreement with the human experts, yielding κ values in the same range as the interobserver agreement. Computer-aided diagnosis may aid radiologists in selecting the appropriate workup for pulmonary nodules.

Airway wall thickness and emphysema contribute to airflow limitation. We examined their association with lung function decline and development of airflow limitation in 2021 male smokers with and without airflow limitation. Airway wall thickness and emphysema were quantified on chest computed tomography and expressed as the square root of wall area of a 10-mm lumen perimeter (Pi10) and the 15th percentile method (Perc15), respectively. Baseline and follow-up (median (interquartile range) 3 (2.9-3.1) years) spirometry was available. Pi10 and Perc15 correlated with baseline forced expiratory volume in 1 s (FEV1) (r= -0.49 and 0.11, respectively (p<0.001)). Multiple linear regression showed that Pi10 and Perc15 at baseline were associated with a lower FEV1 after follow-up (p<0.05). For each sd increase in Pi10 and decrease in Perc15 the FEV1 decreased by 20 mL and 30.2 mL, respectively. The odds ratio for developing airflow limitation after 3 years was 2.45 for a 1-mm higher Pi10 and 1.46 for a 10-HU lower Perc15 (p<0.001). A greater degree of airway wall thickness and emphysema was associated with a higher FEV1 decline and development of airflow limitation after 3 years of follow-up., (Copyright ©ERS 2015.)

The malignancy of lung nodules is most often detected by analyzing changes of the nodule diameter in follow-up scans. A recent study showed that comparing the volume or the mass of a nodule over time is much more significant than comparing the diameter. Since the survival rate is higher when the disease is still in an early stage it is important to detect the growth rate as soon as possible. However manual segmentation of a volume is time-consuming. Whereas there are several well evaluated methods for the segmentation of solid nodules, less work is done on subsolid nodules which actually show a higher malignancy rate than solid nodules. In this work we present a fast, semi-automatic method for segmentation of subsolid nodules. As minimal user interaction the method expects a user-drawn stroke on the largest diameter of the nodule. First, a threshold-based region growing is performed based on intensity analysis of the nodule region and surrounding parenchyma. In the next step the chest wall is removed by a combination of a connected component analyses and convex hull calculation. Finally, attached vessels are detached by morphological operations. The method was evaluated on all nodules of the publicly available LIDC/IDRI database that were manually segmented and rated as non-solid or part-solid by four radiologists (Dataset 1) and three radiologists (Dataset 2). For these 59 nodules the Jaccard index for the agreement of the proposed method with the manual reference segmentations was 0.52/0.50 (Dataset 1/Dataset 2) compared to an inter-observer agreement of the manual segmentations of 0.54/0.58 (Dataset 1/Dataset 2). Furthermore, the inter-observer agreement using the proposed method (i.e. different input strokes) was analyzed and gave a Jaccard index of 0.74/0.74 (Dataset 1/Dataset 2). The presented method provides satisfactory segmentation results with minimal observer effort in minimal time and can reduce the inter-observer variability for segmentation of subsolid nodules in clinical routine.

Objective: The objective of this study was to investigate the association of spirometry and pulmonary CT biomarkers with cardiovascular events., Methods: In this lung cancer screening trial 3,080 male participants without a prior cardiovascular event were analysed. Fatal and non-fatal cardiovascular events were included. Spirometry included forced expiratory volume measured in units of one-second percent predicted (FEV1%predicted) and FEV1 divided by forced vital capacity (FVC; FEV1/FVC). CT examinations were quantified for coronary artery calcium volume, pulmonary emphysema (perc15) and bronchial wall thickness (pi10). Data were analysed via a Cox proportional hazard analysis, net reclassification improvement (NRI) and C-indices., Results: 184 participants experienced a cardiovascular event during a median follow-up of 2.9 years. Age, pack-years and smoking status adjusted hazard ratios were 0.992 (95% confidence interval (CI) 0.985-0.999) for FEV1%predicted, 1.000 (95%CI 0.986-1.015) for FEV1/FVC, 1.014 (95%CI 1.005-1.023) for perc15 per 10 HU, and 1.269 (95%CI 1.024-1.573) for pi10 per 1 mm. The incremental C-index (<0.015) and NRI (<2.8%) were minimal. Coronary artery calcium volume had a hazard ratio of 1.046 (95%CI 1.034-1.058) per 100 mm(3), an increase in C-index of 0.076 and an NRI of 16.9% (P < 0.0001)., Conclusions: Pulmonary CT biomarkers and spirometry measurements were significantly associated with cardiovascular events, but did not contain clinically relevant independent prognostic information for cardiovascular events., Key Points: • Pulmonary CT biomarkers and spirometry are associated with cardiovascular events • These pulmonary measurements do not contain clinically relevant independent prognostic information • Only coronary calcium score improved cardiovascular risk prediction above age and smoking.

Background: The lung volume reduction (LVR) coil is a minimally invasive bronchoscopic nitinol device designed to reduce hyperinflation and improve elastic recoil in severe emphysema. We investigated the feasibility, safety and efficacy of LVR coil treatment in a prospective multicentre cohort trial in patients with severe emphysema., Methods: Patients were treated in 11 centres. Safety was evaluated by recording all adverse events, efficacy by the St George's Respiratory Questionnaire (SGRQ) as primary endpoint, and pulmonary function testing, modified Medical Research Council dyspnoea score (mMRC) and 6-min walk distance (6MWD) up to 12 months after the final treatment., Results: Sixty patients (60.9 ± 7.5 years, forced expiratory volume in 1 s (FEV(1)) 30.2 ± 6.3% pred) were bronchoscopically treated with coils (55 bilateral, 5 unilateral), with a median of 10 (range 5-15) coils per lobe. Within 30 days post-treatment, seven chronic obstructive pulmonary disease exacerbations (6.1%), six pneumonias (5.2%), four pneumothoraces (3.5%) and one haemoptysis (0.9%) occurred as serious adverse events. At 6 and 12 months, respectively, ΔSGRQ was -12.1±12.9 and -11.1±13.3 points, Δ6MWD was +29.7±74.1 m and +51.4±76 m, ΔFEV(1) was +0.11±0.20 L and +0.11±0.30 L, and ΔRV (residual volume) was -0.65±0.90 L and -0.71±0.81 L (all p<0.01). Post hoc analyses showed significant responses for SGRQ, 6MWD and RV in patients with both heterogeneous and homogeneous emphysema., Conclusions: LVR coil treatment results in significant clinical improvements in patients with severe emphysema, with a good safety profile and sustained results for up to 1 year., Trial Registration Number: NCT01328899., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.)

The VESSEL12 (VESsel SEgmentation in the Lung) challenge objectively compares the performance of different algorithms to identify vessels in thoracic computed tomography (CT) scans. Vessel segmentation is fundamental in computer aided processing of data generated by 3D imaging modalities. As manual vessel segmentation is prohibitively time consuming, any real world application requires some form of automation. Several approaches exist for automated vessel segmentation, but judging their relative merits is difficult due to a lack of standardized evaluation. We present an annotated reference dataset containing 20 CT scans and propose nine categories to perform a comprehensive evaluation of vessel segmentation algorithms from both academia and industry. Twenty algorithms participated in the VESSEL12 challenge, held at International Symposium on Biomedical Imaging (ISBI) 2012. All results have been published at the VESSEL12 website http://vessel12.grand-challenge.org. The challenge remains ongoing and open to new participants. Our three contributions are: (1) an annotated reference dataset available online for evaluation of new algorithms; (2) a quantitative scoring system for objective comparison of algorithms; and (3) performance analysis of the strengths and weaknesses of the various vessel segmentation methods in the presence of various lung diseases., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Emphysema, airway wall thickening and air trapping are associated with chronic obstructive pulmonary disease (COPD). All three can be quantified by computed tomography (CT) of the chest. The goal of the current study is to determine the relative contribution of CT derived parameters on spirometry, lung volume and lung diffusion testing. Emphysema, airway wall thickening and air trapping were quantified automatically on CT in 1,138 male smokers with and without COPD. Emphysema was quantified by the percentage of voxels below -950 Hounsfield Units (HU), airway wall thickness by the square root of wall area for a theoretical airway with 10 mm lumen perimeter (Pi10) and air trapping by the ratio of mean lung density at expiration and inspiration (E/I-ratio). Spirometry, residual volume to total lung capacity (RV/TLC) and diffusion capacity (Kco) were obtained. Standardized regression coefficients (β) were used to analyze the relative contribution of CT changes to pulmonary function measures. The independent contribution of the three CT measures differed per lung function parameter. For the FEV1 airway wall thickness was the most contributing structural lung change (β = -0.46), while for the FEV1/FVC this was emphysema (β = -0.55). For the residual volume (RV) air trapping was most contributing (β = -0.35). Lung diffusion capacity was most influenced by emphysema (β = -0.42). In a cohort of smokers with and without COPD the effect of different CT changes varies per lung function measure and therefore emphysema, airway wall thickness and air trapping need to be taken in account.

Purpose: Many medical image analysis systems require segmentation of the structures of interest as a first step. For scans with gross pathology, automatic segmentation methods may fail. The authors' aim is to develop a versatile, fast, and reliable interactive system to segment anatomical structures. In this study, this system was used for segmenting lungs in challenging thoracic computed tomography (CT) scans., Methods: In volumetric thoracic CT scans, the chest is segmented and divided into 3D volumes of interest (VOIs), containing voxels with similar densities. These VOIs are automatically labeled as either lung tissue or nonlung tissue. The automatic labeling results can be corrected using an interactive or a supervised interactive approach. When using the supervised interactive system, the user is shown the classification results per slice, whereupon he/she can adjust incorrect labels. The system is retrained continuously, taking the corrections and approvals of the user into account. In this way, the system learns to make a better distinction between lung tissue and nonlung tissue. When using the interactive framework without supervised learning, the user corrects all incorrectly labeled VOIs manually. Both interactive segmentation tools were tested on 32 volumetric CT scans of pigs, mice and humans, containing pulmonary abnormalities., Results: On average, supervised interactive lung segmentation took under 9 min of user interaction. Algorithm computing time was 2 min on average, but can easily be reduced. On average, 2.0% of all VOIs in a scan had to be relabeled. Lung segmentation using the interactive segmentation method took on average 13 min and involved relabeling 3.0% of all VOIs on average. The resulting segmentations correspond well to manual delineations of eight axial slices per scan, with an average Dice similarity coefficient of 0.933., Conclusions: The authors have developed two fast and reliable methods for interactive lung segmentation in challenging chest CT images. Both systems do not require prior knowledge of the scans under consideration and work on a variety of scans.

Subsolid pulmonary nodules occur less often than solid pulmonary nodules, but show a much higher malignancy rate. Therefore, accurate detection of this type of pulmonary nodules is crucial. In this work, a computer-aided detection (CAD) system for subsolid nodules in computed tomography images is presented and evaluated on a large data set from a multi-center lung cancer screening trial. The paper describes the different components of the CAD system and presents experiments to optimize the performance of the proposed CAD system. A rich set of 128 features is defined for subsolid nodule candidates. In addition to previously used intensity, shape and texture features, a novel set of context features is introduced. Experiments show that these features significantly improve the classification performance. Optimization and training of the CAD system is performed on a large training set from one site of a lung cancer screening trial. Performance analysis on an independent test from another site of the trial shows that the proposed system reaches a sensitivity of 80% at an average of only 1.0 false positive detections per scan. A retrospective analysis of the output of the CAD system by an experienced thoracic radiologist shows that the CAD system is able to find subsolid nodules which were not contained in the screening database., (Copyright © 2013 Elsevier B.V. All rights reserved.)

Background: Finding phenotypes within COPD patients may prove imperative for optimizing treatment and prognosis. We hypothesized that it would be possible to discriminate emphysematous, large airway wall thickening and small airways disease dominant phenotypes., Methods: Inspiratory and expiratory CTs were performed in 1140 male smokers without or with mild COPD to quantify emphysema, airway wall thickness and air trapping. Spirometry, residual volume to total lung capacity (RV/TLC) and diffusion capacity (Kco) were measured. Dominant phenotype (emphysema, airway wall thickening or air trapping dominant) was defined as one of the respective CT measure in the upper quartile, with the other measures not in the upper quartile., Results: 573 subjects had any of the three CT measures in the upper quartile. Of these, 367 (64%) were in a single dominant group and 206 (36%) were in a mixed group. Airway wall thickening dominance was associated with younger age (p < 0.001), higher body mass index (p < 0.001), more wheezing (p < 0.05) and lower FEV1 %predicted (p < 0.001). Emphysema dominant subjects had lower FEV1/FVC (p < 0.05) and Kco %predicted (p < 0.05). There was no significant difference in respiratory related hospitalizations (p = 0.09)., Conclusion: CT measures can discriminate three different CT dominant groups of disease in male smokers without or with mild COPD., Trial Registration Number: ISRCTN63545820, registered at www.trialregister.nl., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Background: In patients with heterogeneous emphysema, surgical and bronchoscopic lung volume reduction (LVR) treatments are available. However, for patients with homogeneous emphysema these treatments are hardly investigated and seem less effective. Bronchoscopic LVR coil treatment has been shown to be effective in patients with heterogeneous emphysema, but this treatment has not been exclusively investigated in homogeneous emphysema., Objectives: The aim of this study was to investigate the safety and efficacy of LVR coil treatment in patients with homogeneous emphysema., Methods: In this single-arm, open-label study, patients received a maximum of 12 LVR coils (PneumRx Inc., Mountain View, Calif., USA) in each upper lobe in two sequential procedures. Tests were performed at baseline and at 6 months. The primary endpoint was the improvement from baseline in 6-min walking distance (6MWD) after treatment., Results: Ten patients with severe airway obstruction and hyperinflation were treated. A median of 11 (range 10-12) coils were placed in each lung. Two chronic obstructive pulmonary disease exacerbations and one small pneumothorax were recorded as serious adverse events. At 6 months, 6MWD had improved from 289 to 350 m (p = 0.005); forced vital capacity from 2.17 to 2.55 liters (p = 0.047); residual volume from 5.04 to 4.44 liters (p = 0.007) and St. George's Respiratory Questionnaire from 63 to 48 points (p = 0.028)., Conclusion: LVR coil treatment in homogeneous patients improves hyperinflation, airway resistance, exercise capacity and quality of life with an acceptable safety profile. The benefit of LVR coil treatment is not limited to patients with heterogeneous emphysema, and patients with homogenous emphysema can benefit as well.

Computed tomography (CT) is the modality of choice for imaging the lungs in vivo. Sub-millimeter isotropic images of the lungs can be obtained within seconds, allowing the detection of small lesions and detailed analysis of disease processes. The high resolution of thoracic CT and the high prevalence of lung diseases require a high degree of automation in the analysis pipeline. The automated segmentation of pulmonary structures in thoracic CT has been an important research topic for over a decade now. This systematic review provides an overview of current literature. We discuss segmentation methods for the lungs, the pulmonary vasculature, the airways, including airway tree construction and airway wall segmentation, the fissures, the lobes and the pulmonary segments. For each topic, the current state of the art is summarized, and topics for future research are identified.

Purpose: Ensemble segmentation methods combine the segmentation results of individual methods into a final one, with the goal of achieving greater robustness and accuracy. The goal of this study was to develop an ensemble segmentation framework for glioblastoma multiforme tumors on single-channel T1w postcontrast magnetic resonance images., Methods: Three base methods were evaluated in the framework: fuzzy connectedness, GrowCut, and voxel classification using support vector machine. A confidence map averaging (CMA) method was used as the ensemble rule., Results: The performance is evaluated on a comprehensive dataset of 46 cases including different tumor appearances. The accuracy of the segmentation result was evaluated using the F1-measure between the semiautomated segmentation result and the ground truth., Conclusions: The results showed that the CMA ensemble result statistically approximates the best segmentation result of all the base methods for each case.

Background: There is increasing evidence that structural lung changes may be present before the occurrence of airflow limitation as assessed by spirometry. This study investigated the prevalence of computed tomography (CT) quantified emphysema, airway wall thickening and gas trapping according to classification of airflow limitation (FEV1/FVC <70% and/or < the lower limit of normal (LLN)) in (heavy) smokers., Methods: A total number of 1,140 male former and current smokers participating in a lung cancer screenings trial (NELSON) were included and underwent chest CT scanning and spirometry. Emphysema was quantified by the 15(th) percentile, air way wall thickening by the square root of wall area for a theoretical airway with 10mm lumen perimeter (Pi10) and gas trapping by the mean lung density expiratory/inspiratory (E/I)-ratio. Participants were classified by entry FEV1/FVC: group 1>70%; group 2<70% but >LLN; and group 370% but FEV1 <80% predicted, were excluded. Multivariate regression analysis correcting for covariates was used to asses the extent of emphysema, airway wall thickening and gas trapping according to three groups of airflow limitation., Results: Mean (standard deviation) age was 62.5 (5.2) years and packyears smoked was 41.0 (18.0). Group 2 subjects when compared to group 1 had a significantly lower 15(th) percentile, -920.6 HU versus -912.2 HU; a higher Pi10, 2.87 mm versus 2.57 mm; and a higher E/I-ratio, 88.6% versus 85.6% (all p<0.001)., Conclusion: Subjects with an FEV1/FVC<70%, but above the LLN, have a significant greater degree of structural lung changes on CT compared to subjects without airflow limitation.

Background: Beyond lung cancer, screening CT contains additional information on other smoking related diseases (e.g. chronic obstructive pulmonary disease, COPD). Since pulmonary function testing is not regularly incorporated in lung cancer screening, imaging biomarkers for COPD are likely to provide important surrogate measures for disease evaluation. Therefore, this study aims to determine the independent diagnostic value of CT emphysema, CT air trapping and CT bronchial wall thickness for COPD in low-dose screening CT scans., Methods: Prebronchodilator spirometry and volumetric inspiratory and expiratory chest CT were obtained on the same day in 1140 male lung cancer screening participants. Emphysema, air trapping and bronchial wall thickness were automatically quantified in the CT scans. Logistic regression analysis was performed to derivate a model to diagnose COPD. The model was internally validated using bootstrapping techniques., Results: Each of the three CT biomarkers independently contributed diagnostic value for COPD, additional to age, body mass index, smoking history and smoking status. The diagnostic model that included all three CT biomarkers had a sensitivity and specificity of 73.2% and 88.%, respectively. The positive and negative predictive value were 80.2% and 84.2%, respectively. Of all participants, 82.8% was assigned the correct status. The C-statistic was 0.87, and the Net Reclassification Index compared to a model without any CT biomarkers was 44.4%. However, the added value of the expiratory CT data was limited, with an increase in Net Reclassification Index of 4.5% compared to a model with only inspiratory CT data., Conclusion: Quantitatively assessed CT emphysema, air trapping and bronchial wall thickness each contain independent diagnostic information for COPD, and these imaging biomarkers might prove useful in the absence of lung function testing and may influence lung cancer screening strategy. Inspiratory CT biomarkers alone may be sufficient to identify patients with COPD in lung cancer screening setting.

Segmentation of the pulmonary lobes is relevant in clinical practice and particularly challenging for cases with severe diseases or incomplete fissures. In this work, an automated segmentation approach is presented that performs a marker-based watershed transformation on computed tomography (CT) scans to subdivide the lungs into lobes. A cost image for the watershed transformation is computed by combining information from fissures, bronchi, and pulmonary vessels. The lobar markers are calculated by an analysis of the automatically labeled bronchial tree. By integration of information from several anatomical structures the segmentation is made robust against incomplete fissures. For evaluation the method was compared to a recently published method on 20 CT scans with no or mild disease. The average distances to the reference segmentation were 0.69, 0.67, and 1.21 mm for the left major, right major, and right minor fissure, respectively. In addition the results were submitted to LOLA11, an international lung lobe segmentation challenge with publically available data including cases with severe diseases. The average distances to the reference for the 55 CT scans provided by LOLA11 were 0.98, 3.97, and 3.09 mm for the left major, right major, and right minor fissure. Moreover, an analysis of the relation between segmentation quality and fissure completeness showed that the method is robust against incomplete fissures.

This paper describes a framework for establishing a reference airway tree segmentation, which was used to quantitatively evaluate fifteen different airway tree extraction algorithms in a standardized manner. Because of the sheer difficulty involved in manually constructing a complete reference standard from scratch, we propose to construct the reference using results from all algorithms that are to be evaluated. We start by subdividing each segmented airway tree into its individual branch segments. Each branch segment is then visually scored by trained observers to determine whether or not it is a correctly segmented part of the airway tree. Finally, the reference airway trees are constructed by taking the union of all correctly extracted branch segments. Fifteen airway tree extraction algorithms from different research groups are evaluated on a diverse set of twenty chest computed tomography (CT) scans of subjects ranging from healthy volunteers to patients with severe pathologies, scanned at different sites, with different CT scanner brands, models, and scanning protocols. Three performance measures covering different aspects of segmentation quality were computed for all participating algorithms. Results from the evaluation showed that no single algorithm could extract more than an average of 74% of the total length of all branches in the reference standard, indicating substantial differences between the algorithms. A fusion scheme that obtained superior results is presented, demonstrating that there is complementary information provided by the different algorithms and there is still room for further improvements in airway segmentation algorithms.

Purpose: To analyze pulmonary function using a fully automatic technique which processes pairs of thoracic CT scans acquired at breath-hold inspiration and expiration, respectively. The following research objectives are identified to: (a) describe and systematically analyze the processing pipeline and its results; (b) verify that the quantitative, regional ventilation measurements acquired through CT are meaningful for pulmonary function analysis; (c) identify the most effective of the calculated measurements in predicting pulmonary function; and (d) demonstrate the potential of the system to deliver clinically important information not available through conventional spirometry., Methods: A pipeline of automatic segmentation and registration techniques is presented and demonstrated on a database of 216 subjects well distributed over the various stages of COPD (chronic obstructive pulmonary disorder). Lungs, fissures, airways, lobes, and vessels are automatically segmented in both scans and the expiration scan is registered with the inspiration scan using a fully automatic nonrigid registration algorithm. Segmentations and registrations are examined and scored by expert observers to analyze the accuracy of the automatic methods. Quantitative measures representing ventilation are computed at every image voxel and analyzed to provide information about pulmonary function, both globally and on a regional basis. These CT derived measurements are correlated with results from spirometry tests and used as features in a kNN classifier to assign COPD global initiative for obstructive lung disease (GOLD) stage., Results: The steps of anatomical segmentation (of lungs, lobes, and vessels) and registration in the workflow were shown to perform very well on an individual basis. All CT-derived measures were found to have good correlation with spirometry results, with several having correlation coefficients, r, in the range of 0.85-0.90. The best performing kNN classifier succeeded in classifying 67% of subjects into the correct COPD GOLD stage, with a further 29% assigned to a class neighboring the correct one., Conclusions: Pulmonary function information can be obtained from thoracic CT scans using the automatic pipeline described in this work. This preliminary demonstration of the system already highlights a number of points of clinical importance such as the fact that an inspiration scan alone is not optimal for predicting pulmonary function. It also permits measurement of ventilation on a per lobe basis which reveals, for example, that the condition of the lower lobes contributes most to the pulmonary function of the subject. It is expected that this type of regional analysis will be instrumental in advancing the understanding of multiple pulmonary diseases in the future.

Objectives: To propose and evaluate a technique for automatic quantification of fissural completeness from chest computed tomography (CT) in a database of subjects with severe emphysema., Methods: Ninety-six CT studies of patients with severe emphysema were included. The lungs, fissures and lobes were automatically segmented. The completeness of the fissures was calculated as the percentage of the lobar border defined by a fissure. The completeness score of the automatic method was compared with a visual consensus read by three radiologists using boxplots, rank sum tests and ROC analysis., Results: The consensus read found 49% (47/96), 15% (14/96) and 67% (64/96) of the right major, right minor and left major fissures to be complete. For all fissures visually assessed as being complete the automatic method resulted in significantly higher completeness scores (mean 92.78%) than for those assessed as being partial or absent (mean 77.16%; all p values <0.001). The areas under the curves for the automatic fissural completeness were 0.88, 0.91 and 0.83 for the right major, right minor and left major fissures respectively., Conclusions: An automatic method is able to quantify fissural completeness in a cohort of subjects with severe emphysema consistent with a visual consensus read of three radiologists., Key Points: • Lobar fissures are important for assessing the extent and distribution of lung disease • Modern CT allows automatic lobar segmentation and assessment of the fissures • This segmentation can also assess the completeness of the fissures. • Such assessment is important for decisions about novel therapies (eg for emphysema).

Objectives: The reproducibilities of CT lung volume and densitometric measures of emphysema were assessed over 1 week. The influence of breathhold on reproducibility was assessed., Methods: HRCT was performed on 44 subjects at inspiration on two visits with a 7-day interval. CT lung volume, relative area below -950HU (RA950-raw), and 15th percentile density (PD15-raw) were computed. Volume correction was used to obtain RA950-adj and PD15-adj. Reproducibilities between visits were assessed using concordance correlation coefficient (CCC) and repeatability coefficient (RC). Reproducibilities were compared between raw and adjusted measures. Differences between visits were computed for volume and density measures. Correlations were computed for density differences versus volume difference. Subgroup analysis was performed using a 0.25 L volume difference threshold., Results: High CCC were observed for all measures in full group (CCC > 0.97). Reproducibilities of volume (RC = 0.67 L), RA950-raw (RC = 2.3%), and PD15-raw (RC = 10.6HU) were observed. Volume correction significantly improved PD15 (RC = 3.6HU) but not RA950 (RC = 1.7%). RA950-raw and PD15-raw had significantly better RC in <0.25 L subgroup than ≥0.25 L. Significant correlations with volume were observed for RA950-raw and PD15-raw (R (2) > 0.71), but not RA950-adj or PD15-adj (R (2) < 0.11)., Conclusions: Good breathhold and RA950 reproducibilities were achieved. PD15 was less reproducible but improved with volume correction or superior breathhold reproduction., Key Points: • Good breath-hold reproducibility is achievable between multiple CT examinations. • Reproducibility of densitometric measures may be improved by statistical volume correction. • Volume correction may result in decreased signal. • Densitometric reproducibility may also be improved by achieving good breath-hold reproduction. • Careful consideration of signal and noise is necessary in reproducibility assessment.

A method for automatic segmentation of pulmonary lobes from computed tomography (CT) scans is presented that is robust against incomplete fissures. The method is based on a multiatlas approach in which existing lobar segmentations are deformed to test scans in which the fissures, the lungs, and the bronchial tree have been automatically segmented. The key element of our method is a cost function that exploits information from fissures, lung borders, and bronchial tree in an effective way, such that less reliable information (lungs, airways) is only used when the most reliable information (fissures) is missing. To cope with the anatomical variation in lobe shape, an atlas selection mechanism is introduced. The method is evaluated on two test sets of 120 scans in total. The results show that the lobe segmentation closely follows the fissures when they are present. In a simulated experiment in which parts of complete fissures are removed, the robustness of the method against different levels of incomplete fissures is shown. When the fissures are incomplete, an observer study shows agreement of the automatically determined lobe borders with a radiologist for 81% of the lobe borders on average.

Atlas-based segmentation is a powerful generic technique for automatic delineation of structures in volumetric images. Several studies have shown that multi-atlas segmentation methods outperform schemes that use only a single atlas, but running multiple registrations on volumetric data is time-consuming. Moreover, for many scans or regions within scans, a large number of atlases may not be required to achieve good segmentation performance and may even deteriorate the results. It would therefore be worthwhile to include the decision which and how many atlases to use for a particular target scan in the segmentation process. To this end, we propose two generally applicable multi-atlas segmentation methods, adaptive multi-atlas segmentation (AMAS) and adaptive local multi-atlas segmentation (ALMAS). AMAS automatically selects the most appropriate atlases for a target image and automatically stops registering atlases when no further improvement is expected. ALMAS takes this concept one step further by locally deciding how many and which atlases are needed to segment a target image. The methods employ a computationally cheap atlas selection strategy, an automatic stopping criterion, and a technique to locally inspect registration results and determine how much improvement can be expected from further registrations. AMAS and ALMAS were applied to segmentation of the heart in computed tomography scans of the chest and compared to a conventional multi-atlas method (MAS). The results show that ALMAS achieves the same performance as MAS at a much lower computational cost. When the available segmentation time is fixed, both AMAS and ALMAS perform significantly better than MAS. In addition, AMAS was applied to an online segmentation challenge for delineation of the caudate nucleus in brain MRI scans where it achieved the best score of all results submitted to date.