Search

Background: To retrospectively assess the added value of an artificial intelligence (AI) algorithm for detecting pulmonary nodules on ultra-low-dose computed tomography (ULDCT) performed at the emergency department (ED)., Methods: In the OPTIMACT trial, 870 patients with suspected nontraumatic pulmonary disease underwent ULDCT. The ED radiologist prospectively read the examinations and reported incidental pulmonary nodules requiring follow-up. All ULDCTs were processed post hoc using an AI deep learning software marking pulmonary nodules ≥ 6 mm. Three chest radiologists independently reviewed the subset of ULDCTs with either prospectively detected incidental nodules in 35/870 patients or AI marks in 458/870 patients; findings scored as nodules by at least two chest radiologists were used as true positive reference standard. Proportions of true and false positives were compared., Results: During the OPTIMACT study, 59 incidental pulmonary nodules requiring follow-up were prospectively reported. In the current analysis, 18/59 (30.5%) nodules were scored as true positive while 104/1,862 (5.6%) AI marks in 84/870 patients (9.7%) were scored as true positive. Overall, 5.8 times more (104 versus 18) true positive pulmonary nodules were detected with the use of AI, at the expense of 42.9 times more (1,758 versus 41) false positives. There was a median number of 1 (IQR: 0-2) AI mark per ULDCT., Conclusion: The use of AI on ULDCT in patients suspected of pulmonary disease in an emergency setting results in the detection of many more incidental pulmonary nodules requiring follow-up (5.8×) with a high trade-off in terms of false positives (42.9×)., Relevance Statement: AI aids in the detection of incidental pulmonary nodules that require follow-up at chest-CT, aiding early pulmonary cancer detection but also results in an increase of false positive results that are mainly clustered in patients with major abnormalities., Trial Registration: The OPTIMACT trial was registered on 6 December 2016 in the National Trial Register (number NTR6163) (onderzoekmetmensen.nl)., Key Points: An AI deep learning algorithm was tested on 870 ULDCT examinations acquired in the ED. AI detected 5.8 times more pulmonary nodules requiring follow-up (true positives). AI resulted in the detection of 42.9 times more false positive results, clustered in patients with major abnormalities. AI in the ED setting may aid in early pulmonary cancer detection with a high trade-off in terms of false positives., Competing Interests: Declarations. Ethics declarations: Written informed consent was provided by all study participants of the OPTIMACT trial. The Ethics Committee of the Amsterdam UMC location AMC waived additional approval of this secondary analysis of the OPTIMACT data. Consent for publication: Not applicable. Competing interests: The host institution of CJ, Radboud University Medical Center, has received research grants and royalties from MeVis Medical Solutions, Bremen, Germany. JS is a member of the Scientific Editorial Board for European Radiology Experimental (section: abdomen/gastrointestinal). They did not participate in the selection nor review processes for this article. All other authors declare that they have no competing interests., (© 2024. The Author(s).)

Objectives: Lung-RADS represents a categorical system published by the American College of Radiology to standardise management in lung cancer screening. The purpose of the study was to quantify how well readers agree in assigning Lung-RADS categories to screening CTs; secondary goals were to assess causes of disagreement and evaluate its impact on patient management., Methods: For the observer study, 80 baseline and 80 follow-up scans were randomly selected from the NLST trial covering all Lung-RADS categories in an equal distribution. Agreement of seven observers was analysed using Cohen's kappa statistics. Discrepancies were correlated with patient management, test performance and diagnosis of malignancy within the scan year., Results: Pairwise interobserver agreement was substantial (mean kappa 0.67, 95% CI 0.58-0.77). Lung-RADS category disagreement was seen in approximately one-third (29%, 971) of 3360 reading pairs, resulting in different patient management in 8% (278/3360). Out of the 91 reading pairs that referred to scans with a tumour diagnosis within 1 year, discrepancies in only two would have resulted in a substantial management change., Conclusions: Assignment of lung cancer screening CT scans to Lung-RADS categories achieves substantial interobserver agreement. Impact of disagreement on categorisation of malignant nodules was low., Key Points: • Lung-RADS categorisation of low-dose lung screening CTs achieved substantial interobserver agreement. • Major cause for disagreement was assigning a different nodule as risk-dominant. • Disagreement led to a different follow-up time in 8% of reading pairs.

It is well known that lung cancer can manifest itself in imaging as solid and subsolid nodules or masses. However, in this era of increased computed tomography use another morphological computed tomography appearance of lung cancer is increasingly being recognised, presenting as a malignancy in relation to cystic airspaces. Despite the fact that it seems to be a relatively common finding in daily practice, literature on this entity is scarce and presumably the overall awareness is limited. This can lead to misinterpretation and delay in diagnosis and, therefore, increased awareness is urgently needed. This review aims to illustrate the imaging appearances of cyst-related primary lung malignancies, demonstrate its mimickers and potential pitfalls, and discuss the clinical implications based on the available literature and our own experience in four different hospitals., Competing Interests: Conflict of interest: O.M. Mets reports personal fees (as an expert panel member) from Aidence, outside the submitted work. Conflict of interest: C.M. Schaefer-Prokop has nothing to disclose. Conflict of interest: P.A. de Jong has nothing to disclose., (Copyright ©ERS 2018.)

Current pulmonary nodule management guidelines are based on nodule volume doubling time, which assumes exponential growth behaviour. However, this is a theory that has never been validated in vivo in the routine-care target population. This study evaluates growth patterns of untreated solid and subsolid lung cancers of various histologies in a non-screening setting.Growth behaviour of pathology-proven lung cancers from two academic centres that were imaged at least three times before diagnosis (n=60) was analysed using dedicated software. Random-intercept random-slope mixed-models analysis was applied to test which growth pattern most accurately described lung cancer growth. Individual growth curves were plotted per pathology subgroup and nodule type.We confirmed that growth in both subsolid and solid lung cancers is best explained by an exponential model. However, subsolid lesions generally progress slower than solid ones. Baseline lesion volume was not related to growth, indicating that smaller lesions do not grow slower compared to larger ones.By showing that lung cancer conforms to exponential growth we provide the first experimental basis in the routine-care setting for the assumption made in volume doubling time analysis., Competing Interests: Conflict of interest: B. van Ginneken reports that he is a founder and shareholder of Thirona, and received grants and royalties from MEVIS Medical Solutions. Conflict of interest: M. Prokop reports receiving speaker's fees from Bracco, Bayer, Toshiba and Siemens. He received research funding from Toshiba., (Copyright ©ERS 2018.)

Objectives: To determine the presence and morphology of subsolid pulmonary nodules (SSNs) in a non-screening setting and relate them to clinical and patient characteristics., Methods: A total of 16,890 reports of clinically obtained chest CT (06/2011 to 11/2014, single-centre) were searched describing an SSN. Subjects with a visually confirmed SSN and at least two thin-slice CTs were included. Nodule volumes were measured. Progression was defined as volume increase exceeding the software interscan variation. Nodule morphology, location, and patient characteristics were evaluated., Results: Fifteen transient and 74 persistent SSNs were included (median follow-up 19.6 [8.3-36.8] months). Subjects with an SSN were slightly older than those without (62 vs. 58 years; p = 0.01), but no gender predilection was found. SSNs were mostly located in the upper lobes. Women showed significantly more often persistent lesions than men (94 % vs. 69 %; p = 0.002). Part-solid lesions were larger (1638 vs. 383 mm 3 ; p < 0.001) and more often progressive (68 % vs. 38 %; p = 0.02), compared to pure ground-glass nodules. Progressive SSNs were rare under the age of 50 years. Logistic regression analysis did not identify additional nodule parameters of future progression, apart from part-solid nature., Conclusions: This study confirms previously reported characteristics of SSNs and associated factors in a European, routine clinical population., Key Points: • SSNs in women are significantly more often persistent compared to men. • SSN persistence is not associated with age or prior malignancy. • The majority of (persistent) SSNs are located in the upper lung lobes. • A part-solid nature is associated with future nodule growth. • Progressive solitary SSNs are rare under the age of 50 years.

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: The aim of this study was to assess awareness and conformance to the Fleischner society recommendations for the management of subsolid pulmonary nodules (SSN) in clinical practice., Methods: An online questionnaire with four imaging cases was sent to 1579 associates from the European Respiratory Society and 757 from the European Society of Thoracic Imaging. Each respondent was asked to choose from several options which one they thought was the indicated management for the nodule presented. Awareness and conformance to the Fleischner recommendations (FR) were assessed and correlated to respondents characteristics., Results: In total, 119 radiologists (response rate 16.0 %) and 243 pulmonologists (response rate 16.5 %) were included. Awareness of the FR was higher in radiologists than in pulmonologists (93 % vs. 70 %, p < 0.001), as was implementation in daily practice (66 % vs. 47 %, p < 0.001). Radiologists conformed to FR in rates of 31, 69, 68, and 82 %, and pulmonologists in 12, 43, 70, and 75 % for cases 1 to 4, respectively. Overmanagement was common. Conformance in SSN management was associated with awareness, working in an academic practice, larger practice size, teaching residents, and higher SSN exposure., Conclusions: Although awareness of the Fleischner recommendations for SSN management is widespread, management choices in clinical practice show large heterogeneity., Key Points: • Guideline awareness among clinicians is widespread, but conformance shows large heterogeneity. • Awareness and conformance is significantly higher among radiologists than pulmonologists. • Overmanagement is common, which may lead to avoidable financial and physical burden.

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: 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.

Purpose: We aimed to study the association between lung function decline and quantitative computed tomography (CT) air trapping., Materials and Methods: Current and former heavy smokers in a lung cancer screening trial underwent volumetric low-dose CT in inspiration and expiration. Spirometry was obtained at baseline and after 3 years. The expiratory to inspiratory ratio of mean lung density (E/I-ratioMLD) was used to quantify air trapping. CT emphysema was defined as voxels in inspiratory CT below -950 Hounsfield Unit. Linear mixed modeling was used to determine the association between CT air trapping and lung function., Results: We included 985 subjects with a mean age of 61.3 years. Independent of CT emphysema, CT air trapping was significantly associated with a reduction in forced expiratory volume in one second (FEV1) and the ratio of FEV1 over the forced vital capacity (FEV1/FVC); FEV1 declines with 33 mL per percent increase in CT air trapping, while FEV1/FVC declines 0.58% per percent increase (both p<0.001). CT air trapping further elicits accelerated loss of FEV1/FVC (additional 0.24% reduction per percent increase; p = 0.014)., Conclusion: In a lung cancer screening cohort, quantitatively assessed air trapping on low-dose CT is independently associated with reduced lung function and accelerated decline of FEV1/FVC.

Objectives: To determine the variation in quantitative computed tomography (CT) measures of air trapping in low-dose chest CTs of heavy smokers., Methods: We analysed 45 subjects from a lung cancer screening trial, examined by CT twice within 3 months. Inspiratory and expiratory low-dose CT was obtained using breath hold instructions. CT air trapping was defined as the percentage of voxels in expiratory CT with an attenuation below -856 HU (EXP(-856)) and the expiratory to inspiratory ratio of mean lung density (E/I-ratio(MLD)). Variation was determined using limits of agreement, defined as 1.96 times the standard deviation of the mean difference. The effect of both lung volume correction and breath hold reproducibility was determined., Results: The limits of agreement for uncorrected CT air trapping measurements were -15.0 to 11.7 % (EXP(-856)) and -9.8 to 8.0 % (E/I-ratio(MLD)). Good breath hold reproducibility significantly narrowed the limits for EXP(-856) (-10.7 to 7.5 %, P = 0.002), but not for E/I-ratio(MLD) (-9.2 to 7.9 %, P = 0.75). Statistical lung volume correction did not improve the limits for EXP(-856) (-12.5 to 8.8 %, P = 0.12) and E/I-ratio(MLD) (-7.5 to 5.8 %, P = 0.17)., Conclusions: Quantitative air trapping measures on low-dose CT of heavy smokers show considerable variation on repeat CT examinations, regardless of lung volume correction or reproducible breath holds., Key Points: Computed tomography quantitatively measures small airways disease in heavy smokers. Measurements of air trapping vary considerably on repeat CT examinations. Variation remains substantial even with reproducible breath holds and lung volume correction.

Background: Lung cancer screening CT scans might provide valuable information about air trapping as an early indicator of smoking-related lung disease. We studied which of the currently suggested measures is most suitable for detecting functionally relevant air trapping on low-dose computed tomography (CT) in a population of subjects with early-stage disease., Methods: This study was ethically approved and informed consent was obtained. Three quantitative CT air trapping measures were compared against a functional reference standard in 427 male lung cancer screening participants. This reference standard for air trapping was derived from the residual volume over total lung capacity ratio (RV/TLC) beyond the 95th percentile of predicted. The following CT air trapping measures were compared: expiratory to inspiratory relative volume change of voxels with attenuation values between -860 and -950 Hounsfield Units (RVC(-860 to -950)), expiratory to inspiratory ratio of mean lung density (E/I-ratio(MLD)) and percentage of voxels below -856 HU in expiration (EXP(-856)). Receiver operating characteristic (ROC) analysis was performed and area under the ROC curve compared., Results: Functionally relevant air trapping was present in 38 (8.9 %) participants. E/I-ratio(MLD) showed the largest area under the curve (0.85, 95 % CI 0.813-0.883), which was significantly larger than RVC(-860 to -950) (0.703, 0.657-0.746; p < 0.001) and EXP(-856) (0.798, 0.757-0.835; p = 0.002). At the optimum for sensitivity and specificity, E/I-ratio(MLD) yielded an accuracy of 81.5 %., Conclusions: The expiratory to inspiratory ratio of mean lung density (E/I-ratio(MLD)) is most suitable for detecting air trapping on low-dose screening CT and performs significantly better than other suggested quantitative measures.

Objectives: To determine the influence of iterative reconstruction (IR) on quantitative computed tomography (CT) measurements of emphysema, air trapping, and airway wall and lumen dimensions, compared to filtered back-projection (FBP)., Methods: Inspiratory and expiratory chest CTs of 75 patients (37 male, 38 female; mean age 64.0 ± 5.7 years) were reconstructed using FBP and IR. CT emphysema, CT air trapping and airway dimensions of a segmental bronchus were quantified using several commonly used quantification methods. The two algorithms were compared using the concordance correlation coefficient (p (c)) and Wilcoxon signed rank test., Results: Only the E/I-ratio(MLD) as a measure of CT air trapping and airway dimensions showed no significant differences between the algorithms, whereas all CT emphysema and the other CT air trapping measures were significantly different at IR when compared to FBP (P < 0.001)., Conclusion: The evaluated IR algorithm significantly influences quantitative CT measures in the assessment of emphysema and air trapping. However, the E/I-ratio(MLD) as a measure of CT air trapping, as well as the airway measurements, is unaffected by this reconstruction method. Quantitative CT of the lungs should be performed with careful attention to the CT protocol, especially when iterative reconstruction is introduced., Key Points: • New techniques in CT allow numerous quantitative measurements of lung function. • Iterative reconstruction influences quantitative CT measurements of emphysema and air trapping. • Expiratory-to-inspiratory ratio of mean lung density and airway measurements are unaffected by iterative reconstruction. • Quantitative lung-CT should be performed with careful attention to the CT protocol.

Objective: The purpose of our study was to assess the normal range of CT measures of emphysema and air trapping in young men with normal lung function., Materials and Methods: A cohort of 70 young men with high-normal spirometry and body plethysmography underwent paired inspiratory and expiratory CT. Visual and quantitative scores of emphysema and air trapping were obtained. On CT, emphysema was defined as the 15th percentile of the attenuation curve (Perc(15)), and as the percentage of inspiratory voxels below -950 (IN(-950)) and below -960 (IN(-960)) HU. On CT, air trapping was defined as the expiratory-to-inspiratory ratio of mean lung density (EI-ratio(MLD)), and the percentage of voxels below -856 HU in expiration (EXP(-856)). Means, medians, and upper limits of normal (ULN) are presented for the total population and for smokers and nonsmokers separately., Results: The mean age (± SD) of the subjects was 36.1 ± 9.3 years. Smoking history was limited (range, 0-11 pack-years). Spirometry was high normal, ranging from 113% to 160% of predicted for vital capacity (VC), and from 104% to 140% of predicted for forced expiratory volume in 1 second (FEV(1)). The ULN was 2.73% for IN(-950), 0.87% for IN(-960), -936 HU for Perc(15), 89.0% for EI-ratio(MLD), and 17.2% for EXP(-856).Visual CT scores showed minimal emphysema in eight (11%), > 5 lobules of air trapping in five (7%), and segmental air trapping in three (4%) subjects. CT measures were similar for never- and ever-smokers., Conclusion: We report the normal range of CT values for young male subjects with normal lung function, which is important to define pulmonary disease.

Background: Incidental CT findings may provide an opportunity for early detection of chronic obstructive pulmonary disease (COPD), which may prove important in CT-based lung cancer screening setting. We aimed to determine the diagnostic performance of human observers to visually evaluate COPD presence on CT images, in comparison to automated evaluation using quantitative CT measures., Methods: This study was approved by the Dutch Ministry of Health and the institutional review board. All participants provided written informed consent. We studied 266 heavy smokers enrolled in a lung cancer screening trial. All subjects underwent volumetric inspiratory and expiratory chest computed tomography (CT). Pulmonary function testing was used as the reference standard for COPD. We evaluated the diagnostic performance of eight observers and one automated model based on quantitative CT measures., Results: The prevalence of COPD in the study population was 44% (118/266), of whom 62% (73/118) had mild disease. The diagnostic accuracy was 74.1% in the automated evaluation, and ranged between 58.3% and 74.3% for the visual evaluation of CT images. The positive predictive value was 74.3% in the automated evaluation, and ranged between 52.9% and 74.7% for the visual evaluation. Interobserver variation was substantial, even within the subgroup of experienced observers. Agreement within observers yielded kappa values between 0.28 and 0.68, regardless of the level of expertise. The agreement between the observers and the automated CT model showed kappa values of 0.12-0.35., Conclusions: Visual evaluation of COPD presence on chest CT images provides at best modest accuracy and is associated with substantial interobserver variation. Automated evaluation of COPD subjects using quantitative CT measures appears superior to visual evaluation by human observers.

Context: Smoking is a major risk factor for both cancer and chronic obstructive pulmonary disease (COPD). Computed tomography (CT)-based lung cancer screening may provide an opportunity to detect additional individuals with COPD at an early stage., Objective: To determine whether low-dose lung cancer screening CT scans can be used to identify participants with COPD., Design, Setting, and Patients: Single-center prospective cross-sectional study within an ongoing lung cancer screening trial. Prebronchodilator pulmonary function testing with inspiratory and expiratory CT on the same day was obtained from 1140 male participants between July 2007 and September 2008. Computed tomographic emphysema was defined as percentage of voxels less than -950 Hounsfield units (HU), and CT air trapping was defined as the expiratory:inspiratory ratio of mean lung density. Chronic obstructive pulmonary disease was defined as the ratio of forced expiratory volume in the first second to forced vital capacity (FEV(1)/FVC) of less than 70%. Logistic regression was used to develop a diagnostic prediction model for airflow limitation., Main Outcome Measures: Diagnostic accuracy of COPD diagnosis using pulmonary function tests as the reference standard., Results: Four hundred thirty-seven participants (38%) had COPD according to lung function testing. A diagnostic model with CT emphysema, CT air trapping, body mass index, pack-years, and smoking status corrected for overoptimism (internal validation) yielded an area under the receiver operating characteristic curve of 0.83 (95% CI, 0.81-0.86). Using the point of optimal accuracy, the model identified 274 participants with COPD with 85 false-positives, a sensitivity of 63% (95% CI, 58%-67%), specificity of 88% (95% CI, 85%-90%), positive predictive value of 76% (95% CI, 72%-81%); and negative predictive value of 79% (95% CI, 76%-82%). The diagnostic model showed an area under the receiver operating characteristic curve of 0.87 (95% CI, 0.86-0.88) for participants with symptoms and 0.78 (95% CI, 0.76-0.80) for those without symptoms., Conclusion: Among men who are current and former heavy smokers, low-dose inspiratory and expiratory CT scans obtained for lung cancer screening can identify participants with COPD, with a sensitivity of 63% and a specificity of 88%.