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Pulmonary nodules are managed on the basis of their size and morphologic characteristics. Radiologists are familiar with assessing nodule size by measuring diameter using manually deployed electronic calipers. Size may also be assessed with 3D volumetric measurements (referred to as volumetry) obtained with software. Nodule size and growth are more accurately assessed with volumetry than on the basis of diameter, and the evidence supporting clinical use of volumetry has expanded, driven by its use in lung cancer screening nodule management algorithms in Europe. The application of volumetry has the potential to reduce recommendations for imaging follow-up of indeterminate solid nodules without impacting cancer detection. Although changes in scanning conditions and volumetry software packages can lead to variation in volumetry results, ongoing technical advances have improved the reliability of calculated volumes. Volumetry is now the primary method for determining size of solid nodules in the European lung cancer screening position statement and British Thoracic Society recommendations. The purposes of this article are to review technical aspects, advantages, and limitations of volumetry and, by considering specific scenarios, to contextualize the use of volumetry with respect to its importance in morphologic evaluation, its role in predicting malignancy in risk models, and its practical impact on nodule management. Implementation challenges and areas requiring further evidence are also highlighted.

Objectives: To investigate the extent to which observer variability of computed tomography (CT) lung nodule assessment may affect clinical treatment stratification in Wilms tumour (WT) patients, according to the recent Société Internationale d'Oncologie Pédiatrique Renal Tumour Study Group (SIOP-RTSG) UMBRELLA protocol. Methods: I: CT thoraces of children with WT submitted for central review were used to estimate size distribution of lung metastases. II: Scans were selected for blinded review by five radiologists to determine intra- and inter-observer variability. They assessed identical scans on two occasions 6 months apart. III: Monte Carlo simulation (MCMC) was used to predict the clinical impact of observer variation when applying the UMBRELLA protocol size criteria. Results: Lung nodules were found in 84 out of 360 (23%) children with WT. For 21 identified lung nodules, inter-observer limits of agreement (LOA) for the five readers were ±2.4 and ±1.4 mm (AP diameter), ±1.9 and ±1.8 mm (TS diameter) and ±2.0 and ±2.4 mm (LS diameter) at assessments 1 and 2. Intra-observer LOA across the three dimensions were ±1.5, ±2.2, ±3.5, ±3.1 and ±2.6 mm (readers 1–5). MCMC demonstrated that 17% of the patients with a ‘true’ nodule size of ≥3 mm will be scored as

Early detection and diagnosis of coronary artery disease could reduce the risk of developing a heart attack. The coronary arteries are optimally visualised using computed tomography coronary angiography (CTCA) imaging. These images are reviewed by specialist radiologists who evaluate the coronary arteries for potential narrowing. A lack of radiologists in the UK is a constraint to timely diagnosis of coronary artery disease, particularly in the acute accident and emergency department setting. The development of automated methods by which coronary artery narrowing can be identified rapidly and accurately are therefore timely. Such complex computer based tools also need to be sufficiently computationally efficient that they can run on servers typically found in hospital settings, where graphical processing units for example are unavailable. We propose a fully automatic two-dimensional Unet model to segment the aorta and coronary arteries on CTCA images. Two models are trained to segment two regions of interest, (1) the aorta and the coronary arteries or (2) the coronary arteries alone. Our method achieves 91.20% and 88.80% dice similarity coefficient accuracy on regions of interest 1 and 2 respectively. Compared with a semi-automatic segmentation method, our model performs better when segmenting the coronary arteries alone. The performance of the proposed method is comparable to existing published two-dimensional or three-dimensional deep learning models. Importantly, the algorithmic and graphical processing unit memory efficiencies are maintained such that the model can be deployed without requiring graphical processing units, and therefore can be used in a hospital setting.

Introduction Longitudinal data on management and progression of type 2 diabetes mellitus (T2DM) in India are scarce. LANDMARC (CTRI/2017/05/008452), first‐of‐its‐kind, pan‐India, prospective, observational study aimed to evaluate real‐world patterns and management of T2DM over 3 years. Methods Adults (≥25 to ≤60 years old at T2DM diagnosis; diabetes duration ≥2 years at enrolment; controlled/uncontrolled on ≥2 anti‐diabetic agents) were enrolled. The first‐year trends for glycaemic control, therapy and diabetic complications, including those from metropolitan and non‐metropolitan cities are reported here. Results Of 6236 enrolled participants, 5654 completed 1 year in the study. Although the overall mean glycated haemoglobin (HbA1c) improved by 0.5% (baseline: 8.1%) at 1 year, only 20% of the participants achieved HbA1c 3 vs. ≤3 OADs. Conclusions First‐year trends of the LANDMARC study offer insights into real‐world disease progression, suggesting the need for controlling risk factors and timely treatment intensification in people with T2DM., The literature describing the burden of T2DM in India are sparse. The present study reports 1‐year trend from LANDMARC study, a nationwide, prospective, longitudinal study planned for 3 years. This study highlights some important observations in terms of diabetes complications, treatment patterns and glycaemic control in T2DM patients from India and also emphasizes the disease burden in metropolitan and non‐metropolitan cities. This study revealed that the burden of uncontrolled T2DM is high in India with only 20% of participants achieving glycaemic control in 1 year (HbA1c

Introduction Longitudinal data on progression, complications, and management of type 2 diabetes mellitus (T2DM) across India are scarce. LANDMARC (CTRI/2017/05/008452), the first pan‐India, longitudinal, prospective, observational study, aims to understand the management and real‐world outcomes of T2DM over 3 years. Methods Adults (≥25 to ≤60 years old at T2DM diagnosis; diabetes duration ≥2 years at enrollment; controlled/uncontrolled on ≥2 anti‐diabetic agents) were enrolled. Baseline characteristics were analyzed using descriptive statistics. Results Of the 6279 recruited participants, 6236 were eligible for baseline assessment (56.6% [n/N = 3528/6236] men; mean ± SD age: 52.1 ± 9.2 years, diabetes duration: 8.6 ± 5.6 years). mean ± SD HbA1c, fasting plasma glucose, and postprandial glucose values were 64 ± 17 mmol/mol (8.1 ± 1.6%), 142.8 ± 50.4 mg/dl, and 205.7 ± 72.3 mg/dl, respectively. Only 25.1% (n/N = 1122/6236) participants had controlled glycemia (HbA1c

[No Abstract Available], Wellcome Trust [209553/Z/17/Z]; UCLH Biomedical Research Centre, This study was supported by Wellcome Trust grant 209553/Z/17/Z and the UCLH Biomedical Research Centre. Funding information for this article has been deposited with the Crossref Funder Registry.

Convolutional Neural Networks (CNNs) are widely used for image classification in a variety of fields, including medical imaging. While most studies deploy cross-entropy as the loss function in such tasks, a growing number of approaches have turned to a family of contrastive learning-based losses. Even though performance metrics such as accuracy, sensitivity and specificity are regularly used for the evaluation of CNN classifiers, the features that these classifiers actually learn are rarely identified and their effect on the classification performance on out-of-distribution test samples is insufficiently explored. In this paper, motivated by the real-world task of lung nodule classification, we investigate the features that a CNN learns when trained and tested on different distributions of a synthetic dataset with controlled modes of variation. We show that different loss functions lead to different features being learned and consequently affect the generalization ability of the classifier on unseen data. This study provides some important insights into the design of deep learning solutions for medical imaging tasks., Accepted at iMIMIC, MICCAI 2021

Background: Idiopathic pulmonary fibrosis (IPF) and pleuroparenchymal fibroelastosis (PPFE) are known to have poor outcomes but detailed examinations of prognostic significance of an association between these morphologic processes are lacking. Methods: Retrospective observational study of independent derivation and validation cohorts of IPF populations. Upper-lobe PPFE extent was scored visually (vPPFE) as categories of absent, moderate, marked. Computerised upper-zone PPFE extent (cPPFE) was examined continuously and using a threshold of 2.5% pleural surface area. vPPFE and cPPFE were evaluated against 1-year FVC decline (estimated using mixed-effects models) and mortality. Multivariable models were adjusted for age, gender, smoking history, antifibrotic treatment and diffusion capacity for carbon monoxide. Findings: PPFE prevalence was 49% (derivation cohort, n = 142) and 72% (validation cohort, n = 145). vPPFE marginally contributed 3-14% to variance in interstitial lung disease (ILD) severity across both cohorts. In multivariable models, marked vPPFE was independently associated with 1-year FVC decline (derivation: regression coefficient 18.3, 95 CI 8.47-28.2%; validation: 7.51, 1.85-13.2%) and mortality (derivation: hazard ratio [HR] 7.70, 95% CI 3.50-16.9; validation: HR 3.01, 1.33-6.81). Similarly, continuous and dichotomised cPPFE were associated with 1-year FVC decline and mortality (cPPFE > 2.5% derivation: HR 5.26, 3.00-9.22; validation: HR 2.06, 1.28-3.31). Individuals with cPPFE > 2.5% or marked vPPFE had the lowest median survival, the cPPFE threshold demonstrated greater discrimination of poor outcomes at two and three years than marked vPPFE. Interpretation: PPFE quantification supports distinction of IPF patients with a worse outcome independent of established ILD severity measures. This has the potential to improve prognostic management and elucidate separate pathways of disease progression. Funding: This research was funded in whole or in part by the Wellcome Trust [209,553/Z/17/Z] and the NIHR UCLH Biomedical Research Centre, UK. (c) 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/), Wellcome Trust [209553/Z/17/Z]; NIHR UCLH Biomedical Research Centre, UK; NIHR South-ampton Biomedical Research Centre; EPSRC [EP/M020533/1], This research was funded in whole or in part by the Wellcome Trust [209553/Z/17/Z]. For the purpose of open access, the author has applied a CC-BY public copyright licence to any author accepted manuscript version arising from this submission. This project, JJ, EG and SMJ were also supported by the NIHR UCLH Biomedical Research Centre, UK. MGJ and CB acknowledge the support of the NIHR South-ampton Biomedical Research Centre. EPSRC grant EP/M020533/1 helped support this work.

As the coronavirus pandemic evolves, the focus of radiology departments has begun to change. The acute phase of imaging a new disease entity whilst rationalising radiology services in the face of lockdown has passed. Radiologists are now becoming familiar with the complications of COVID-19, particularly the lung parenchymal and pulmonary vascular sequelae and are considering the impact follow-up imaging may have on departments already struggling with a backlog of suspended imaging in the face of reduced capacity. This review from the British Society of Thoracic Imaging explores both the thoracic and extra-thoracic complications of COVID-19, recognising the importance of a holistic approach to patient follow-up. The British Thoracic Society guidelines for respiratory follow-up of COVID-19 will be discussed, together with newly developed reporting templates, which aim to provide consistency for clinicians as well as an opportunity for longer-term data collection.

The case series provides radiographers with essential information to preliminarily evaluate chest imaging and prompt the rapid diagnosis of COVID-19. Image 1

Since the emergence of the novel coronavirus SARS-CoV-2 in Wuhan, China in 2019 [1], the resulting COVID-19 disease has rapidly transitioned into a global pandemic with over 31 000 deaths and 215 000 infections in the UK as of 10th May 2020 [2]. Confirmation of SARS-CoV-2 infection requires reverse-transcriptase polymerase chain reaction (RT-PCR) testing [3]. Chest radiographic and/or computed tomography imaging is also central to diagnosis and management [4]., The National COVID-19 Chest Imaging Database (NCCID) is a repository of chest X-Ray, CT and MRI images and clinical data from COVID-19 patients across the UK, to support research and development of AI technology that may proffer insights into the disease.

In accordance with guidance from the Chief Medical Officer's office and the Royal College of Radiologists, the British Society of Thoracic Imaging (BSTI) recognises that based on the available evidence computed tomography (CT) currently has no upfront role in the diagnostic work-up of 2019 novel coronavirus (COVID-19) infection (https://www.rcr.ac.uk/college/coronavirus-covid-19-what-rcr-doing/rcr-position-role-ct-patients-suspected-covid-19). Nevertheless, a number of reports have been published highlighting CT appearances in COVID-19, raising the possibility of a role for CT in patient management.1, 2, 3, 4, 5 In response to these reports, the BSTI published a preliminary consensus statement on 6 March 2020.6 We discuss below what role, if any, CT would play in the detection and management of COVID-19 infection in the UK, and the logistics of imaging delivery. This role is heavily predicated on the clinical context as well as the timing of its intended use within the diagnostic pathway, especially relative to the current reference standard diagnostic test, real-time reverse transcriptase polymerase chain reaction (RT-PCR) of a pharyngeal swab,7 and other clinical and laboratory investigations. Although it may not be feasible or desirable for isolation purposes to perform a chest radiograph (CXR), we should acknowledge that pragmatically patients with a respiratory complaint are likely to present via any number of routes (primary care, emergency departments [EDs] or outpatient clinics) having already had a CXR, other than to isolation pods outside a hospital, and work-up of a respiratory complaint would usually include a CXR in such settings. Cognizant of this fact, in the following discussion we have considered how a CXR would also fit into diagnostic algorithms, and in particular, how the use of CT would alter management in settings where a CXR was or was not available. As such, we deliberate the following questions: (1) would a CT thorax contribute to management of symptomatic cases after a rapidly available RT-PCR result? (2) Would a CT thorax contribute to symptomatic cases if an RT-PCR test was not available or had to be rationed, and (a) a chest radiograph had been performed and was abnormal? (b) A chest radiograph had been performed and was normal, or was not/could not be performed? (3) Would a CT thorax contribute to the detection and management of COVID-19 in asymptomatic high-risk cases? (4) How should imaging (CT thorax or CXR) be provided? (5) What would a COVID-19 diagnostic algorithm look like? In the following discussion, a high pre-test probability is assumed for symptomatic cases, based on one or more of: clinical presentation (Pyrexia of 37.8); acute onset persistent cough, hoarseness, nasal discharge or congestion, shortness of breath, sore throat, wheezing, sneezing; and compatible laboratory abnormalities (relative lymphopenia, elevated C-reactive protein [CRP]).8

Increasing bacterial burden in the lower airways of patients with idiopathic pulmonary fibrosis confers an increased risk of disease progression and mortality. However, it remains unclear whether this increased bacterial burden directly influences progression of fibrosis or simply reflects the magnitude of the underlying disease extent or severity. We prospectively recruited 193 patients who underwent bronchoscopy and received a multidisciplinary diagnosis of idiopathic pulmonary fibrosis. Quantification of the total bacterial burden in bronchoalveolar lavage fluid was performed by 16S rRNA gene qPCR. Imaging was independently evaluated by two readers assigning quantitative scores for extent, severity and topography of radiographic changes and relationship of these features with bacterial burden was assessed. Increased bacterial burden significantly associated with disease progression (HR 2.1; 95% CI 1.287–3.474; p=0.0028). Multivariate stepwise regression demonstrated no relationship between bacterial burden and radiological features or extent of disease. When specifically considering patients with definite or probable usual interstitial pneumonia there was no difference in bacterial burden between these two groups. Despite a postulated association between pleuroparenchymal fibroelastosis and clinical infection, there was no relationship between either the presence or extent of pleuroparenchymal fibroelastosis and bacterial burden. We demonstrate that bacterial burden in the lower airways is not simply secondary to the extent of the underlying architectural destruction of the lung parenchyma seen in idiopathic pulmonary fibrosis. The independent nature of this association supports a relationship with the underlying pathogenic mechanisms and highlights the urgent need for functional studies., Elevated bacterial burden predicts mortality in IPF and is independent of both radiological features and extent of disease http://bit.ly/2RaDbdv

Numerous lung diseases, such as idiopathic pulmonary fibrosis (IPF), exhibit dilation of the airways. Accurate measurement of dilatation enables assessment of the progression of disease. Unfortunately the combination of image noise and airway bifurcations causes high variability in the profiles of cross-sectional areas, rendering the identification of affected regions very difficult. Here we introduce a noise-robust method for automatically detecting the location of progressive airway dilatation given two profiles of the same airway acquired at different time points. We propose a probabilistic model of abrupt relative variations between profiles and perform inference via Reversible Jump Markov Chain Monte Carlo sampling. We demonstrate the efficacy of the proposed method on two datasets; (i) images of healthy airways with simulated dilatation; (ii) pairs of real images of IPF-affected airways acquired at 1 year intervals. Our model is able to detect the starting location of airway dilatation with an accuracy of 2.5 mm on simulated data. The experiments on the IPF dataset display reasonable agreement with radiologists. We can compute a relative change in airway volume that may be useful for quantifying IPF disease progression.

Biphasic pleural mesothelioma (BPM) accounts for approximately 10% of all pleural mesothelioma. Our aim was to assess the clinical, radiologic, and pathologic factors impacting survival in BPM and to better identify patients most likely to benefit from active treatment.A 10-year retrospective review was made of 214 biopsy-proven BPM cases with minimum 2-year follow-up. Patients with insufficient tissue for analysis were excluded (n = 96). Clinical and pathologic factors were evaluated along with radiologic assessment of pleural thickness. Survival was measured from time of diagnosis. Univariable and multivariable predictors of survival were evaluated.In all, 118 patients were included; 28 underwent pleurectomy/decortication, with 27 receiving additional modalities. Ninety patients underwent chemotherapy (n = 18) or radiotherapy alone (n = 9), 63 received combination therapy, and 27 received best supportive care. Median overall survival was 11.2 months (range, 0.3 to 36.2). At univariable analysis, pleurectomy/decortication (p = 0.0061), radiotherapy (p0.0001), and chemotherapy (p0.0001) were associated with superior survival when compared with best supportive care alone. Pleurectomy/decortication demonstrated 40% survival improvement compared with no surgery (p = 0.122). In a multivariable model, necrosis was negatively prognostic (hazard ratio 2.1, SE 0.76). Furthermore, increased sarcomatoid component was associated with worse survival without radiotherapy.BPM prognosis remains poor despite multimodality treatment. Anticancer treatment is associated with superior outcome in this nonrandomized retrospective series. Our findings suggest superior survival for patients with a lower proportion of sarcomatoid disease, with selective benefit of radiotherapy in higher proportions of sarcomatoid disease. When planning active treatment, the potential survival benefits require balancing against associated morbidity and recovery period.

The accurate identification and characterization of small pulmonary nodules at low-dose CT is an essential requirement for the implementation of effective lung cancer screening. Individual reader detection performance is influenced by nodule characteristics and technical CT parameters but can be improved by training, the application of CT techniques, and by computer-aided techniques. However, the evaluation of nodule detection in lung cancer screening trials differs from the assessment of individual readers as it incorporates multiple readers, their inter-observer variability, reporting thresholds, and reflects the program accuracy in identifying lung cancer. Understanding detection and interpretation errors in screening trials aids in the implementation of lung cancer screening in clinical practice. Indeed, as CT screening moves to ever lower radiation doses, radiologists must be cognisant of new technical challenges in nodule assessment. Screen detected lung cancers demonstrate distinct morphological features from incidentally or symptomatically detected lung cancers. Hence characterization of screen detected nodules requires an awareness of emerging concepts in early lung cancer appearances and their impact on radiological assessment and malignancy prediction models. Ultimately many nodules remain indeterminate, but further imaging evaluation can be appropriate with judicious utilization of contrast enhanced CT or MRI techniques or functional evaluation by PET-CT.

Predation is a fundamental interaction between species, yet it is unclear what escape strategies are effective for prey survival. Classical theory proposes that prey should either escape in a direction that conforms to a performance optimum or that is random and therefore unpredictable. Here, we show that larval zebrafish (Danio rerio) instead use a mixed strategy that may be either random or directed. This was determined by testing classic theory with measurements of the escape direction in response to a predator robot. We found that prey consistently escaped in a direction contralateral to the robot when approached from the side of the prey's body. At such an orientation, the predator appeared in the prey's central visual field and the contralateral response was consistent with a model of strategy that maximizes the distance from the predator. By contrast, when the robot approached the rostral or caudal ends of the body, and appeared in the prey's peripheral vision, the escape showed an equal probability of a contralateral or ipsilateral direction. At this orientation, a contralateral response offered little strategic advantage. Therefore, zebrafish larvae adopt an escape strategy that maximizes distance from the threat when strategically beneficial and that is otherwise random. This sensory-mediated mixed strategy may be employed by a diversity of animals and offers a new paradigm for understanding the factors that govern prey survival.

Aim To evaluate the impact of inspiratory effort and emphysema on reproducibility of pulmonary nodule volumetry. Materials and methods Eighty-eight nodules in 24 patients with emphysema were studied retrospectively. All patients had undergone volumetric inspiratory and end-expiratory thoracic computed tomography (CT) for consideration of bronchoscopic lung volume reduction. Inspiratory and expiratory nodule volumes were measured using commercially available software. Local emphysema extent was established by analysing a segmentation area extended circumferentially around each nodule (quantified as percent of lung with density of –950 HU or less). Lung volumes were established using the same software. Differences in inspiratory and expiratory nodule volumes were illustrated using the Bland–Altman test. The influences of percentage reduction in lung volume at expiration, local emphysema extent, and nodule size on nodule volume variability were tested with multiple linear regression. Results The majority of nodules (59/88 [67%]) showed an increased volume at expiration. Mean difference in nodule volume between expiration and inspiration was +7.5% (95% confidence interval: –24.1, 39.1%). No relationships were demonstrated between nodule volume variability and emphysema extent, degree of expiration, or nodule size. Conclusion Expiration causes a modest increase in volumetry-derived nodule volumes; however, the effect is unpredictable. Local emphysema extent had no significant effect on volume variability in the present cohort.

The aim of this review is to give a comprehensive and concise overview of coronary embryology and normal coronary anatomy, describe common variants of normal and summarize typical patterns of anomalous coronary artery anatomy. Extensive iconography supports the text, with particular attention to images obtained in vivo using non-invasive imaging. We have divided this article into three groups, according to their frequency in the general population: Normal, normal variant and anomaly. Although congenital coronary artery anomalies are relatively uncommon, they are the second most common cause of sudden cardiac death among young athletes and therefore warrant detailed review. Based on the functional relevance of each abnormality, coronary artery anomalies can be classified as anomalies with obligatory ischemia, without ischemia or with exceptional ischemia. The clinical symptoms may include chest pain, dyspnea, palpitations, syncope, cardiomyopathy, arrhythmia, myocardial infarction and sudden cardiac death. Moreover, it is important to also identify variants and anomalies without clinical relevance in their own right as complications during surgery or angioplasty can occur.

BackgroundLung cancer kills more people than any other cancer in the UK (5-year survival ObjectivesTo (1) analyse LDCT lung cancer screening in a high-risk UK population, determine optimum recruitment, screening, reading and care pathway strategies; and (2) assess the psychological consequences and the health-economic implications of screening.DesignA pilot randomised controlled trial comparing intervention with usual care. A population-based risk questionnaire identified individuals who were at high risk of developing lung cancer (≥ 5% over 5 years).SettingThoracic centres with expertise in lung cancer imaging, respiratory medicine, pathology and surgery: Liverpool Heart & Chest Hospital, Merseyside, and Papworth Hospital, Cambridgeshire.ParticipantsIndividuals aged 50–75 years, at high risk of lung cancer, in the primary care trusts adjacent to the centres.InterventionsA thoracic LDCT scan. Follow-up computed tomography (CT) scans as per protocol. Referral to multidisciplinary team clinics was determined by nodule size criteria.Main outcome measuresPopulation-based recruitment based on risk stratification; management of the trial through web-based database; optimal characteristics of CT scan readers (radiologists vs. radiographers); characterisation of CT-detected nodules utilising volumetric analysis; prevalence of lung cancer at baseline; sociodemographic factors affecting participation; psychosocial measures (cancer distress, anxiety, depression, decision satisfaction); and cost-effectiveness modelling.ResultsA total of 247,354 individuals were approached to take part in the trial; 30.7% responded positively to the screening invitation. Recruitment of participants resulted in 2028 in the CT arm and 2027 in the control arm. A total of 1994 participants underwent CT scanning: 42 participants (2.1%) were diagnosed with lung cancer; 36 out of 42 (85.7%) of the screen-detected cancers were identified as stage 1 or 2, and 35 (83.3%) underwent surgical resection as their primary treatment. Lung cancer was more common in the lowest socioeconomic group. Short-term adverse psychosocial consequences were observed in participants who were randomised to the intervention arm and in those who had a major lung abnormality detected, but these differences were modest and temporary. Rollout of screening as a service or design of a full trial would need to address issues of outreach. The health-economic analysis suggests that the intervention could be cost-effective but this needs to be confirmed using data on actual lung cancer mortality.ConclusionsThe UK Lung Cancer Screening (UKLS) pilot was successfully undertaken with 4055 randomised individuals. The data from the UKLS provide evidence that adds to existing data to suggest that lung cancer screening in the UK could potentially be implemented in the 60–75 years age group, selected via the Liverpool Lung Project risk model version 2 and using CT volumetry-based management protocols.Future workThe UKLS data will be pooled with the NELSON (Nederlands Leuvens Longkanker Screenings Onderzoek: Dutch–Belgian Randomised Lung Cancer Screening Trial) and other European Union trials in 2017 which will provide European mortality and cost-effectiveness data. For now, there is a clear need for mortality results from other trials and further research to identify optimal methods of implementation and delivery. Strategies for increasing uptake and providing support for underserved groups will be key to implementation.Trial registrationCurrent Controlled Trials ISRCTN78513845.FundingThis project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full inHealth Technology Assessment; Vol. 20, No. 40. See the NIHR Journals Library website for further project information.