Enhancing cancer prediction in challenging screen-detected incident lung nodules using time-series deep learning.

Lung cancer screening (LCS) using annual computed tomography (CT) scanning significantly reduces mortality by detecting cancerous lung nodules at an earlier stage. Deep learning algorithms can improve nodule malignancy risk stratification. However, they have typically been used to analyse single time point CT data when detecting malignant nodules on either baseline or incident CT LCS rounds. Deep learning algorithms have the greatest value in two aspects. These approaches have great potential in assessing nodule change across time-series CT scans where subtle changes may be challenging to identify using the human eye alone. Moreover, they could be targeted to detect nodules developing on incident screening rounds, where cancers are generally smaller and more challenging to detect confidently. Here, we show the performance of our Deep learning-based Computer-Aided Diagnosis model integrating Nodule and Lung imaging data with clinical Metadata Longitudinally (DeepCAD-NLM-L) for malignancy prediction. DeepCAD-NLM-L showed improved performance (AUC = 88%) against models utilizing single time-point data alone. DeepCAD-NLM-L also demonstrated comparable and complementary performance to radiologists when interpreting the most challenging nodules typically found in LCS programs. It also demonstrated similar performance to radiologists when assessed on out-of-distribution imaging dataset. The results emphasize the advantages of using time-series and multimodal analyses when interpreting malignancy risk in LCS.

Predicting programmed death-ligand 1 (PD-L1) expression with fluorine-18 fluorodeoxyglucose ([18F]FDG) positron emission tomography/computed tomography (PET/CT) metabolic parameters in resectable non-small cell lung cancer.

BACKGROUND: Programmed death-ligand 1 (PD-L1) expression is a predictive biomarker for immunotherapy in non-small cell lung cancer (NSCLC). PD-L1 and glucose transporter 1 expression are closely associated, and studies demonstrate correlation of PD-L1 with glucose metabolism. AIM: The aim of this study was to investigate the association of fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography ([18F]FDG-PET/CT) metabolic parameters with PD-L1 expression in primary lung tumour and lymph node metastases in resected NSCLC. METHODS: We conducted a retrospective analysis of 210 patients with node-positive resectable stage IIB-IIIB NSCLC. PD-L1 tumour proportion score (TPS) was determined using the DAKO 22C3 immunohistochemical assay. Semi-automated techniques were used to analyse pre-operative [18F]FDG-PET/CT images to determine primary and nodal metabolic parameter scores (including max, mean, peak and peak adjusted for lean body mass standardised uptake values (SUV), metabolic tumour volume (MTV), total lesional glycolysis (TLG) and SUV heterogeneity index (HISUV)). RESULTS: Patients were predominantly male (57%), median age 70 years with non-squamous NSCLC (68%). A majority had negative primary tumour PD-L1 (TPS < 1%; 53%). Mean SUVmax, SUVmean, SUVpeak and SULpeak values were significantly higher (p < 0.05) in those with TPS ≥ 1% in primary tumour (n = 210) or lymph nodes (n = 91). However, ROC analysis demonstrated only moderate separability at the 1% PD-L1 TPS threshold (AUCs 0.58-0.73). There was no association of MTV, TLG and HISUV with PD-L1 TPS. CONCLUSION: This study demonstrated the association of SUV-based [18F]FDG-PET/CT metabolic parameters with PD-L1 expression in primary tumour or lymph node metastasis in resectable NSCLC, but with poor sensitivity and specificity for predicting PD-L1 positivity ≥ 1%. CLINICAL RELEVANCE STATEMENT: Whilst SUV-based fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography metabolic parameters may not predict programmed death-ligand 1 positivity ≥ 1% in the primary tumour and lymph nodes of resectable non-small cell lung cancer independently, there is a clear association which warrants further investigation in prospective studies. TRIAL REGISTRATION: Non-applicable KEY POINTS: • Programmed death-ligand 1 immunohistochemistry has a predictive role in non-small cell lung cancer immunotherapy; however, it is both heterogenous and dynamic. • SUV-based fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography ([18F]FDG-PET/CT) metabolic parameters were significantly higher in primary tumour or lymph node metastases with positive programmed death-ligand 1 expression. • These SUV-based parameters could potentially play an additive role along with other multi-modal biomarkers in selecting patients within a predictive nomogram.

Patient Reidentification from Chest Radiographs: An Interpretable Deep Metric Learning Approach and Its Applications.

Purpose: To train an explainable deep learning model for patient reidentification in chest radiograph datasets and assess changes in model-perceived patient identity as a marker for emerging radiologic abnormalities in longitudinal image sets. Materials and Methods: This retrospective study used a set of 1 094 537 frontal chest radiographs and free-text reports from 259 152 patients obtained from six hospitals between 2006 and 2019, with validation on the public ChestX-ray14, CheXpert, and MIMIC-CXR datasets. A deep learning model was trained for patient reidentification and assessed on patient identity confirmation, retrieval of patient images from a database based on a query image, and radiologic abnormality prediction in longitudinal image sets. The representation learned was incorporated into a generative adversarial network, allowing visual explanations of the relevant features. Performance was evaluated with sensitivity, specificity, F1 score, Precision at 1, R-Precision, and area under the receiver operating characteristic curve (AUC) for normal and abnormal prediction. Results: Patient reidentification was achieved with a mean F1 score of 0.996 ± 0.001 (2 SD) on the internal test set (26 152 patients) and F1 scores of 0.947-0.993 on the external test data. Database retrieval yielded a mean Precision at 1 score of 0.976 ± 0.005 at 299 × 299 resolution on the internal test set and Precision at 1 scores between 0.868 and 0.950 on the external datasets. Patient sex, age, weight, and other factors were identified as key model features. The model achieved an AUC of 0.73 ± 0.01 for abnormality prediction versus an AUC of 0.58 ± 0.01 for age prediction error. Conclusion: The image features used by a deep learning patient reidentification model for chest radiographs corresponded to intuitive human-interpretable characteristics, and changes in these identifying features over time may act as markers for an emerging abnormality.Keywords: Conventional Radiography, Thorax, Feature Detection, Supervised Learning, Convolutional Neural Network, Principal Component Analysis Supplemental material is available for this article. © RSNA, 2023See also the commentary by Raghu and Lu in this issue.

Weakly supervised segmentation models as explainable radiological classifiers for lung tumour detection on CT images.

PURPOSE: Interpretability is essential for reliable convolutional neural network (CNN) image classifiers in radiological applications. We describe a weakly supervised segmentation model that learns to delineate the target object, trained with only image-level labels ("image contains object" or "image does not contain object"), presenting a different approach towards explainable object detectors for radiological imaging tasks. METHODS: A weakly supervised Unet architecture (WSUnet) was trained to learn lung tumour segmentation from image-level labelled data. WSUnet generates voxel probability maps with a Unet and then constructs an image-level prediction by global max-pooling, thereby facilitating image-level training. WSUnet's voxel-level predictions were compared to traditional model interpretation techniques (class activation mapping, integrated gradients and occlusion sensitivity) in CT data from three institutions (training/validation: n = 412; testing: n = 142). Methods were compared using voxel-level discrimination metrics and clinical value was assessed with a clinician preference survey on data from external institutions. RESULTS: Despite the absence of voxel-level labels in training, WSUnet's voxel-level predictions localised tumours precisely in both validation (precision: 0.77, 95% CI: [0.76-0.80]; dice: 0.43, 95% CI: [0.39-0.46]), and external testing (precision: 0.78, 95% CI: [0.76-0.81]; dice: 0.33, 95% CI: [0.32-0.35]). WSUnet's voxel-level discrimination outperformed the best comparator in validation (area under precision recall curve (AUPR): 0.55, 95% CI: [0.49-0.56] vs. 0.23, 95% CI: [0.21-0.25]) and testing (AUPR: 0.40, 95% CI: [0.38-0.41] vs. 0.36, 95% CI: [0.34-0.37]). Clinicians preferred WSUnet predictions in most instances (clinician preference rate: 0.72 95% CI: [0.68-0.77]). CONCLUSION: Weakly supervised segmentation is a viable approach by which explainable object detection models may be developed for medical imaging. CRITICAL RELEVANCE STATEMENT: WSUnet learns to segment images at voxel level, training only with image-level labels. A Unet backbone first generates a voxel-level probability map and then extracts the maximum voxel prediction as the image-level prediction. Thus, training uses only image-level annotations, reducing human workload. WSUnet's voxel-level predictions provide a causally verifiable explanation for its image-level prediction, improving interpretability. KEY POINTS: • Explainability and interpretability are essential for reliable medical image classifiers. • This study applies weakly supervised segmentation to generate explainable image classifiers. • The weakly supervised Unet inherently explains its image-level predictions at voxel level.

Prevalence and clinical characteristics of non-malignant CT detected incidental findings in the SUMMIT lung cancer screening cohort.

BACKGROUND: Pulmonary and extrapulmonary incidental findings are frequently identified on CT scans performed for lung cancer screening. Uncertainty regarding their clinical significance and how and when such findings should be reported back to clinicians and participants persists. We examined the prevalence of non-malignant incidental findings within a lung cancer screening cohort and investigated the morbidity and relevant risk factors associated with incidental findings. We quantified the primary and secondary care referrals generated by our protocol. METHODS: The SUMMIT study (NCT03934866) is a prospective observational cohort study to examine the performance of delivering a low-dose CT (LDCT) screening service to a high-risk population. Spirometry, blood pressure, height/weight and respiratory history were assessed as part of a Lung Health Check. Individuals at high risk of lung cancer were offered an LDCT and returned for two further annual visits. This analysis is a prospective evaluation of the standardised reporting and management protocol for incidental findings developed for the study on the baseline LDCT. RESULTS: In 11 115 participants included in this analysis, the most common incidental findings were coronary artery calcification (64.2%) and emphysema (33.4%). From our protocolised management approach, the number of participants requiring review for clinically relevant findings in primary care was 1 in 20, and the number potentially requiring review in secondary care was 1 in 25. CONCLUSIONS: Incidental findings are common in lung cancer screening and can be associated with reported symptoms and comorbidities. A standardised reporting protocol allows systematic assessment and standardises onward management.

The SUMMIT Study: Utilising a written 'Next Steps' information booklet to prepare participants for potential lung cancer screening results and follow-up.

OBJECTIVES: Low-Dose Computed Tomography (LDCT) screening for lung cancer can result in several potential outcomes of varying significance. Communication methods used in Lung Cancer Screening (LCS) programmes must, therefore, ensure that participants are prepared for the range of possible results and follow-up. Here, we assess perceptions of a written preparatory information booklet provided to participants in a large LCS cohort designed to convey this information. MATERIALS AND METHODS: All participants in the SUMMIT Study (NCT03934866) were provided with a results preparation information booklet, entitled 'The SUMMIT Study: Next Steps' at their baseline appointment which outlined potential results, their significance, and timelines for follow up. Results from the LDCT scan and Lung Health Check were subsequently sent by letter. Perceptions of this booklet were assessed among participants with indeterminate pulmonary findings when they attended a face-to-face appointment immediately before their three-month interval scan. Specifically, questions assessed the perceived usefulness of the booklet and the amount of information contained in it. RESULTS: 70.1% (n = 1,412/2,014) participants remembered receiving the booklet at their appointment. Of these participants, 72.0% (n = 1,017/1,412) found it quite or very useful and 68.0% (n = 960/1,412) reported that it contained the right amount of information. Older participants, those from the least deprived socioeconomic quintile and those of Black ethnicity were less likely to report finding the booklet either quite or very useful, or that it contained the right amount of information. Participants who remembered receiving the booklet were more likely to be satisfied with the process of results communication by letter. CONCLUSION: Providing written information that prepares participants for possible LDCT results and their significance appears to be a useful resource and a helpful adjunct to a written method of results communication for large scale LCS programmes.

Uptake of invitations to a lung health check offering low-dose CT lung cancer screening among an ethnically and socioeconomically diverse population at risk of lung cancer in the UK (SUMMIT): a prospective, longitudinal cohort study.

BACKGROUND: Lung cancer screening with low-dose CT reduces lung cancer mortality, but screening requires equitable uptake from candidates at high risk of lung cancer across ethnic and socioeconomic groups that are under-represented in clinical studies. We aimed to assess the uptake of invitations to a lung health check offering low-dose CT lung cancer screening in an ethnically and socioeconomically diverse cohort at high risk of lung cancer. METHODS: In this multicentre, prospective, longitudinal cohort study (SUMMIT), individuals aged 55-77 years with a history of smoking in the past 20 years were identified via National Health Service England primary care records at practices in northeast and north-central London, UK, using electronic searches. Eligible individuals were invited by letter to a lung health check offering lung cancer screening at one of four hospital sites, with non-responders re-invited after 4 months. Individuals were excluded if they had dementia or metastatic cancer, were receiving palliative care or were housebound, or declined research participation. The proportion of individuals invited who responded to the lung health check invitation by telephone was used to measure uptake. We used univariable and multivariable logistic regression analyses to estimate associations between uptake of a lung health check invitation and re-invitation of non-responders, adjusted for sex, age, ethnicity, smoking, and deprivation score. This study was registered prospectively with ClinicalTrials.gov, NCT03934866. FINDINGS: Between March 20 and Dec 12, 2019, the records of 2 333 488 individuals from 251 primary care practices across northeast and north-central London were screened for eligibility; 1 974 919 (84·6%) individuals were outside the eligible age range, 7578 (2·1%) had pre-existing medical conditions, and 11 962 (3·3%) had opted out of particpation in research and thus were not invited. 95 297 individuals were eligible for invitation, of whom 29 545 (31·0%) responded. Due to the COVID-19 pandemic, re-invitation letters were sent to only a subsample of 4594 non-responders, of whom 642 (14·0%) responded. Overall, uptake was lower among men than among women (odds ratio [OR] 0·91 [95% CI 0·88-0·94]; p<0·0001), and higher among older age groups (1·48 [1·42-1·54] among those aged 65-69 years vs those aged 55-59 years; p<0·0001), groups with less deprivation (1·89 [1·76-2·04] for the most vs the least deprived areas; p<0·0001), individuals of Asian ethnicity (1·14 [1·09-1·20] vs White ethnicity; p<0·0001), and individuals who were former smokers (1·89 [1·83-1·95] vs current smokers; p<0·0001). When ethnicity was subdivided into 16 groups, uptake was lower among individuals of other White ethnicity than among those with White British ethnicity (0·86 [0·83-0·90]), whereas uptake was higher among Chinese, Indian, and other Asian ethnicities than among those with White British ethnicity (1·33 [1·13-1·56] for Chinese ethnicity; 1·29 [1·19-1·40] for Indian ethnicity; and 1·19 [1·08-1·31] for other Asian ethnicity). INTERPRETATION: Inviting eligible adults for lung health checks in areas of socioeconomic and ethnic diversity should achieve favourable participation in lung cancer screening overall, but inequalities by smoking, deprivation, and ethnicity persist. Reminder and re-invitation strategies should be used to increase uptake and the equity of response. FUNDING: GRAIL.

Growing small solid nodules in lung cancer screening: safety and efficacy of a 200 mm3 minimum size threshold for multidisciplinary team referral.

The optimal management of small but growing nodules remains unclear. The SUMMIT study nodule management algorithm uses a specific threshold volume of 200 mm3 before referral of growing solid nodules to the multidisciplinary team for further investigation is advised, with growing nodules below this threshold kept under observation within the screening programme. Malignancy risk of growing solid nodules of size >200 mm3 at initial 3-month interval scan was 58.3% at a per-nodule level, compared with 13.3% in growing nodules of size ≤200 mm3 (relative risk 4.4, 95% CI 2.17 to 8.83). The positive predictive value of a combination of nodule growth (defined as percentage volume change of ≥25%), and size >200 mm3 was 65.9% (29/44) at a cancer-per-nodule basis, or 60.5% (23/38) on a cancer-per-participant basis. False negative rate of the protocol was 1.9% (95% CI 0.33% to 9.94%). These findings support the use of a 200 mm3 minimum volume threshold for referral as effective at reducing unnecessary multidisciplinary team referrals for small growing nodules, while maintaining early-stage lung cancer diagnosis.

Reporting and management of incidental lung findings on computed tomography: beyond lung nodules.

Non-nodular incidental lung findings can broadly be categorised as airway- or airspace-related abnormalities and diffuse parenchymal abnormalities. Airway-related abnormalities include bronchial dilatation and thickening, foci of low attenuation, emphysema, and congenital variants. Diffuse parenchymal abnormalities relate to the spectrum of diffuse parenchymal lung diseases cover a spectrum from interstitial lung abnormalities (ILAs) and pulmonary cysts to established diffuse parenchymal lung abnormalities such as the idiopathic interstitial pneumonias and cystic lung diseases. In this review, we discuss the main manifestations of these incidental findings, paying attention to their prevalence and importance, descriptors to use when reporting, the limits of what can be considered "normal", and conclude each section with some pragmatic reporting recommendations. We also highlight technical and patient factors which can lead to spurious abnormalities.

Utilisation of primary care electronic patient records for identification and targeted invitation of individuals to a lung cancer screening programme.

Lung cancer screening (LCS) eligibility is largely determined by tobacco consumption. Primary care smoking data could guide LCS invitation and eligibility assessment. We present observational data from the SUMMIT Study, where individual self-reported smoking status was concordant with primary care records in 75.3%. However, 10.3% demonstrated inconsistencies between historic and most recent smoking status documentation. Quantified tobacco consumption was frequently missing, precluding direct LCS eligibility assessment. Primary care recorded "ever-smoker" status, encompassing both recent and historic documentation, can be used to target LCS invitation. Identifying those with missing or erroneous "never-smoker" smoking status is crucial for equitable invitation to LCS.

Detection of COPD in the SUMMIT Study lung cancer screening cohort using symptoms and spirometry.

BACKGROUND: COPD is a major comorbidity in lung cancer screening (LCS) cohorts, with a high prevalence of undiagnosed COPD. Combining symptom assessment with spirometry in this setting may enable earlier diagnosis of clinically significant COPD and facilitate increased understanding of lung cancer risk in COPD. In this study, we wished to understand the prevalence, severity, clinical phenotype and lung cancer risk of individuals with symptomatic undiagnosed COPD in a LCS cohort. METHODS: 16 010 current or former smokers aged 55-77 years attended a lung health check as part of the SUMMIT Study. A respiratory consultation and spirometry were performed alongside LCS eligibility assessment. Those with symptoms, no previous COPD diagnosis and airflow obstruction were labelled as undiagnosed COPD. Baseline low-dose computed tomography (LDCT) was performed in those at high risk of lung cancer (PLCOm2012 score ≥1.3% and/or meeting USPSTF 2013 criteria). RESULTS: Nearly one in five (19.7%) met criteria for undiagnosed COPD. Compared with those previously diagnosed, those undiagnosed were more likely to be male (59.1% versus 53.2%; p<0.001), currently smoking (54.9% versus 47.6%; p<0.001) and from an ethnic minority group (p<0.001). Undiagnosed COPD was associated with less forced expiratory volume in 1 s impairment (Global Initiative for Chronic Obstructive Lung Disease (GOLD) grades 1 and 2: 85.3% versus 68.4%; p<0.001) and lower symptom/exacerbation burden (GOLD A and B groups: 95.6% versus 77.9%; p<0.001) than those with known COPD. Multivariate analysis demonstrated that airflow obstruction was an independent risk factor for lung cancer risk on baseline LDCT (adjusted OR 2.74, 95% CI 1.73-4.34; p<0.001), with a high risk seen in those with undiagnosed COPD (adjusted OR 2.79, 95% CI 1.67-4.64; p<0.001). CONCLUSIONS: Targeted case-finding within LCS detects high rates of undiagnosed symptomatic COPD in those most at risk. Individuals with undiagnosed COPD are at high risk for lung cancer.

Telephone risk-based eligibility assessment for low-dose CT lung cancer screening.

Eligibility for lung cancer screening (LCS) requires assessment of lung cancer risk, based on smoking history alongside demographic and medical factors. Reliance on individual face-to-face eligibility assessment risks inefficiency and costliness. The SUMMIT Study introduced a telephone-based lung cancer risk assessment to guide invitation to face-to-face LCS eligibility assessment, which significantly increased the proportion of face-to-face attendees eligible for LCS. However, levels of agreement between phone screener and in-person responses were lower in younger individuals and minority ethnic groups. Telephone-based risk assessment is an efficient way to optimise selection for LCS appointments but requires further iteration to ensure an equitable approach.

The role of computer-assisted radiographer reporting in lung cancer screening programmes.

OBJECTIVES: Successful lung cancer screening delivery requires sensitive, timely reporting of low-dose computed tomography (LDCT) scans, placing a demand on radiology resources. Trained non-radiologist readers and computer-assisted detection (CADe) software may offer strategies to optimise the use of radiology resources without loss of sensitivity. This report examines the accuracy of trained reporting radiographers using CADe support to report LDCT scans performed as part of the Lung Screen Uptake Trial (LSUT). METHODS: In this observational cohort study, two radiographers independently read all LDCT performed within LSUT and reported on the presence of clinically significant nodules and common incidental findings (IFs), including recommendations for management. Reports were compared against a 'reference standard' (RS) derived from nodules identified by study radiologists without CADe, plus consensus radiologist review of any additional nodules identified by the radiographers. RESULTS: A total of 716 scans were included, 158 of which had one or more clinically significant pulmonary nodules as per our RS. Radiographer sensitivity against the RS was 68-73.7%, with specificity of 92.1-92.7%. Sensitivity for detection of proven cancers diagnosed from the baseline scan was 83.3-100%. The spectrum of IFs exceeded what could reasonably be covered in radiographer training. CONCLUSION: Our findings highlight the complexity of LDCT reporting requirements, including the limitations of CADe and the breadth of IFs. We are unable to recommend CADe-supported radiographers as a sole reader of LDCT scans, but propose potential avenues for further research including initial triage of abnormal LDCT or reporting of follow-up surveillance scans. KEY POINTS: • Successful roll-out of mass screening programmes for lung cancer depends on timely, accurate CT scan reporting, placing a demand on existing radiology resources. • This observational cohort study examines the accuracy of trained radiographers using computer-assisted detection (CADe) software to report lung cancer screening CT scans, as a potential means of supporting reporting workflows in LCS programmes. • CADe-supported radiographers were less sensitive than radiologists at identifying clinically significant pulmonary nodules, but had a low false-positive rate and good sensitivity for detection of confirmed cancers.

The reporting of pulmonary nodule results by letter in a lung cancer screening setting.

OBJECTIVES: Pulmonary nodules are commonly found in Lung Cancer Screening (LCS), with results typically communicated by face-to-face or telephone consultation. Providing LCS on a population basis requires resource efficient and scalabe communication methods. Written communication provides one such method. Here, we assess participant satisfaction with this approach in a LCS setting and investigate characteristics associated with dissatisfaction. MATERIALS AND METHODS: The SUMMIT Study is a prospective observational cohort study which aims to assess the implementation of Low-Dose Computed Tomography (LDCT) scanning for LCS in a high-risk population and validate a multi-cancer early detection blood test (NCT03934866). Participants with indeterminate pulmonary nodules requiring a three-month interval LDCT were informed of their result by postal letter and given a face-to-face appointment with a study practitioner at their interval LDCT appointment. At this appointment, having previously received their results letter, participants were verbally asked questions to assess their satisfaction with, and preferences for, methods of results communication. RESULTS: 1,900 participants were included in the analysis. 82.8% (n = 1573) were satisfied with receiving their results by letter, with 2.9% (n = 55) reporting dissatisfaction. 86.3% (n = 1640) stated it was their preferred communication method and 77.3% (n = 1469) reported that their letter contained the right amount of information. Participants from less deprived socioeconomic quintiles were more likely to report that the letter contained insufficient information and individuals aged ≥ 70 years were less likely to do so. Although 13.7% (n = 261) participants had discussed their results with their General Practitioner (GP) prior to the study visit, 83.9% (n = 219) of these participants were satisfied with receiving results by letter, with the same proportion preferring this communication method. CONCLUSION: We report high participant satisfaction with the reporting of pulmonary nodule results by letter in a LCS setting. We believe this provides a feasible route forward for large-scale screening programmes.

Lung Screen Uptake Trial: results from a single lung cancer screening round.

The Lung Screen Uptake Trial tested a novel invitation strategy to improve uptake and reduce socioeconomic and smoking-related inequalities in lung cancer screening (LCS) participation. It provides one of the first UK-based 'real-world' LCS cohorts. Of 2012 invited, 1058 (52.6%) attended a 'lung health check'. 768/996 (77.1%) in the present analysis underwent a low-dose CT scan. 92 (11.9%) and 33 (4.3%) participants had indeterminate pulmonary nodules requiring 3-month and 12-month surveillance, respectively; 36 lung cancers (4.7%) were diagnosed (median follow-up: 1044 days). 72.2% of lung cancers were stage I/II and 79.4% of non-small cell lung cancer had curative-intent treatment.

Prevalence, Symptom Burden, and Underdiagnosis of Chronic Obstructive Pulmonary Disease in a Lung Cancer Screening Cohort.

Rationale: Individuals eligible for lung cancer screening (LCS) by low-dose computed tomography (LDCT) are also at risk of chronic obstructive pulmonary disease (COPD) due to age and smoking exposure. Whether the LCS episode is useful for early detection of COPD is not well established.Objectives: To explore associations between symptoms, comorbidities, spirometry, and emphysema in participants enrolled in the Lung Screen Uptake Trial.Methods: This cross-sectional study was a prespecified analysis nested within Lung Screen Uptake Trial, which was a randomized study testing the impact of differing invitation materials on attendance of 60- to 75-year-old smokers and ex-smokers to a "lung health check" between November 2015 and July 2017. Participants with a smoking history ≥30 pack-years and who quit ≤15 years ago, or meeting a lung cancer risk of ≥1.51% via the Prostate Lung Colorectal Ovarian model or ≥2.5% via the Liverpool Lung Project model, were offered LDCT. COPD was defined and classified according to the GOLD (Global Initiative for Obstructive Lung Disease) criteria using prebronchodilator spirometry. Analyses included the use of descriptive statistics, chi-square tests to examine group differences, and univariable and multivariable logistic regression to explore associations between symptom prevalence, airflow limitation, and visually graded emphysema.Results: A total of 560 of 986 individuals included in the analysis (57%) had prebronchodilator spirometry consistent with COPD; 67% did not have a prior history of COPD and were termed "undiagnosed." Emphysema prevalence in those with known and "undiagnosed" COPD was 73% and 68%, respectively. A total of 32% of those with "undiagnosed COPD" had no emphysema on LDCT. Inhaler use and symptoms were more common in the "known" than the "undiagnosed" COPD group (63% vs. 33% with persistent cough [P < 0.001]; 73% vs. 33% with dyspnea [P < 0.001]). Comorbidities were common in all groups. Adjusted odds ratio (aOR) of respiratory symptoms were more significant for airflow obstruction (aOR GOLD 1 and 2, 1.57; confidence interval [CI], 1.14-2.17; aOR GOLD 3 and 4, 4.6; CI, 2.17-9.77) than emphysema (aOR mild, 1.12; CI, 0.81-1.55; aOR moderate, 1.33; CI, 0.85-2.09; aOR severe, 4.00; CI, 1.57-10.2).Conclusions: There is high burden of "undiagnosed COPD" and emphysema in LCS participants. Adding spirometry findings to the LDCT enhances identification of individuals with COPD.Clinical trial registered with www.clinicaltrials.gov (NCT02558101).

Evaluation of cardiovascular risk in a lung cancer screening cohort.

INTRODUCTION: Lung cancer screening (LCS) by low-dose computed tomography (LDCT) offers an opportunity to impact both lung cancer and coronary heart disease mortality through detection of coronary artery calcification (CAC). Here, we explore the value of CAC and cardiovascular disease (CVD) risk assessment in LCS participants in the Lung Screen Uptake Trial (LSUT). METHODS: In this cross-sectional study, current and ex-smokers aged 60-75 were invited to a 'lung health check'. Data collection included a CVD risk assessment enabling estimation of 10 year CVD risk using the QRISK2 score. Participants meeting the required lung cancer risk underwent an ungated, non-contrast LDCT. Descriptive data, bivariate associations and a multivariate analysis of predictors of statin use are presented. RESULTS: Of 1005 individuals enrolled, 680 were included in the final analysis. 421 (61.9%) had CAC present and in 49 (7.2%), this was heavy. 668 (98%) of participants had a QRISK2≥10% and QRISK2 was positively associated with increasing CAC grade (OR 4.29 (CI 0.93 to 19.88) for QRISK2=10%-20% and 12.29 (CI 2.68 to 56.1) for QRISK2≥20% respectively). Of those who qualified for statin primary prevention (QRISK2≥10%), 56.8% did not report a history of statin use. In the multivariate analysis statin use was associated with age, body mass index and history of hypertension and diabetes. CONCLUSIONS: LCS offers an important opportunity for instituting CVD risk assessment in all LCS participants irrespective of the presence of LDCT-detected CAC. Further studies are needed to determine whether CAC could enhance uptake and adherence to primary preventative strategies.