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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.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.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.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.

The optimal management of small but growing nodules remains unclear. The SUMMIT study nodule management algorithm uses a specific threshold volume of 200 mm3before 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 mm3at 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 mm3was 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 mm3minimum volume threshold for referral as effective at reducing unnecessary multidisciplinary team referrals for small growing nodules, while maintaining early-stage lung cancer diagnosis.

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.

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.

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.

BackgroundExplainability is a major limitation of current convolutional neural network (CNN) image classifiers. A CNN is required which supports its image-level prediction with a voxel-level segmentation.MethodsA weakly-supervised Unet architecture (WSUnet) is proposed to model voxel classes, by training with image-level supervision. WSUnet computes the image-level class prediction from the maximal voxel class prediction. Thus, voxel-level predictions provide a causally verifiable saliency map for the image-level decision.WSUnet is applied to explainable lung cancer detection in CT images. For comparison, current model explanation approaches are also applied to a standard CNN. Methods are compared using voxel-level discrimination metrics and a clinician preference survey.ResultsIn test data from two external institutions, WSUnet localised the tumour precisely at voxel-level (Precision: 0.93 [0.93-0.94]), achieving superior voxel-level discrimination to the best comparator (AUPR: 0.55 [0.54-0.55] vs. 0.36 [0.35-0.36]). Clinicians preferred WSUnet predictions in most test instances (Clinician Preference Rate: 0.72 [0.68-0.77]).ConclusionsWSUnet is a simple extension of the Unet, which facilitates voxel-level modelling from image-level labels. As WSUnet supports its image-level prediction with a causative voxel-level segmentation, it functions as a self-explaining image classifier.Graphical AbstractThe weakly-supervised Unet converts voxel-level predictions to image-level predictions using a global max-pooling layer. Thus, loss is computed at image-level. Following training with image-level labels, voxel-level predictions are extracted from the voxel-level output layer.FundingAuthors acknowledge funding support from the UK Research & Innovation London Medical Imaging and Artificial Intelligence Centre; Wellcome/Engineering and Physical Sciences Research Council Centre for Medical Engineering at King’s College London [WT 203148/Z/16/Z]; National Institute for Health Research Biomedical Research Centre at Guy’s & St Thomas’ Hospitals and King’s College London; National Institute for Health Research Biomedical Research Centre at Guy’s & St Thomas’ Hospitals and King’s College London; Cancer Research UK National Cancer Imaging Translational Accelerator [C1519/A28682]. For the purpose of open access, authors have applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.HIGHLIGHTSWSUnet is a weakly supervised Unet architecture which can learn semantic segmentation from data labelled only at image-level.WSUnet is a convolutional neural network image classifier which provides a causally verifiable voxel-level explanation to support its image-level prediction.In application to explainable lung cancer detection, WSUnet’s voxel-level output localises tumours precisely, outperforming current model explanation methods.WSUnet is a simple extension of the standard Unet architecture, requiring only the addition of a global max-pooling layer to the output.

The optimal management of small but growing nodules remains unclear. The SUMMIT study nodule management algorithm uses a specific threshold volume of 200 mm

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.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.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.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.

BackgroundCOPD 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.Methods16 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 (PLCOm2012score ≥1.3% and/or meeting USPSTF 2013 criteria).ResultsNearly 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%versus53.2%; pversus47.6%; pversus68.4%; pversus77.9%; pConclusionsTargeted 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.

In this study, seven soil and sedimentary humic acid samples were analyzed by synchronous scan fluorescence (SSF) spectroscopy. The spectra of these humic acids were compared to each other and characterized, based on three major SSF peaks centered at approximately 281, 367 and 470 nm. Intensity ratios were calculated based on these peaks that were used to numerically assist in source discrimination. All humic acid samples were then reacted with Ferrate(VI) and were again analyzed with SSF. Upon the addition of Ferrate(VI) SSF spectra were obtained which more readily differentiated humic acid source. This method will assist geochemists and water management districts in tracing sources of organic matter to receiving water bodies and may aid in the elucidation of the chemical nature of humic acids.