Assessment of Advanced Diagnostic Bronchoscopy Outcomes for Peripheral Lung Lesions: A Delphi Consensus Definition of Diagnostic Yield and Recommendations for Patient-centered Study Designs. An Official American Thoracic Society/American College of Chest Physicians Research Statement.

Background: Advanced diagnostic bronchoscopy targeting the lung periphery has developed at an accelerated pace over the last two decades, whereas evidence to support introduction of innovative technologies has been variable and deficient. A major gap relates to variable reporting of diagnostic yield, in addition to limited comparative studies. Objectives: To develop a research framework to standardize the evaluation of advanced diagnostic bronchoscopy techniques for peripheral lung lesions. Specifically, we aimed for consensus on a robust definition of diagnostic yield, and we propose potential study designs at various stages of technology development. Methods: Panel members were selected for their diverse expertise. Workgroup meetings were conducted in virtual or hybrid format. The cochairs subsequently developed summary statements, with voting proceeding according to a modified Delphi process. The statement was cosponsored by the American Thoracic Society and the American College of Chest Physicians. Results: Consensus was reached on 15 statements on the definition of diagnostic outcomes and study designs. A strict definition of diagnostic yield should be used, and studies should be reported according to the STARD (Standards for Reporting Diagnostic Accuracy Studies) guidelines. Clinical or radiographic follow-up may be incorporated into the reference standard definition but should not be used to calculate diagnostic yield from the procedural encounter. Methodologically robust comparative studies, with incorporation of patient-reported outcomes, are needed to adequately assess and validate minimally invasive diagnostic technologies targeting the lung periphery. Conclusions: This American Thoracic Society/American College of Chest Physicians statement aims to provide a research framework that allows greater standardization of device validation efforts through clearly defined diagnostic outcomes and robust study designs. High-quality studies, both industry and publicly funded, can support subsequent health economic analyses and guide implementation decisions in various healthcare settings.

Factors influencing clinician and patient interaction with machine learning-based risk prediction models: a systematic review.

Machine learning (ML)-based risk prediction models hold the potential to support the health-care setting in several ways; however, use of such models is scarce. We aimed to review health-care professional (HCP) and patient perceptions of ML risk prediction models in published literature, to inform future risk prediction model development. Following database and citation searches, we identified 41 articles suitable for inclusion. Article quality varied with qualitative studies performing strongest. Overall, perceptions of ML risk prediction models were positive. HCPs and patients considered that models have the potential to add benefit in the health-care setting. However, reservations remain; for example, concerns regarding data quality for model development and fears of unintended consequences following ML model use. We identified that public views regarding these models might be more negative than HCPs and that concerns (eg, extra demands on workload) were not always borne out in practice. Conclusions are tempered by the low number of patient and public studies, the absence of participant ethnic diversity, and variation in article quality. We identified gaps in knowledge (particularly views from under-represented groups) and optimum methods for model explanation and alerts, which require future research.

Impact of the SARS-CoV-2 pandemic on lung cancer survival in England: an analysis of the rapid cancer registration dataset.

Early changes in lung cancer care can affect survival. Given the decrease in diagnosis during lockdowns, we calculated their impact on survival using National Lung Cancer Audit data. Percentage survival and HRs for death were compared between 2019 and lockdown periods of 2020. Decreased survival was observed from the first national lockdown onwards and within 90 days of diagnosis. HRs were highest for people diagnosed at the end of 2020 at 1.26 (95% CI 1.20 to 1.32) for death within 90 days and 1.51 (95% CI 1.42 to 1.60) for death between 91 and 270 days. Further work is needed on measures to mitigate this impact.

Assessing eligibility for lung cancer screening using parsimonious ensemble machine learning models: A development and validation study.

BACKGROUND: Risk-based screening for lung cancer is currently being considered in several countries; however, the optimal approach to determine eligibility remains unclear. Ensemble machine learning could support the development of highly parsimonious prediction models that maintain the performance of more complex models while maximising simplicity and generalisability, supporting the widespread adoption of personalised screening. In this work, we aimed to develop and validate ensemble machine learning models to determine eligibility for risk-based lung cancer screening. METHODS AND FINDINGS: For model development, we used data from 216,714 ever-smokers recruited between 2006 and 2010 to the UK Biobank prospective cohort and 26,616 high-risk ever-smokers recruited between 2002 and 2004 to the control arm of the US National Lung Screening (NLST) randomised controlled trial. The NLST trial randomised high-risk smokers from 33 US centres with at least a 30 pack-year smoking history and fewer than 15 quit-years to annual CT or chest radiography screening for lung cancer. We externally validated our models among 49,593 participants in the chest radiography arm and all 80,659 ever-smoking participants in the US Prostate, Lung, Colorectal and Ovarian (PLCO) Screening Trial. The PLCO trial, recruiting from 1993 to 2001, analysed the impact of chest radiography or no chest radiography for lung cancer screening. We primarily validated in the PLCO chest radiography arm such that we could benchmark against comparator models developed within the PLCO control arm. Models were developed to predict the risk of 2 outcomes within 5 years from baseline: diagnosis of lung cancer and death from lung cancer. We assessed model discrimination (area under the receiver operating curve, AUC), calibration (calibration curves and expected/observed ratio), overall performance (Brier scores), and net benefit with decision curve analysis. Models predicting lung cancer death (UCL-D) and incidence (UCL-I) using 3 variables-age, smoking duration, and pack-years-achieved or exceeded parity in discrimination, overall performance, and net benefit with comparators currently in use, despite requiring only one-quarter of the predictors. In external validation in the PLCO trial, UCL-D had an AUC of 0.803 (95% CI: 0.783, 0.824) and was well calibrated with an expected/observed (E/O) ratio of 1.05 (95% CI: 0.95, 1.19). UCL-I had an AUC of 0.787 (95% CI: 0.771, 0.802), an E/O ratio of 1.0 (95% CI: 0.92, 1.07). The sensitivity of UCL-D was 85.5% and UCL-I was 83.9%, at 5-year risk thresholds of 0.68% and 1.17%, respectively, 7.9% and 6.2% higher than the USPSTF-2021 criteria at the same specificity. The main limitation of this study is that the models have not been validated outside of UK and US cohorts. CONCLUSIONS: We present parsimonious ensemble machine learning models to predict the risk of lung cancer in ever-smokers, demonstrating a novel approach that could simplify the implementation of risk-based lung cancer screening in multiple settings.

PET/CT in treatment response assessment in lung cancer. When should it be recommended?

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer. Different treatment options are now possible both for surgical candidates and for those NSCLC patients deemed not suitable for surgery. Despite the treatments available, only a limited number of less advanced stages are potentially curable, with many patients suffering local recurrence or distant metastases. FDG-PET/CT is commonly used in many centers for post-treatment evaluation, follow-up, or surveillance; Nonetheless, there is no clear consensus regarding the indications in these cases. Based upon the results of a literature review and local expertise from a large lung cancer unit, we built clinical evidence-based recommendations for the use of FDG-PET/CT in response assessment. We found that in general this is not recommended earlier than 3 months from treatment; however, as described in detail the correct timing will also depend upon the type of treatment used. We also present a structured approach to assessing treatment changes when reporting FDG-PET/CT, using visual or quantitative approaches.

The evolution of non-small cell lung cancer metastases in TRACERx.

Metastatic disease is responsible for the majority of cancer-related deaths1. We report the longitudinal evolutionary analysis of 126 non-small cell lung cancer (NSCLC) tumours from 421 prospectively recruited patients in TRACERx who developed metastatic disease, compared with a control cohort of 144 non-metastatic tumours. In 25% of cases, metastases diverged early, before the last clonal sweep in the primary tumour, and early divergence was enriched for patients who were smokers at the time of initial diagnosis. Simulations suggested that early metastatic divergence more frequently occurred at smaller tumour diameters (less than 8 mm). Single-region primary tumour sampling resulted in 83% of late divergence cases being misclassified as early, highlighting the importance of extensive primary tumour sampling. Polyclonal dissemination, which was associated with extrathoracic disease recurrence, was found in 32% of cases. Primary lymph node disease contributed to metastatic relapse in less than 20% of cases, representing a hallmark of metastatic potential rather than a route to subsequent recurrences/disease progression. Metastasis-seeding subclones exhibited subclonal expansions within primary tumours, probably reflecting positive selection. Our findings highlight the importance of selection in metastatic clone evolution within untreated primary tumours, the distinction between monoclonal versus polyclonal seeding in dictating site of recurrence, the limitations of current radiological screening approaches for early diverging tumours and the need to develop strategies to target metastasis-seeding subclones before relapse.

What is the optimal management of potentially resectable stage III-N2 NSCLC? Results of a fixed-effects network meta-analysis and economic modelling.

Introduction: There is a critical need to understand the optimal treatment regimen in patients with potentially resectable stage III-N2 nonsmall cell lung cancer (NSCLC). Methods: A systematic review of randomised controlled trials was carried out using a literature search including the CDSR, CENTRAL, DARE, HTA, EMBASE and MEDLINE bibliographic databases. Selected trials were used to perform a Bayesian fixed-effects network meta-analysis and economic modelling of treatment regimens relevant to current-day treatment options: chemotherapy plus surgery (CS), chemotherapy plus radiotherapy (CR) and chemoradiotherapy followed by surgery (CRS). Findings: Six trials were prioritised for evidence synthesis. The fixed-effects network meta-analyses demonstrated an improvement in disease-free survival (DFS) for CRS versus CS and CRS versus CR of 0.34 years (95% CI 0.02-0.65) and 0.32 years (95% CI 0.05-0.58) respectively, over a 5-year period. No evidence of effect was observed in overall survival although point estimates favoured CRS. The probabilities that CRS had a greater mean survival time and greater probability of being alive than the reference treatment of CR at 5 years were 89% and 86% respectively. Survival outcomes for CR and CS were essentially equivalent. The economic model calculated that CRS and CS had incremental cost-effectiveness ratios of £19 000/quality-adjusted life-year (QALY) and £78 000/QALY compared to CR. The probability that CRS generated more QALYs than CR and CS was 94%. Interpretation: CRS provides an extended time in a disease-free state leading to improved cost-effectiveness over CR and CS in potentially resectable stage III-N2 NSCLC.

Brain imaging in lung cancer staging: A real-world, multi-centre study of prevalence of brain metastases, impact on treatment and re-modelling of the NICE health economic analysis.

INTRODUCTION: In 2019, the National Institute for Health and Care Excellence (NICE) updated their recommendations with respect to brain imaging in the staging of non-small cell lung cancer (NSCLC) based on an analytic cost-effectiveness model using published data and modelling assumptions from committee experts. In this study, we aimed to re-run this model using real-world multi-centre UK data. MATERIALS AND METHODS: Retrospective data was collected on consecutive patients with radically treatable clinical stage II and III lung cancer from eleven acute NHS Trusts during the calendar year 01/01/2018 to 31/12/2018. Following a written application to the NICE lung cancer guideline committee, we were granted access to the NG122 brain imaging economic model for the purpose of updating the input parameters in line with the real-world findings from this study. RESULTS: A total of 444 patients had data for analysis. The combined prevalence of occult brain metastases was 6.2% (10/165) in stage II and 6% (17/283) in stage III, compared to 9.5% and 9.3% used in the NICE economic model. 30% of patients with clinical stage III NSCLC and occult BMs on pre-treatment imaging went onto complete the planned curative intent treatment of extracranial disease, 60% completed SRS to the brain and 30% completed WBRT. This compares to 0%, 10% and 0% in the NICE assumptions. The health economic analysis concluded that brain imaging was no longer cost-effective in stage II disease (ICERs £50,023-£115,785) whilst brain imaging remained cost-effective for stage III patients (ICERs 17,000-£22,173), with MRI being the most cost-effective strategy. CONCLUSION: This re-running of the NICE health economic model with real-world data strongly supports the NICE guideline recommendation for brain imaging prior to curative-intent treatment in stage III lung cancer but questions the cost-effectiveness of CT brain imaging prior to curative-intent treatment in stage II lung cancer.

The Impact of COVID-19 on Lung Cancer Incidence in England: Analysis of the National Lung Cancer Audit 2019 and 2020 Rapid Cancer Registration Datasets.

BACKGROUND: The COVID-19 pandemic has caused significant disruption to health-care services and delivery worldwide. The impact of the pandemic and associated national lockdowns on lung cancer incidence in England have yet to be assessed. RESEARCH QUESTION: What was the impact of the first year of the COVID-19 pandemic on the incidence and presentation of lung cancer in England? STUDY DESIGN AND METHODS: In this retrospective observational study, incidence rates for lung cancer were calculated from The National Lung Cancer Audit Rapid Cancer Registration Datasets for 2019 and 2020, using midyear population estimates from the Office of National Statistics as the denominators. Rates were compared using Poisson regression according to time points related to national lockdowns in 2020. RESULTS: Sixty-four thousand four hundred fifty-seven patients received a diagnosis of lung cancer across 2019 (n = 33,088) and 2020 (n = 31,369). During the first national lockdown, a 26% reduction in lung cancer incidence was observed compared with the equivalent calendar period of 2019 (adjusted incidence rate ratio [IRR], 0.74; 95% CI, 0.71-0.78). This included a 23% reduction in non-small cell lung cancer (adjusted IRR, 0.77; 95% CI, 0.74-0.81) and a 45% reduction in small cell lung cancer (adjusted IRR, 0.55; 95% CI, 0.46-0.65) incidence. Thereafter, incidence rates almost recovered to baseline, without overcompensation (adjusted IRR, 0.96; 95% CI, 0.94-0.98). INTERPRETATION: The incidence rates of lung cancer in England fell significantly by 26% during the first national lockdown in 2020 and did not compensate later in the year.

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.

Usage of epidermal growth factor mutation testing and impact on treatment patterns in non-small cell lung cancer: An international observational study.

OBJECTIVES: Epidermal growth factor receptor (EGFR) mutations (EGFRm) are common oncogene drivers in non-small cell lung cancer (NSCLC). This real-world study explored treatment patterns and time to receive EGFRm test results in patients with advanced EGFRm NSCLC. METHODS: A cross-sectional medical chart review was completed May-August 2020 in Australia, Canada, Germany, Italy, South Korea, Taiwan, UK, and USA. Eligible patients had advanced NSCLC and a positive EGFRm test result January-December 2017. Data were abstracted from NSCLC diagnosis to end of follow-up (31 March 2020) or patient's death whichever occurred earlier. The index date was the date of EGFRm confirmation. RESULTS: 223 physicians provided data for 1,793 patients. Patients' mean age was 64.7 years, 54 % were male, 30.7 % had no history of smoking. Overall, 78 % of EGFRm test results were received ≤ 2 weeks after request (range of median 7-14 days across countries). Median time from advanced NSCLC diagnosis to EGFRm test result was 18 days (median range 10-22 days across countries). Over a third (37 %) of patients received a systemic treatment prior to EGFRm result; chemotherapy (25 %) and EGFR-TKI (15 %) were most commonly prescribed; post-EGFR test-result was EGFR-TKI (68 %); 80 % of patients initiated EGFR-TKI at any time point post-NSCLC diagnosis. Of those receiving a first-line EGFR-TKI post-EGFRm testing, 84 % received a TKI alone, 12 % in combination with chemotherapy, and 3 % with other treatments. Median time from first-line EGFR-TKI initiation post-EGFRm testing to first subsequent treatment was 19.8 months. CONCLUSION: Over one-fifth of patients wait >14 days for their EGFRm test results, affecting their likelihood of receiving first-line EGFR-TKI with 20 % of patients never receiving EGFR TKI treatment. There was significant inter-country variability in the proportion of patients receiving EGFR TKIs. Our study highlights the need to improve EGFRm testing turnaround times and treatment initiation across countries.

A radiomics-based decision support tool improves lung cancer diagnosis in combination with the Herder score in large lung nodules.

BACKGROUND: Large lung nodules (≥15 mm) have the highest risk of malignancy, and may exhibit important differences in phenotypic or clinical characteristics to their smaller counterparts. Existing risk models do not stratify large nodules well. We aimed to develop and validate an integrated segmentation and classification pipeline, incorporating deep-learning and traditional radiomics, to classify large lung nodules according to cancer risk. METHODS: 502 patients from five U.K. centres were recruited to the large-nodule arm of the retrospective LIBRA study between July 2020 and April 2022. 838 CT scans were used for model development, split into training and test sets (70% and 30% respectively). An nnUNet model was trained to automate lung nodule segmentation. A radiomics signature was developed to classify nodules according to malignancy risk. Performance of the radiomics model, termed the large-nodule radiomics predictive vector (LN-RPV), was compared to three radiologists and the Brock and Herder scores. FINDINGS: 499 patients had technically evaluable scans (mean age 69 ± 11, 257 men, 242 women). In the test set of 252 scans, the nnUNet achieved a DICE score of 0.86, and the LN-RPV achieved an AUC of 0.83 (95% CI 0.77-0.88) for malignancy classification. Performance was higher than the median radiologist (AUC 0.75 [95% CI 0.70-0.81], DeLong p = 0.03). LN-RPV was robust to auto-segmentation (ICC 0.94). For baseline solid nodules in the test set (117 patients), LN-RPV had an AUC of 0.87 (95% CI 0.80-0.93) compared to 0.67 (95% CI 0.55-0.76, DeLong p = 0.002) for the Brock score and 0.83 (95% CI 0.75-0.90, DeLong p = 0.4) for the Herder score. In the international external test set (n = 151), LN-RPV maintained an AUC of 0.75 (95% CI 0.63-0.85). 18 out of 22 (82%) malignant nodules in the Herder 10-70% category in the test set were identified as high risk by the decision-support tool, and may have been referred for earlier intervention. INTERPRETATION: The model accurately segments and classifies large lung nodules, and may improve upon existing clinical models. FUNDING: This project represents independent research funded by: 1) Royal Marsden Partners Cancer Alliance, 2) the Royal Marsden Cancer Charity, 3) the National Institute for Health Research (NIHR) Biomedical Research Centre at the Royal Marsden NHS Foundation Trust and The Institute of Cancer Research, London, 4) the National Institute for Health Research (NIHR) Biomedical Research Centre at Imperial College London, 5) Cancer Research UK (C309/A31316).

Optimising tissue acquisition and the molecular testing pathway for patients with non-small cell lung cancer: A UK expert consensus statement.

Targeted therapy against actionable variants has revolutionised the treatment landscape for non-small cell lung cancer (NSCLC). Approximately half of NSCLC adenocarcinomas have an actionable variant, making molecular testing a critical component of the diagnostic process to personalise therapeutic options, optimise clinical outcomes and minimise toxicity. Recently, genomic testing in England has undergone major changes with the introduction of Genomic Laboratory Hubs, designed to consolidate and enhance existing laboratory provision and deliver genomic testing as outlined in the National Genomic Test Directory. Similar changes are ongoing in Scotland, Wales and Northern Ireland. However, multiple challenges exist with current tissue acquisition procedures and the molecular testing pathway in the UK, including quantity and quality of available tissue, adequacy rates, test availability among genomic laboratories, turnaround times, multidisciplinary team communication, and limited guidance and standardisation. The COVID-19 pandemic has added an extra layer of complexity. Herein, we summarise best practice recommendations, based on expert opinion, to overcome existing challenges in the UK. The least invasive biopsy technique should be undertaken with the aim of acquiring the greatest quality and quantity of tissue. Use of sedation should be considered to improve patient experience. Rapid on-site evaluation may also be useful to help guide adequate sampling, and liquid biopsy may be beneficial in some instances. Sample processing should be appropriate to facilitate biomarker testing, in particular, next-generation sequencing for comprehensive genomic information. Steps to optimise tissue utilisation and turnaround times, such as planning of tissue usage, limiting immunohistochemistry, tumour enrichment, and reflex testing at diagnosis, should be implemented. Guidelines for tissue acquisition and sample processing may help to improve sample adequacy to perform downstream testing. Communication among genomic laboratories will help to standardise test availability across England and local auditing could identify further areas for optimisation, including ways to improve turnaround times and adequacy rates.

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.

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.

Ninety-day mortality following lung cancer surgery: outcomes from the English national clinical outcomes audit.

Accurately explaining perioperative mortality and risk to patients is an essential part of shared decision making. In the case of lung cancer surgery, the currently available multivariable mortality prediction tools perform poorly, and could mislead patients. Using data from 2004 to 2012, this group has previously produced data tables for 90-day postoperative mortality, to be used as a communication aid in the consenting process. Using National Lung Cancer Clinical Outcomes audit data from 2017 to 2018, we have produced updated early mortality tables, to reflect current thoracic surgery practice.

Frailty in Patients With Lung Cancer: A Systematic Review and Meta-Analysis.

BACKGROUND: Previous studies regarding the prevalence of frailty in patients with lung cancer and mortality in frail patients with lung cancer are inconsistent and require clarification. RESEARCH QUESTION: What is the prevalence and impact of frailty in patients with lung cancer? STUDY DESIGN AND METHODS: This systematic review and meta-analysis used a combination of free-text terms and medical subject headings terms, according to the database requirements in MEDLINE/PubMed, Scopus, and Cochrane Library from inception until November 15, 2020. RESULTS: A total of 2,571 articles were identified, and 16 articles involving 4,183 patients were included for study. The prevalence of frailty in lung cancer was 45% (95% CI, 28-61; I2 = 99.5%; P < .0001). In patients with lung cancer, frailty was associated with an increased hazard ratio for mortality (hazard ratio, 3.01; 95% CI, 1.77-5.10; P < .001). INTERPRETATION: The prevalence of frailty in lung cancer is 45%, which has a significant negative impact on survival of patients with lung cancer. These results highlight the importance of measuring frailty, which provides important prognostic information, and may provide opportunities for interventions to improve outcomes in patients with lung cancer.

Association between time-to-treatment and outcomes in non-small cell lung cancer: a systematic review.

BACKGROUND: National targets for timely diagnosis and management of a potential cancer are driven in part by the perceived risk of disease progression during avoidable delays. However, it is unclear to what extent time-to-treatment impacts prognosis for patients with non-small cell lung cancer, with previous reviews reporting mixed or apparently paradoxical associations. This systematic review focuses on potential confounders in order to identify particular patient groups which may benefit most from timely delivery of care. METHODS: Medline, EMBASE and Cochrane databases were searched for publications between January 2012 and October 2020, correlating timeliness in secondary care pathways to patient outcomes. The protocol is registered with PROSPERO (the International Prospective Register of Systematic Reviews; ID 99239). Prespecified factors (demographics, performance status, histology, stage and treatment) are examined through narrative synthesis. RESULTS: Thirty-seven articles were included. All but two were observational. Timely care was generally associated with a worse prognosis in those with advanced stage disease (6/8 studies) but with better outcomes for patients with early-stage disease treated surgically (9/12 studies). In one study, patients with squamous cell carcinoma referred for stereotactic ablative radiotherapy benefited more from timely care, compared with patients with adenocarcinoma. One randomised controlled trial supported timeliness as being advantageous in those with stage I-IIIA disease. CONCLUSION: There are limitations to the available evidence, but observed trends suggest timeliness to be of particular importance in surgical candidates. In more advanced disease, survival trends are likely outweighed by symptom burden, performance status or clinical urgency dictating timeliness of treatment.