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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., (Copyright © 2023 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.)

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; I 2  = 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., (Copyright © 2022 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.)

Background: Programmed death-ligand 1 (PD-L1) expression on cancer cells is a clinically important biomarker to select patients with non-small cell lung cancer (NSCLC) for treatment with programmed death-1/PD-L1 inhibitors. Clinical trials of immunotherapy in patients with NSCLC have required histologic evidence for PD-L1 testing; in clinical practice, cytologic samples commonly are acquired in patients with advanced disease., Research Question: This study aims to investigate whether endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) samples are adequate for PD-L1 testing in NSCLC., Study Design and Methods: This study investigates the sampling adequacy of EBUS-TBNA for PD-L1 testing when compared with other methods. Furthermore, the relationship between clinicopathologic characteristics and PD-L1 expression in the study population have been examined. Five hundred seventy-seven NSCLC specimens were analyzed from consecutive patients with NSCLC across six centers in the United Kingdom and one center in the United States between January 2015 and December 2016., Results: In the EBUS-TBNA group (189 specimens), the overall percentage of patients with successful PD-L1 testing was 94.7%. There was no significant difference in sampling adequacy with other methods of tissue acquisition. Older patients had higher failure rates of PD-L1 testing (OR, 1.06; P = .008). In multivariate analysis, advanced N-stage (P = .048) and presence of brain metastasis (P < .001) were associated with high PD-L1 expression., Interpretation: This large multicenter study shows that EBUS-TBNA provides samples adequate for PD-L1 testing and that advanced N stage and the presence of brain metastasis are associated with high PD-L1 expression., (Copyright © 2020 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.)

Background: Clinical staging of non-small cell lung cancer (NSCLC) helps determine the prognosis and treatment of patients; few data exist on the accuracy of clinical staging and the impact on treatment and survival of patients. We assessed whether participant or trial characteristics were associated with clinical staging accuracy as well as impact on survival., Methods: We used individual participant data from randomized controlled trials (RCTs), supplied for a meta-analysis of preoperative chemotherapy (± radiotherapy) vs surgery alone (± radiotherapy) in NSCLC. We assessed agreement between clinical TNM (cTNM) stage at randomization and pathologic TNM (pTNM) stage, for participants in the control group., Results: Results are based on 698 patients who received surgery alone (± radiotherapy) with data for cTNM and pTNM stage. Forty-six percent of cases were cTNM stage I, 23% were cTNM stage II, and 31% were cTNM stage IIIa. cTNM stage disagreed with pTNM stage in 48% of cases, with 34% clinically understaged and 14% clinically overstaged. Agreement was not associated with age (P = .12), sex (P = .62), histology (P = .82), staging method (P = .32), or year of randomization (P = .98). Poorer survival in understaged patients was explained by the underlying pTNM stage. Clinical staging failed to detect T4 disease in 10% of cases and misclassified nodal disease in 38%., Conclusions: This study demonstrates suboptimal agreement between clinical and pathologic staging. Discrepancies between clinical and pathologic T and N staging could have led to different treatment decisions in 10% and 38% of cases, respectively. There is therefore a need for further research into improving staging accuracy for patients with stage I-IIIa NSCLC., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Endobronchial ultrasound (EBUS)-guided biopsy is the mainstay for investigation of mediastinal lymphadenopathy for laboratory diagnosis of malignancy, sarcoidosis, or TB. However, improved methods for discriminating between TB and sarcoidosis and excluding malignancy are still needed. We sought to evaluate the role of genomewide transcriptional profiling to aid diagnostic processes in this setting., Methods: Mediastinal lymph node samples from 88 individuals were obtained by EBUS-guided aspiration for investigation of mediastinal lymphadenopathy and subjected to transcriptional profiling in addition to conventional laboratory assessments. Computational strategies were used to evaluate the potential for using the transcriptome to distinguish between diagnostic categories., Results: Molecular signatures associated with granulomas or neoplastic and metastatic processes were clearly discernible in granulomatous and malignant lymph node samples, respectively. Support vector machine (SVM) learning using differentially expressed genes showed excellent sensitivity and specificity profiles in receiver operating characteristic curve analysis with area under curve values > 0.9 for discriminating between granulomatous and nongranulomatous disease, TB and sarcoidosis, and between cancer and reactive lymphadenopathy. A two-step decision tree using SVM to distinguish granulomatous and nongranulomatous disease, then between TB and sarcoidosis in granulomatous cases, and between cancer and reactive lymphadenopathy in nongranulomatous cases, achieved > 90% specificity for each diagnosis and afforded greater sensitivity than existing tests to detect TB and cancer. In some diagnostically ambiguous cases, computational classification predicted granulomatous disease or cancer before pathologic abnormalities were evident., Conclusions: Machine learning analysis of transcriptional profiling in mediastinal lymphadenopathy may significantly improve the clinical utility of EBUS-guided biopsies., (Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.)