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Background: In prior randomised controlled trials, lung cancer screening using low-dose computed tomography (LDCT) has been shown to reduce lung cancer mortality and overall mortality. Despite these results, organised screening in France remains a challenge. This study assessed the feasibility and efficacy of lung cancer screening within a real-life context in a French administrative territory., Methods: DEP KP80 was a single-arm prospective study. Participants aged between 55 and 74 years, smokers or former smokers of ≥30 pack-years, were recruited. An annual LDCT scan was scheduled and three rounds were performed. Subjects were selected by general practitioners or pulmonologists, who checked the inclusion criteria and prescribed the CT scan., Findings: Between March 2016 and February 2020, 1254 participants were enrolled. Overall, 945 (75.4%) participants underwent baseline LDCT (T0), 376 (42.8%) completed the first round (T1) and 270 (31%) the second (T2) one. Forty-two lung cancers were diagnosed, 30 cancers (71.4%) were stage I or II and 34 cancers (80.9%) were treated surgically. In this study, the overall positive predictive value for a positive screening was 48% (95% CI 37-59) and the negative predictive value 100% (95% CI 100-100)., Interpretation: This study demonstrated the feasibility and efficacy of lung cancer screening in a real-life context with most lung cancers diagnosed at an early stage and surgically removed. Our results also highlighted the importance of participation in each round, underlining the fact that optimising organisation is a major goal., Funding: Agence Régionale de Santé de Picardie, La Ligue contre le cancer, le Conseil Départemental de la Somme, and AstraZeneca., Competing Interests: Declaration of interests NS, DB, VJ, JdB, VP, BM, PB, MA and AL declare no potential conflicts of interest. SC declares grants from AstraZeneca, Amgen, Adene, BDbioscience, BMS, Boeringher, Chugai, Janssens, Laidet, Lilly, MSD, Novartis, Roche, Pfizer, Takeda, Volition and Transdiag, royalties from Volition, payment for expert testimony from Amgen, Astra Zeneca, Boeringher, Health Event, MSD, Novartis, Pfizer and Sanofi, and support for attending meetings from Amgen, Astra Zeneca, BMS, Chugai, Janssens, Laidet and Takeda. OL declares grants from Chiesi, AstraZeneca, BMS and Amgen, and support for attending meetings from France Oxygene and Aeris., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: Pulmonary nodules are a common incidental finding on chest Computed Tomography scans (CT), most of the time outside of lung cancer screening (LCS). We aimed to evaluate the number of incidental pulmonary nodules (IPN) found in 1 year in our hospital, as well as the follow-up (FUP) rate and the clinical and radiological features associated with FUP., Methods: We trained a Natural Language Processing (NLP) tool to identify the transcripts mentioning the presence of a pulmonary nodule, among a large population of patients from a French hospital. We extracted nodule characteristics using keyword analysis. NLP algorithm accuracy was determined through manual reading from a sample of our population. Electronic health database and medical record analysis by clinician allowed us to obtain information about FUP and cancer diagnoses., Results: In this retrospective observational study, we analyzed 101,703 transcripts corresponding to the entire CTs performed in 2020. We identified 1,991 (2 %) patients with an IPN. NLP accuracy for nodule detection in CT reports was 99 %. Only 41 % received a FUP between January 2020 and December 2021. Patient age, nodule size, and the mention of the nodule in the impression part were positively associated with FUP, while nodules diagnosed in the context of COVID-19 were less followed. 36 (2 %) lung cancers were subsequently diagnosed, with 16 (45 %) at a non-metastatic stage., Conclusions: We identified a high prevalence of IPN with a low FUP rate, encouraging the implementation of IPN management program. We also highlighted the potential of NLP for database analysis in clinical research., Competing Interests: Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Masson SAS.. All rights reserved.)

Introduction: Implementation of Lung cancer screening (LCS) programs is challenging. The ILYAD study objectives is to evaluate communication methods to improve participation rate among the Lyon University Hospital employees. In this first part of the study, we aimed to determinate the number of eligible individuals among our population of employees., Method: In November 2020, we conducted a questionnaire based cross sectional survey among the Lyon University Hospital employees (N = 26,954). We evaluated the PLCO m2012 risk prediction model and the eligibility criteria recommended by French guidelines. We assessed the proportion of eligible individuals among the responders and calculated the total eligible individuals in our hospital., Results: Overall, 4,526 questionnaires were available for analysis. 16.0% were current smokers, and 28.2% were former smokers. Among the 50-75yo ever-smoker employees, 27% were eligible according to the French guidelines, 2.7% of all eversmokers according to a PLCO m2012 score ≥ 1.51%, and thus, 3.8% of the surveyed population were eligible to the combined criteria. The factors associated with higher eligibility among 50-75yo ever-smokers were educational level, feeling symptoms related to tobacco smoking, personal history of COPD and family history of lung cancer. Using the French guidelines criteria only, we estimated the total number of eligible individuals in the hospital at 838., Conclusion: In this study, we determined a theoretical number of eligible employees to LCS in our institution and the factors associated to eligibility. Secondly, we will propose LCS to all eligible employees of Lyon University Hospital with incremented information actions., Competing Interests: Declaration of Competing Interest None., (Copyright © 2022. Published by Elsevier Masson SAS.)

Competing Interests: All authors declare no competing interests.