OBJECTIVES:Tobacco consumption, combined with individual genetic predispositions, contributes to an age-dependent risk not only for lung cancer but also for other non-communicable diseases (NCDs) such as cardiovascular disease (CVD), chronic obstructive pulmonary disease (COPD), osteoporosis, and diabetes. The MULTIPREVENT project aims to validate whether low-dose computed tomography (LDCT) of the chest, combined with simple biomarkers, functional tests, and genomic profiling, can serve as an effective tool for comprehensive health assessment and risk prediction of multimorbidity in adults. STUDY DESIGN:The study is based on a prospective epidemiological design involving 3000 participants from the MOLTEST-BIS lung cancer screening cohort (2016-2018). These participants, aged 50-79 years (during MOLTEST-BIS) and with a smoking history of at least 30 pack-years, will undergo two follow-up assessments in 2025-2027 and 2030-2032. METHODS:Each follow-up includes LDCT, spirometry, standardized blood pressure measurement, anthropometric evaluation, biomarker assessment (lipid profile, lipoprotein(a), glycated haemoglobin), and health-related questionnaires. Genetic profiling will be performed using the Illumina Infinium Global Screening Arrays approach to identify inherited predispositions to major NCDs. All data, clinical, imaging (including radiomics), molecular, and genetic, will be integrated through machine learning algorithms to develop AI-based risk prediction models. RESULTS:The MULTIPREVENT study is expected to generate a wide range of scientific, clinical, and infrastructural results that will serve as a foundation for future public health initiatives in integrated prevention. CONCLUSIONS:By linking imaging and biochemical markers, genetic susceptibility, and clinical parameters within a longitudinal design, MULTIPREVENT will establish data-driven, AI-supported prevention strategies aimed at reducing morbidity and mortality among adults exposed to tobacco. The project will also serve as a model for population-based multimorbidity prevention programs.

Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide, and emphysema is present in the majority of affected patients and can be identified on computed tomography (CT). This study investigated whether radiomic features derived from automatically and adaptively segmented low-attenuation lung regions can capture distinct imaging characteristics of COPD beyond conventional emphysema measures. Radiomic features were extracted from 6078 chest CT scans of 2243 participants from the COPDGene cohort. Emphysematous regions were segmented using the MimSeg method based on Gaussian mixture modelling with patient-adjusted thresholding, and radiomic features were computed for individual lesion clusters and aggregated per patient using summary statistics, yielding 780 features per subject. Uniform Manifold Approximation and Projection (UMAP) was used to generate a low-dimensional embedding, and feature contributions were evaluated using SHAP analysis and statistical testing. The resulting embedding demonstrated structured patterns broadly aligned with Global Initiative for Chronic Obstructive Lung Disease (GOLD) stages, with greater overlap among GOLD 0–2 and more consolidated groupings for GOLD 3 and 4, reflecting differences in disease severity. The most influential features were predominantly derived from Grey Level Run Length Matrix measures, capturing textural heterogeneity and spatial organisation of emphysematous changes that are not directly described by standard density-based metrics. These findings suggest that radiomic analysis of adaptively segmented CT data may provide complementary and structurally distinct information relative to conventional emphysema measures, supporting a more nuanced characterisation of emphysema patterns in COPD.

ABSTRACT Small extracellular vesicles (sEVs) are key mediators of intercellular communication, influencing diverse pathological processes, including cancer. While mass spectrometry (MS) has enabled the proteomic analysis of sEVs, sample preparation losses remain a critical bottleneck, particularly for scarce tissue-derived sEVs (Ti-EVs). Here, we systematically benchmark five proteomic workflows introducing Exo-insert, a novel single-vessel method, and Exo-SP3, across both Ti-Evs and cell culture-derived sEVs (CCM-EVs) at low input (0.5–4 µg). Exo-insert and Exo-SP3 enable the identification of ∼1100 protein groups from as little as 0.5 µg sEV input. Notably, optimal sample preparation for MS is source-dependent: Exo-insert and Exo-SP3 display divergent performance across sEV sources. Comparative DDA/DIA analyses establish sample preparation as the primary determinant of proteome recovery, offering a practical framework that matches workflows to sEV amounts and source-specific content for biomarker discovery.

Background Lung cancer remains the deadliest cancer worldwide because it is often diagnosed too late. Effective treatment depends on detection at an early screening stage. However, the growing number of patients and the limited number of radiologists lead to prolonged diagnostic waiting times. In very early stage lung cancer, nodule visibility is further reduced by adjacent blood vessels and airway walls, because nodules are often connected to or supplied by these structures. Task-specific analysis of the bronchovascular bundle is therefore important for efficient nodule detection, and its removal can increase the diagnostic potential of lung cancer screening. Materials and Methods To assess the efficacy of the proposed method, we used series from widely utilized LDCT datasets, including the Duke Lung Cancer Screening (DLCS) dataset and the Pilot Pomeranian Lung Cancer Screening Program. The proposed bronchovascular bundle segmentation pipeline, RONALD, operates on computed tomography images and returns binary masks of vessels and bronchi located in the lung parenchyma. The method includes a preprocessing stage with lung, lobe, and mediastinum segmentation, followed by separate vessel and bronchial tree segmentation. Results The proposed pipeline segmented the bronchovascular bundle in low-dose computed tomography scans while improving nodule retention compared with other segmentation methods: from 93.98 Conclusion The resulting segmentations can improve lung nodule detection in the very early stages of lung cancer.

ABSTRACT Formalin-fixed, paraffin-embedded (FFPE) tissues remain an essential resource for molecular studies, yet formalin-induced cytosine deamination introduces characteristic C>T/G>A artifacts that compromise the accuracy of next-generation sequencing (NGS) analyses. Numerous computational methods and enzymatic DNA repair strategies have been proposed to reduce these artifacts, but no systematic comparison across tools and experimental conditions exists. Here, we evaluate the performance of seven computational approaches (SOBDetector, Ideafix, MicroSEC, FFPolish, DeepOmics FFPE/FFPE-PLUS, FFPErase) together with the NEBNext® FFPE DNA Repair Mix v2, a multi-enzyme repair system applied during DNA preparation. Using three independent datasets, one based on whole genome sequencing (CGCI-BL) and two on whole exome sequencing (TCGA-PC and SUT-LUAD, the latter containing enzymatically repaired samples), and matched fresh-frozen samples as the gold standard, we assess precision, sensitivity, and artifact reduction efficiency across all methods. We further examine the potential synergy between enzymatic repair and post-sequencing computational filtering. Our results provide practical guidelines for FFPE artifact correction and demonstrate that enzymatic treatment provides the best results, while among the computational methods, FFPErase offers the most robust reduction of cytosine deamination artifacts while maximizing the retention of true somatic variants. KEY MESSAGES Formalin fixation in FFPE samples introduces artifacts that can significantly affect the accuracy of NGS analyses. Among the evaluated approaches, enzymatic repair using NEBNext® FFPE DNA Repair Mix v2 achieves the most effective reduction of sequencing artifacts. Computational methods vary in performance, with FFPErase showing the most robust balance between artifact removal and retention of true somatic variants. Combining enzymatic repair with computational filtering did not lead to consistent improvements in performance across datasets.

OBJECTIVES:This observational case-control study aims to evaluate the prevalence of comorbidities and 10-year overall survival in an LDCT-screened population compared to a propensity score-matched general Polish population. METHODS:Aged 50-75 years (n=43,686) with at least 10 years of follow-up were included. The screening group (n=7281) underwent LDCT lung cancer screening between 2009 and 2011. The control group (n=36,405) was matched from the general population using 32 variables. The primary endpoint was 10-year overall survival; secondary endpoints included comorbidity prevalence and multivariate analysis results. RESULTS:Common comorbidities included hypertension (52.2%), chronic coronary artery disease (20.5%), hypercholesterolemia (13.2%), and diabetes (13.0%). Multivariate analysis showed improved survival associated with LDCT screening (HR=0.653), hypertension (HR=0.882), hypercholesterolemia (HR=0.567), and female sex (HR=0.527). The 10-year overall survival was higher in the screening group by 5.6 percentage points (86.9%) compared with controls (81.3%, p<0.001). CONCLUSIONS:Participation in lung cancer screening using LDCT was associated with longer 10-year overall survival in adults aged 50-75 years. These results support the use of LDCT in high-risk individuals and emphasize the need for prospective studies to elucidate the mechanisms underlying the observed association.

Lung cancer, the leading cause of cancer-related deaths globally and in Poland, accounts for 25% of all cancer-related deaths, with smoking being its predominant cause. While primary prevention through smoking cessation is crucial, the effectiveness of lung cancer screening (LCS) with low-dose computed tomography in reducing mortality has gained international recognition. This expert consensus, developed through multidisciplinary collaboration, proposes a comprehensive framework for smoking cessation interventions within LCS. Key recommendations include providing participants with educational materials, cognitive-behavioral counseling, and pharmacotherapy. Proactive follow-up, biochemical addiction validation, and teleconsultations are essential to ensure long-term cessation. Besides, participants should be discouraged from using alternative nicotine products, such as heated tobacco or electronic cigarettes due to their limited efficacy, highly probable health risks and potential for nicotine addiction. By integrating evidence-based cessation methods, LCS programs can serve as a model for broader smoking cessation strategies in healthcare.

INTRODUCTION:Lung cancer screening (LCS) using low-dose-computed tomography reduces lung cancer mortality in high-risk individuals. Evaluating and monitoring LCS programs are important to ensure and improve quality, efficiency, and participant outcomes. There is no agreement on LCS quality indicators (QIs). METHODS:Twenty multidisciplinary members of the International Association for the Study of Lung Cancer used a Delphi process to develop consensus QIs. They considered 50 QIs during information/discussion sessions and two anonymous voting rounds. In total, 80% or more voting agree or strongly agree on a five-point Likert scale determined consensus. RESULTS:Twenty essential and six desirable QIs were identified in 10 of 11 LCS pathway domain categories (ENTRY: Proportion eligible who got screened; SMOKING_CESSATION: Proportion of current-smoking individuals offered cessation interventions; IMAGING: Proportion screened requiring clinical diagnostic assessment, scan results distribution, proportion scans requiring early follow-up, proportion baseline or regular scans with actionable additional findings; ADHERENCE to: Annual or regular scans, early interim scans, clinical diagnostic assessment; DIAGNOSTIC: Proportion suspicious-for-lung-cancer scans receiving clinical investigation, undergoing invasive diagnostic procedures; OUTCOMES: Cancer detection rate, stage distribution, interval cancer rate; HARMS: Number and proportion of serious complications after invasive procedures, non-lung cancer diagnoses after invasive procedures or surgery, 30-day mortality after invasive procedure; TREATMENT: Proportion early-stage cancers receiving treatment with curative intent; WAIT_TIMES: Suspicious-for-lung-cancer scan to definitive diagnosis, to curative-intent treatment for individuals with early-stage disease, scan completion to reporting results to primary care provider and participant; EQUITY: Race, sex, and socioeconomic differences in adherence to regular screens, early-stage cancer treatment, offer of smoking cessation interventions, clinical investigation of suspicious-for-lung-cancer screens). CONCLUSIONS:A review among panel members provided recommended LCS QIs that should be considered in the development of LCS initiatives.

Linear endobronchial ultrasound-guided sampling of accessible mediastinal lesions is well established as a first-choice modality for lung cancer mediastinal staging. Parenchymal lung lesions, however, are routinely accessed by either a percutaneous (computed tomography guided) or a bronchoscopic approach. Direct comparisons between the percutaneous approach and bronchoscopy, endobronchial ultrasound, or mediastinoscopy are sparse in regard to diagnostic accuracy, and it remains unknown which sampling technique is the safest and offers the most adequate material for comprehensive biomarker testing. This guideline addresses new evidence and aims to answer these questions relevant to contemporary lung cancer clinical practice. A multidisciplinary expert panel from the American Association of Bronchology and Interventional Pulmonology and the Early Detection and Screening Committee of the International Association for the Study of Lung Cancer was convened to address four Patient, Intervention, Comparison, and Outcome questions pertaining to the safety and adequacy of comprehensive biomarker testing for frequently used intrathoracic biopsy techniques. The panel included 24 experts in thoracic procedures, including 18 pulmonologists, two radiologists, one pathologist, and three thoracic surgeons from 22 hospitals across 12 countries. All panel members participated in the development of the final recommendations using a modified Delphi technique. Specific recommendations are provided on safety and adequacy of minimally invasive thoracic interventions on patients with confirmed or suspected lung cancer for which comprehensive biomarker testing is needed for standard of care or clinical trial participation.

Lung cancer remains the leading cause of cancer-related deaths worldwide. Low-dose computed tomography (LD-CT) screening, combined with effective minimally invasive molecular testing such circulating microRNA, has the potential to reduce the burden of lung cancer. However, their clinical application requires further validation, including studies across diverse patient cohorts from different countries. In this study, we propose a signature of 9 circulating miRNAs derived from a robust multi-platform workflow with a multi-center design, for a total of 276 lung cancer and 451 non-cancer controls, based on the data from two European LD-CT screening cohorts (Poland and Italy). The classification performance of the signature was stable in the two screening cohorts, with AUC=0.78 (SE, 76%; SP, 67%; ACC=70%), and AUC=0.75 (SE, 82%; SP, 68%; ACC=71%) in the Polish and Italian cohorts, respectively. The diagnostic accuracy of the signature was remarkably independent of age, gender, smoking (status and intensity), nodule size, and density. Additionally, the signature demonstrated strong performance in detecting stage I lung cancer, with AUC=0.76 (95%CI: 0.68-0.84), and 0.69 (95%CI: 0.49-0.89) in the Polish and Italian cohorts respectively, with a prediction ability of 63-73%. The signature's ability to discriminate benign nodules was satisfactory, with AUC=0.71 (95%CI: 0.58-0.84). The proposed panel of 9 circulating miRNAs provides a robust and precise diagnostic tool to substantially advance the effectiveness of the LD-CT screening program.

Background: The prediction of postoperative functional status in non-small cell lung cancer patients based on preoperative assessment of physical and respiratory capacity is inadequate based on recent RCTs. Material and methods: Prospectively collected spirometry data and the six-minute walk test results of 57 patients treated with lobectomy for non-small cell lung cancer were analyzed. The tests were performed before surgery, and 30 and 90 days after lobectomy. All patients underwent a respiratory functional and physical capacity assessment. Results: All 57 patients underwent lobectomy. Before surgery, mean FEV1 was 2.4 ± 0.7 L, corresponding to %FEV1 of 88.3 ± 17.3%. The mean absolute and expected 6MWT distance was 548 ± 74.6 m and 108.9 ± 14.5%, respectively. At the first postoperative evaluation 30 days after surgery, FEV1 and %FEV1 decreased significantly by an average of 0.5 ± 0.3 L and 15.1 ± 10.7%, while 6MWT and expected 6MWT decreased minimally by an average of 1.0 m and 0.8%, respectively. Three months after lobectomy, FEV1 and %FEV1, compared with the initial assessment, decreased by an average of 0.3 ± 0.3 l and 7.8 ± 10.0%, while 6MWT and its expected score increased to 564.6 ± 84.6 m and 112.8 ± 15.8%, respectively. Conclusions: After lobectomy, FEV1 decreased slightly and less than expected, while 6MWT increased proportionally compared to the preoperative evaluation.