BACKGROUND:Childhood maltreatment (CM) has been associated with a higher risk of developing cancer later in life. However, the association between CM and lung cancer has not been thoroughly validated, and a comprehensive assessment of the mechanistic pathways underlying this relationship remains unaddressed. OBJECTIVE:This study aims to prospectively investigate the impact of CM and its specific subtypes on the risk of incident lung cancer in adulthood, and to explore the mediating effects of unfavorable lifestyles, psychological adversity, and biological alterations. METHODS:We included 117,281 participants enrolled in the UK Biobank between 2006 and 2010, who were followed up until June 1, 2022. CM was retrospectively assessed in 2016 using the online Childhood Trauma Screener. Cox proportional hazard models were employed to examine the association between CM and lung cancer. Mediation analysis was performed to estimate the mediating effects of potential mediators. RESULTS:Over a median follow-up period of 13.46 years, 414 participants were diagnosed with lung cancer. CM was associated with a greater risk of lung cancer. Each additional type of CM corresponding to an 11% higher risk (hazard ratio [HR], 1.11 [95% CI, 1.02-1.20]). The presence of physical abuse (HR, 1.42 [95% CI, 1.12-1.78]) and emotional abuse (HR, 1.33 [95% CI, 1.03-1.71]) was associated with an increased risk of lung cancer, respectively. Mediation analyses indicated that the observed association was largely mediated by smoking status (41.39%), self-rated mental problem (23.26%), and CRP-to-albumin ratio (6.64%). CONCLUSIONS:CM experiences are associated with higher risk of developing lung cancer in later life. Furthermore, smoking and self-rated mental problem constitute the main pathways in this relationship. These findings not only suggest the potential clinical value of incorporating childhood maltreatment assessments into future precision screening models, but also underscore the necessity of deeply integrating mental health support with traditional smoking cessation interventions for this vulnerable demographic. Fundamentally, preventing the occurrence of maltreatment during childhood can effectively mitigate the subsequent risk of lung cancer incidence.

Large-scale, standardized epidemiological evidence on the relationship between low fruit consumption and lung cancer mortality remains limited. Based on the Global Burden of Disease (GBD) Study 2021, we aimed to systematically describe the association between low fruit consumption and global lung cancer mortality, and its spatiotemporal distribution patterns. Age-standardized exposure value (ASEV) was used to assess low fruit consumption levels, and age-standardized mortality rate (ASMR) was used to evaluate lung cancer mortality burden. Through multi-source data integration and standardized processing, we assessed low fruit intake exposure and attributable lung cancer mortality across different regions, socioeconomic development levels, and demographic characteristics from 2000 to 2021, analyzing temporal trends using estimated annual percentage change (EAPC). The global ASEV for low fruit consumption was 40.90

Despite curative-intent treatment, recurrence risk in resectable non-small cell lung cancer (NSCLC) remains difficult to define, and postoperative management still depends on imperfect clinicopathologic assessment. Circulating tumor DNA (ctDNA)-based minimal residual disease (MRD) has emerged as a promising adjunct for postoperative risk stratification, early relapse detection, and perioperative response assessment. Its clinical translation, however, is still limited by low sensitivity in low-shedding diseases, imperfect specificity from biological background signals, interpretive uncertainty, and a lack of assay standardization. In this context, artificial intelligence (AI) is relevant not as a generic add-on, but as a potential means to improve weak-signal detection, refine variant-origin assignment, and translate serial ctDNA measurements into clinically interpretable risk estimates. This review considers the current clinical role of ctDNA-MRD in perioperative NSCLC, the barriers to routine implementation, and the extent to which AI may help make molecular monitoring more reliable and clinically useful.

Background: Low-dose computed tomography (LDCT) has improved early detection of lung cancer, but the identification of indeterminate pulmonary nodules (PNs) often triggers psychological distress. Evidence remains limited regarding longitudinal psychological changes following different management approaches and the identification of patients at risk for persistent distress. This study aimed to longitudinally evaluate psychological outcomes and identify predictors of symptom change in patients with LDCT-detected PNs managed by surgical resection/biopsy or radiological surveillance. Methods: A prospective observational study with baseline and 1-month follow-up assessments was conducted. A total of 936 patients with newly detected PNs were consecutively enrolled at a thoracic surgery clinic. Anxiety, depression, and insomnia were assessed using the Generalized Anxiety Disorder-7 (GAD-7), Patient Health Questionnaire-9 (PHQ-9), and Insomnia Severity Index (ISI) scales. Group differences were analyzed using Wilcoxon signed-rank tests, and multivariate logistic regression with inverse probability of treatment weighting (IPTW) was applied to identify predictors of psychological improvement. Results: At baseline, clinically significant anxiety, depression, and insomnia were reported in 40.2%, 31.7%, and 24.6% of patients, respectively. After 1 month, symptoms significantly improved across the cohort (P<0.05). Surgical resection was associated with psychological improvement compared with surveillance [anxiety: odds ratio (OR) =15.80, 95% confidence interval (CI): 10.18-25.95; depression: OR =7.38, 95% CI: 4.94-11.46; insomnia: OR =8.58, 95% CI: 5.60-13.77]. Female sex, age <60 years, and mixed ground-glass opacity (GGO) nodules were independently associated with improved distress. Conclusions: Psychological distress is highly prevalent among patients with LDCT-detected PNs. Surgical resection is significantly associated with anxiety, depression, and insomnia, while surveillance alone offers limited psychological benefit. Early identification of vulnerable subgroups is crucial to guide integrated clinical and psychological care and inform patient-centered management strategies.

Background and Objective:The integration of artificial intelligence (AI) into thoracic surgery accelerated notably over the course of 2025, transitioning from isolated diagnostic aids toward comprehensive clinical pathway integration. The objective of this narrative review is to synthesize the latest evidence on AI applications across the entire thoracic surgical workflow, organized along the patient care continuum from preoperative assessment through intraoperative execution to postoperative management. Methods:A PubMed/MEDLINE search was performed on March 6, 2026, and retrieved 378 English-language records published between January 1, 2025 and March 6, 2026. After title and abstract screening, potentially relevant articles underwent full-text review, and studies addressing the clinical applications of AI and related digital technologies across the thoracic surgical pathway were included in this narrative review. Key Content and Findings:In the preoperative domain, large-scale foundation models and computational pathology systems have demonstrated strong performance in nodule risk stratification and noninvasive genomic prediction [area under the curve (AUC) >0.900]. Notably, dual-phase computed tomography (CT) systems such as NeoPred have achieved validation for predicting pathological response to neoadjuvant immunochemotherapy. Intraoperatively, augmented reality (AR) navigation has achieved randomized controlled trial (RCT)-level evidence outperforming conventional localization, while generative AI systems have attained expert-level anatomical recognition for surgical video analytics. Postoperatively, wearable continuous monitoring systems and digital therapeutics (DTx) entered prospective clinical validation. Furthermore, large language models (LLMs) emerged as increasingly important tools for automated surgical documentation. Despite these advances, most studies remain retrospective, and domain shift across institutions limits generalizability. Conclusions:While AI has substantially affected thoracic surgery, important gaps persist regarding prospective validation and regulatory governance. Future priorities must focus on prospective multicenter interventional trials linking AI predictions to standardized clinical action protocols, federated learning architectures to overcome data silos, and the development of specialty-specific guidelines building upon the Artificial Intelligence Organization for Next Generation Surgeons (AIONS) 2025 consensus.

Abstract Background This study investigates the role of the pioneer transcription factor FOXA1 as a master gene in sustaining epithelial cell polarization in early-stage lung adenocarcinoma. The partial loss of FOXA1 is explored to determine if it will affect plasticity and progression of lung adenocarcinoma. The study also addresses the transcriptional circuitry that links polarity defects to lysosome homeostasis. Methods A multiomics approach was used to define the status of the chromatin in epithelial and mesenchymal states of A549 adenocarcinoma cells obtained with a newly synthetized TGF-β receptor inhibitor or TGF-β respectively. The study leveraged ATAC-seq, RNA sequencing, Cut&Tag sequencing of FOXA1 and histone marks profiling. The functional impact of FOXA1 was examined by partial silencing in vitro and by heterozygous FOXA1 deletion in a Kras G12D mouse model. Three-dimensional organoid culture, high-resolution electron microscopy, spatial transcriptomics and multiplex immunohistochemistry assessed carcinoma cell polarity, proliferation, the tumor microenvironment and organelle content. Group differences were evaluated with two-tailed t tests or one-way analysis of variance. Results FOXA1 binding and expression were highest in cells harboring an epithelial phenotype. In mouse Kras G12D LUAD tumors FOXA1 marked polarized, CDH1-positive cells; heterozygous loss diminished CDH1, disrupted apical-basal architecture, lowered organoid-forming efficiency and remodeled the immune microenvironment. Spatial transcriptomics and ultrastructural analyses showed that FOXA1-deficient carcinoma cells accumulated lysosomes, down-regulated vesicle fusion genes of the SNARE family and activated the lysosomal CLEAR gene network. FOXA1 occupied enhancers of lysosome-associated genes and competed with the transcription factor TFE3, thereby suppressing transcription of cathepsin B and cathepsin C and restricting lysosome biogenesis. Conclusions FOXA1 is a central regulator that preserves epithelial cell polarity and limits lysosome formation in lung adenocarcinoma. Targeting the FOXA1–TFE3–lysosome axis may affect tumor plasticity and provide new therapeutic opportunities.

Background and Objective: The year 2025 brought important refinements in thoracic surgery and thoracic oncology, particularly in risk-adapted screening, biomarker-guided perioperative therapy, minimally invasive surgery, and perioperative recovery. This narrative review summarizes clinically relevant evidence reported in 2025 and discusses how these updates may influence multidisciplinary practice. Methods: We conducted a narrative review of literature and conference reports published or presented between January 1 and December 31, 2025. PubMed, Google Scholar, and the official abstract proceedings/ websites of the American Society of Clinical Oncology (ASCO), the World Conference on Lung Cancer (WCLC), and the European Society for Medical Oncology (ESMO) were searched using combinations of disease-, treatment-, and surgery-related keywords. Earlier pivotal trials were cited selectively to provide background context where needed. Key Content and Findings: The National Comprehensive Cancer Network (NCCN) updated lung cancer screening criteria toward broader risk inclusion. In resectable non-small cell lung cancer (NSCLC), 2025 data further refined perioperative strategies for molecularly selected and driver-negative populations, including long-term outcomes from immunotherapy trials and phase III evidence for neoadjuvant targeted therapy. Robot-assisted thoracic surgery (RATS) continued to show advantages in selected complex settings, especially after induction treatment. Progress in mesothelioma, esophageal cancer, perioperative rehabilitation, and digital symptom monitoring also underscored the increasing importance of whole-patient care. However, several emerging tools, including circulating tumor DNA (ctDNA) and artificial intelligence (AI)-assisted models, still require prospective validation, standardization, and broader accessibility before routine implementation. Conclusions: Recent advances in thoracic oncology increasingly support a more precise, multidisciplinary, and patient-centered model of care. The most meaningful progress in 2025 lay not only in new treatments, but also in better integration of molecular stratification, surgical decision-making, and perioperative management.

The integration of artificial intelligence (AI) into surgical practices is advancing towards greater intelligence and precision. This study assesses the potential of AI in video-assisted thoracoscopic surgery (VATS) lobectomy for lung cancer by developing an AI system named LungSurg. LungSurg comprises two interconnected networks: a segmentation network for identifying intrathoracic anatomy and surgical instruments, and a classification network for recognizing surgical phases. We prospectively collected 222 VATS lobectomy videos from eight centers, generating over 32,000 annotations and more than one million frames with phase information. In external validation, the segmentation network achieved mean Average precision scores of 0.745 for the left lung and 0.726 for the right lung across various instruments and anatomical structures. The classification network demonstrated Top-1 and Top-3 accuracies of 71.5% and 88.0%, respectively, in identifying 14 surgical phases. Comparative experiments revealed that LungSurg performed comparably to senior surgeons in anatomical identification and surpassed them in sensitivity. In addition, an educational study showed that surgical residents trained with LungSurg significantly improved their anatomical identification and phase classification skills compared to those using conventional methods. These results indicate that LungSurg accurately analyzes VATS lobectomy procedures, highlighting the feasibility and potential of AI-driven tools in enhancing thoracic surgical practices.

Background:Research has increasingly shown that lipid metabolism contributes to lymph node metastasis (LNM) and the progression of cancer. This study investigates the role of lipid metabolism in LNM of lung adenocarcinoma (LUAD). Methods:We gathered clinicopathological data and RNA-sequencing information from LUAD patients using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We identified differentially expressed lipid metabolism-related genes (LMRGs) in LUAD with and without LNM. Using a prognostic risk scoring system, we correlated LMRGs with clinicopathological outcomes, genomic alterations, immune features, immunotherapy responses, and drug susceptibility. Results:A three-gene lipid metabolic signature (GPD1L, SPHK1, and ST3GAL4) associated with LNM and tumor progression was identified. High-risk patients exhibited worse overall survival (OS), increased infiltration of M0 macrophages and degranulating mast cells, and were more likely to be non-responsive to immunotherapy. Additionally, the high-risk group showed higher tumor mutation burden (TMB) and programmed death-ligand 1 (PD-L1) expression, suggesting a potential benefit from combined immunotherapy and LMRG-targeted therapy. Differentially expressed genes (DEGs) in the high-risk group were enriched in the reactive oxygen species pathway. Conclusions:Our findings provide new insights into the molecular mechanisms underlying regional metastasis in LUAD related to lipid metabolism and highlight the potential for precision therapies targeting this pathway.

The concomitant development and evolution of lung computed tomography (CT) and artificial intelligence (AI) have made non-invasive lung imaging a key component of clinical care of patients. However, the scarcity of labeled CT data and the limited generative capacity of existing models have constrained their clinical utility. Here, we present LCTfound, a large-scale vision foundation model designed to overcome these limitations. Trained on a multi-center dataset comprising 105,184 CT scans, LCTfound leverages diffusion-based pretraining and joint encoding of imaging and clinical information to support 8 tasks, including CT enhancement, virtual computed tomography angiography (CTA), sparse-view reconstruction, lesion segmentation, diagnosis, prognosis, cancer pathological response prediction, and three-dimensional surgical navigation. In comprehensive multicenter evaluations, LCTfound consistently outperforms leading baseline models, delivering a unified, broadly deployable solution that both augments clinical decision-making and elevates CT image quality across diverse practice settings. LCTfound establishes a scalable foundation for next-generation clinical imaging intelligence, uniting large AI model with precision healthcare.

ABSTRACTBackgroundPlasma protein has gained prominence in the non‐invasive predicting of lung cancer. We utilised Zeolite Zotero NaY‐based plasma proteomics to investigate its potential for multiple event predicting, including lung cancer diagnosis (task #1), lymph node metastasis detection (task #2) and tumour‒node‒metastasis (TNM) staging (task #3).MethodsA total of 4703 plasma proteins were quantified from 241 participants based on a prospective cohort of 2757 participants. An additional 46 participants from external prospective cohort of 735 participants were used for validation. Feature selection was performed using differential expressed protein analysis, area under curve (AUC) evaluation and least absolute shrinkage and selection operator (LASSO) regression. Random forest was used for multitask model construction based on the key proteins. Feature importance was interpreted using Shapley additive explanations (SHAP) algorithm.ResultsFor task #1, 10 proteins panel showed an AUC of .87 (.77‒.97) in the external validation. After integrating clinical factors, a significant increase diagnostic accuracy was observed with AUC of .91 (.85‒.98). For task #2, nine proteins panel achieved an AUC of .88 (.80‒.96), integration model showed an increase diagnostic accuracy with AUC of .90 (.85‒.97). For task #3, 10 proteins panel showed an AUC of .88 (.74‒.96) for stage I, .92 (.84‒.97) for stage II, .88 (.76‒.96) for stage III and .99 (.98‒.99) for stage IV in the integration model.ConclusionsThis study comprehensively profiled the NaY‐based plasma proteome biomarker, laying the foundation for a high‐performance blood test for predicting multiple events in lung cancer.Key points Our study developed an innovative nanomaterial, Zeolite NaY, which addressed the masking effect and improved the depth of the proteome. The performance of NaY‐based plasma proteomics as a preclinical diagnostic tool was validated through both internal and external cohort. Furthermore, we explored the different patterns of plasma protein changes during the progression of lung cancer and used the explanations method to elucidate the roles of proteins in the multitask predictive model.

Genetically engineered pig lungs have not previously been transplanted into humans, leaving key questions unanswered regarding the human immune response in the context of a xenotransplanted lung and the possibility of hyperacute rejection. Here, we report a case of pig-to-human lung xenotransplantation, in which a lung from a six-gene-edited pig was transplanted into a 39-year-old brain-dead male human recipient following a brain hemorrhage. The lung xenograft maintained viability and functionality over the course of the 216 hours of the monitoring period, without signs of hyperacute rejection or infection. Severe edema resembling primary graft dysfunction was observed at 24 hours after transplantation, potentially due to ischemia-reperfusion injury. Antibody-mediated rejection appeared to contribute to xenograft damage on postoperative days 3 and 6, with partial recovery by day 9. Immunosuppression included rabbit anti-thymocyte globulin, basiliximab, rituximab, eculizumab, tofacitinib, tacrolimus, mycophenolate mofetil and tapering steroids, with adjustments made during the postoperative period based on assessments of immune status. Although this study demonstrates the feasibility of pig-to-human lung xenotransplantation, substantial challenges relating to organ rejection and infection remain, and further preclinical studies are necessary before clinical translation of this procedure.

Background and objective: Traditional biopsy methods often limit diagnostic accuracy and treatment options due to inadequate tissue samples. En-bloc biopsy (EB), a minimally invasive technique, offers adequate tissue for both pathological and genetic analysis while reducing tumor burden. This study evaluates the clinical applicability and survival benefits of EB in advanced lung cancer. Methods: We retrospectively reviewed advanced lung cancer patients with pulmonary tumors and distant metastases confirmed by PET-CT, who underwent EB via video-assisted thoracoscopic surgery (VATS) at our center from 2010 to 2020. Clinical characteristics, pathological and genetic results, surgical details, and survival data were analyzed. Kaplan-Meier and Log-Rank tests were used to compare overall survival (OS) between: (1) targeted vs. non-targeted therapies within the EB group, and (2) EB vs. traditional biopsy, with further subgroup analysis focusing on targeted therapy recipients and stage IVA patients. Results: Among 142 patients (majority male, non-smokers, under 65, ECOG 0–1), 128 (90.1 ​%) had adenocarcinoma. No severe perioperative complications or early postoperative deaths occurred. All 132 genetic samples were valid; 62.9 ​% were EGFR-positive. Median follow-up was 52.0 months; median OS, 66.0 months. In the EB group, targeted therapy was linked to longer OS than non-targeted (80.0 vs. 43.0 months, p ​= ​0.0445). EB outperformed traditional biopsy in OS (66.0 vs. 28.0 months, p ​= ​0.0025). Subgroups receiving targeted therapy (HR ​= ​0.55, p ​= ​0.0260) and with stage IVA disease (HR ​= ​0.66, p ​= ​0.0338) showed survival benefit. Conclusion: VATS-based EB is safe and feasible in advanced lung cancer, improves access to precision therapy, and significantly prolongs survival.

Background:Postoperative pulmonary complications (PPCs) remain one of the common challenges in video-assisted thoracic surgeries. While tubeless anesthesia using laryngeal mask has emerged as a fast-track recovery approach, offering multiple advantages for both lungs, such as reduced anesthetic dosage, avoidance of airway injury, and facilitation of surgical manipulation, its impact on the non-operative lung remains unclear. We aim to conduct a retrospective study based on computed tomography (CT) imaging. Methods:We included lung surgery cases from January 2020 to March 2025 at The First Affiliated Hospital of Guangzhou Medical University. Pre- vs. postoperative CT reports were compared to identify novo contralateral lung abnormalities. Intraoperative respiratory parameters were collected and analyzed. Propensity score matching (PSM) was used to balance confounding factors between the tubeless anesthesia and intubated groups, followed by a conditional logistic regression model to analyze the incidence and severity of PPCs as the dependent variables, aiming to clarify the safety and efficacy of this anesthesia method. Results:After screening 140,036 surgeries, 1,294 cases were eligible. Following 2:1 PSM, 427 patients in the intubated group were matched with 234 patients in the tubeless group. The tubeless group showed lower average airway pressures (peak: 19.39 vs. 6.80 cmH2O, mean: 7.28 vs. 3.23 cmH2O, plateau: 17.88 vs. 8.03 cmH2O, all P<0.001) and ventilation parameters (tidal volume: 320.42 vs. 279.90 mL, P<0.001; minute ventilation: 4.65 vs. 3.47 L, P<0.001). For surgeries lasting 60-120 minutes, the tubeless group showed lower rates of overall CT changes (15.6% vs. 8.2% P=0.04) and consolidation (12.6% vs. 5.7% P=0.03). Postoperative hospital stay was shorter in the tubeless group (4.51 vs. 6.43 days, P<0.001). Average respiratory rate [odds ratio (OR) 1.36, 95% confidence interval (CI): 1.07-1.74, P=0.01] and minute ventilation (OR 1.32, 95% CI: 0.91-1.90, P=0.14) showed the strongest correlation with CT changes. Conclusions:Tubeless approach is associated with lower mechanical ventilation pressures and less overall image abnormalities, potentially protecting the non-operative lung. These findings suggest that tubeless approach has protective effect on the non-operative lung, contributing to the enhanced recovery after thoracic surgery.