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:Epidermal growth factor receptor (EGFR) mutations are the most common oncogenic subtype in non-small cell lung cancer (NSCLC) among Asians. EGFR tyrosine kinase inhibitors (TKIs) have become the mainstay of therapy, significantly improving survival outcomes. However, prognostic factors influencing survival in real-world settings among patients treated with EGFR-TKIs remain underexplored. This study aims to identify prognostic factors in EGFR-TKI-treated patients using data from a nationwide registry. Methods:Patient data were sourced from the "Meina Xinsheng" registry, with survival metrics provided by the China Center for Disease Control. We analyzed the impact of sex, age, disease stage, histology, gene mutation type, and Karnofsky Performance Status (KPS) score on duration of treatment (DoT), overall survival (OS), and the incidence of long-term survival (>5 years), using both univariate and multivariate analyses. A reference cohort of EGFR wild-type patients receiving EGFR-TKI therapy was also included. Results:Among 231,699 patients registered for EGFR-TKI treatment across 3,445 hospitals nationally, 221,788 cases of advanced NSCLC were analyzed. Within the subset of 83,791 patients eligible for survival analysis spanning 2012 to 2018, the median OS was 3.2 years [95% confidence interval (CI): 3.18-3.3], and the median lung cancer-specific survival (LCSS) was 4.1 years (95% CI: 4.02-4.1). At least 7.7% of patients achieved a survival milestone of more than 5 years. Factors associated with improved OS and higher long-term survival rates included female sex, stage IIIb disease, adenocarcinoma histology, EGFR exon 19 deletion, superior KPS scores, prolonged DoT, receiving EGFR-TKI as first-line treatment, and achieving a complete response (CR). Younger patients (<40 years) exhibited better OS, albeit with a shorter DoT. Notably, patients maintaining disease control for 22 months had significantly higher long-term survival (12.8%) compared with those who did not (2.7%). Conclusions:In this large real-world cohort of advanced NSCLC patients treated with EGFR-TKI, female sex, stage IIIb (vs. stage IV) disease, adenocarcinoma histology, EGFR exon 19 deletion, and the use of EGFR-TKI as first-line therapy were independently associated with longer DoT and/or OS. These factors may help identify patients more likely to derive durable benefit from EGFR-TKIs and support risk stratification and treatment optimization in EGFR-mutant NSCLC.

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.

INTRODUCTION:The generation of drug-tolerant persister (DTP) cancer cells remains a major challenge in treating lung adenocarcinoma (LUAD) patients with EGFR tyrosine kinase inhibitors (TKIs), as these cells eventually drive drug resistance and disease progression. However, the mechanisms underlying DTP formation are poorly understood, limiting therapeutic options upon the emergence of DTP state or resistance after TKI therapy. METHODS:In this study, we analyzed samples from LUAD patients receiving frontline osimertinib therapy (including baseline, DTP, and stable resistance states) to dissect the cellular and transcriptomic features of TKI-induced DTP cells via single-cell RNA sequencing. Corresponding in vitro/in vivo experiments and external cohort validation were further conducted to validate key findings from clinical sample analysis. RESULTS:DTP cells exhibited an active drug-metabolizing phenotype, characterized by significantly upregulated GSTA1 expression regulated by RSPH1. Mechanistically, elevated GSTA1 expression in cancer cells promoted osimertinib degradation. Additionally, RSPH1+ DTP cells interacted with macrophages via PROS1-AXL signaling to establish an immunosuppressive tumor microenvironment, contributing to persister formation. Investigation of the RSPH1-CALML4-GSTA1 regulatory axis showed PROS1 expression was also governed by this axis, suggesting GSTA1 acts as an upstream regulator of the PROS1-AXL pathway. The feasibility of osimertinib combined with the GSTA1 inhibitor curzerene was evaluated in osimertinib-induced DTP and acquired resistance mouse models. Notably, this strategy showed superior efficacy compared with osimertinib combinations with chemotherapy or AXL inhibitor in both settings. CONCLUSION:Collectively, this study elucidated novel mechanisms underlying TKI-induced DTP state and provided a promising combination strategy for overcoming drug tolerance and resistance in osimertinib-treated LUAD patients.

With the widespread adoption of low-dose computed tomography (LDCT) screening, the detection rate of small pulmonary nodules has surged, necessitating precise localization for minimally invasive resection. Video-assisted thoracoscopic surgery (VATS), being the gold standard for diagnosis and treatment, faces significant challenges in localizing nodules <1 or >1.5 cm from the pleural surface, particularly ground-glass nodules, due to limited tactile feedback. This review systematically evaluates advancements in preoperative and intraoperative localization techniques, focusing on four domains: computed tomography (CT)-guided methods (e.g., liquid agents like indocyanine green, metallic markers, and robotic-assisted puncture), bronchoscopy-guided approaches [electromagnetic navigation, radial endobronchial ultrasound (EBUS), and robotic bronchoscopy], three-dimensional (3D) printing navigation, and emerging technologies (augmented reality and real-time noninvasive systems). Despite progress, limitations persist: CT-guided methods face complications like pneumothorax (20%) and operator dependency, while bronchoscopic techniques exhibit lower diagnostic yields (18-60% vs. 70-90% for percutaneous biopsy). Innovations such as four-hook anchor devices and hybrid robotic systems demonstrated promise in improving localization accuracy and patient outcomes in reducing learning curves and improving precision. Future directions emphasize integrating artificial intelligence with multi-modal platforms to optimize accuracy and accessibility. This synthesis underscores the imperative for tailored strategies in nodule management, balancing efficacy, safety, and technological feasibility.

BACKGROUND:Although the relationship between environmental pollutants and respiratory health has received widespread attention, no studies have explored the association between volatile organic compounds (VOCs) and preserved ratio impaired spirometry (PRISm). The Systemic Inflammation Index (SII) is widely recognized as a reliable surrogate marker of systemic inflammatory status and has been identified as a mediating factor correlating various environmental pollutants to respiratory diseases. OBJECTIVE:This study aims to investigate the potential associations between individual and combined VOCs and PRISm, and further explore the potential mediating role of SII. METHODS:This study analyzed a subset of data from NHANES collected between 2007 and 2012. Multivariable logistic regression was employed to examine the association between individual VOCs and PRISm. Additionally, Weighted Quantile Sum (WQS) regression, the quantile g-computation (qgcomp) model, and Bayesian Kernel Machine Regression (BKMR) were used to assess the relationships between mixed VOCs and PRISm. We trained ten machine learning models to identify PRISm and assessed the relative importance of each feature using Shapley Additive Explanations (SHAP). RESULTS:A total of 2616 participants were included in this study. In the fully adjusted model, multivariable logistic regression results indicated that each 1 ng/mL increase in benzene was associated with a 26 % increase in the prevalence of PRISm. The WQS regression, qgcomp model, and BKMR all suggested that a mixture of VOCs was associated with PRISm. Exploratory pathway analysis indicated that inflammation accounted for 7.69 % of the observed effect (Indirect Effect [IE]: Coefficient = 0.002, 95 % CI: 0.001-0.003, p-value = 0.006; Direct Effect [DE]: Coefficient = 0.024, 95 % CI: 0.017-0.031, p-value < 0.001). Additionally, the LightGBM model exhibited the highest predictive performance, with an AUC of 0.839. SHAP analysis identified race, body mass index (BMI), and 1,4-dichlorobenzene as the three most important factors. CONCLUSION:Our findings demonstrate the correlations between VOCs and PRISm, with inflammation potentially mediating the relationship. The machine learning results highlight the potential of combining VOCs with demographic characteristics to improve PRISm identification, thereby supporting the development of prevention and intervention strategies.

Early and accurate prediction of pathological response in non-small cell lung cancer (NSCLC) receiving neoadjuvant immune(chemo)immunotherapy is essential for guiding treatment strategies. This meta-analysis aims to assess the predictive performance of PET/CT-derived metabolic parameters for identifying major pathological response (MPR) and pathological complete response (pCR). The PubMed, Web of Science, Embase, and Cochrane Library were searched for related studies from inception to January 2025. Studies assessed evaluated predefined 18F-FDG PET/CT parameter (PERCIST, PERCIST-max, ΔSUVmax, ΔSULpeak, ΔSULmax, ΔMTV, ΔTLG, pre/post-treatment SUVmax, post-treatment SULmax/SULpeak) and reported pathological outcomes were included. Methodological quality was evaluated using QUADAS-2. Sensitivity, specificity and heterogeneity were analyzed via HSROC models. Publication bias was assessed via Deeks’ test. Sixteen studies met inclusion criteria. For MPR, PERCIST showed excellent sensitivity (1.00) but limited specificity (0.67), while PERCIST-max achieved both high sensitivity (0.92) and superior specificity (0.94). ΔSUVmax demonstrated balanced accuracy (sensitivity: 0.92; specificity: 0.77; AUC: 0.94), and ΔSULpeak also yielded high diagnostic performance (sensitivity: 0.94; specificity: 0.91). Volumetric metrics such as ΔMTV and ΔTLG showed variable results, with ΔMTV performing moderately (sensitivity: 0.77; specificity: 0.83), and ΔTLG exhibiting lower sensitivity (0.63) despite good specificity (0.81). For pCR, ΔSUVmax retained high sensitivity (0.94) but showed reduced specificity (0.57; AUC: 0.86). SUVmax-Pre showed moderate accuracy (sensitivity: 0.78; specificity: 0.81). Subgroup analyses indicated that the diagnostic performance of ΔSUVmax varied depending on treatment regimen. The study support ΔSUVmax as a reliable indicator of MPR, although its specificity for pCR remains limited. PERCIST’s high sensitivity aids in excluding non-responders. However, limited study numbers for parameters like ΔSULpeak, SULpeak-Post, and ΔSULmax reduce generalizability, necessitating larger validation studies to confirm their diagnostic utility.

BACKGROUND:Sub-lobar resection is a well-established surgical approach for solitary pulmonary ground-glass opacity (GGO) lesions. However, conventional CT-guided percutaneous localization is associated with complications such as pneumothorax, hemothorax, and patient discomfort. To address these concerns, a novel real-time, non-invasive localization technique was developed. This study aimed to evaluate the effectiveness and safety of this innovative method for localizing small pulmonary nodules during sub-lobar resection. METHODS:A non-inferiority randomized clinical trial was conducted at the First Affiliated Hospital of Guangzhou Medical University from July 2022 to July 2023. Patients were randomized 1:1 to either non-invasive or CT-guided localization. The primary endpoint was the successful resection rate. Secondary endpoints included margin distance, surgical plan changes, conversion rate, intraoperative blood loss, chest tube placement duration, postoperative hospital stay, localization-related and postoperative complications, and 30-day postoperative mortality. RESULTS:Of the 440 randomized patients, 430 underwent surgery. The successful resection rate was comparable between the non-invasive and CT-guided groups (98.1 % vs. 98.6 %; P = 0.703). No significant differences were observed in margin distance, intraoperative blood loss, chest tube duration, postoperative hospital stay, or postoperative complication rates. Localization-related complications were significantly higher in the CT-guided group, including misplacement (9.8 %), puncture site pain (55.8 %), pneumothorax (42.3 %), and minor hemorrhage (30.2 %). No localization-related complications occurred in the non-invasive group. CONCLUSIONS:The novel non-invasive localization technique demonstrated comparable effectiveness to CT-guided localization for sub-lobar resection, with significantly fewer localization-related complications, offering a safer alternative for managing small pulmonary nodules.

Background Accurate preoperative prediction of major pathological response or pathological complete response after neoadjuvant chemo-immunotherapy remains a critical unmet need in resectable non-small-cell lung cancer (NSCLC). Conventional size-based imaging criteria offer limited reliability, while biopsy confirmation is available only post-surgery.Methods We retrospectively assembled 509 consecutive NSCLC cases from four Chinese thoracic-oncology centers (March 2018 to March 2023) and prospectively enrolled 50 additional patients. Three 3-dimensional convolutional neural networks (pre-treatment CT, pre-surgical CT, dual-phase CT) were developed; the best-performing dual-phase model (NeoPred) optionally integrated clinical variables. Model performance was measured by area under the receiver-operating-characteristic curve (AUC) and compared with nine board-certified radiologists.Results In an external validation set (n=59), NeoPred achieved an AUC of 0.772 (95% CI: 0.650 to 0.895), sensitivity 0.591, specificity 0.733, and accuracy 0.627; incorporating clinical data increased the AUC to 0.787. In a prospective cohort (n=50), NeoPred reached an AUC of 0.760 (95% CI: 0.628 to 0.891), surpassing the experts’ mean AUC of 0.720 (95% CI: 0.574 to 0.865). Model assistance raised the pooled expert AUC to 0.829 (95% CI: 0.707 to 0.951) and accuracy to 0.820. Marked performance persisted within radiological stable-disease subgroups (external AUC 0.742, 95% CI: 0.468 to 1.000; prospective AUC 0.833, 95% CI: 0.497 to 1.000).Conclusions Combining dual-phase CT and clinical variables, NeoPred reliably and non-invasively predicts pathological response to neoadjuvant chemo-immunotherapy in NSCLC, outperforms unaided expert assessment, and significantly enhances radiologist performance. Further multinational trials are needed to confirm generalizability and support surgical decision-making.

BACKGROUND:Preserved ratio impaired spirometry (PRISm) has been identified as a potential precursor to chronic obstructive pulmonary disease (COPD) and demonstrates a significant correlation with unfavorable clinical outcomes. Modification of PRISm-related risk factors is a higher priority in public health than treating PRISm itself. Dietary fatty acids (FAs) affect human health through a variety of physiological pathways. However, no prior research has investigated the associations of FAs and their subclasses with PRISm, particularly the combined effects of different types of FAs. METHODS:Data analysis was conducted on 8,836 individuals drawn from the NHANES dataset spanning the years 2007 to 2012. Logistic regression and smooth curve fitting were first used to assess relationships of individual FA intake with PRISm. Multiple comparisons were adjusted using the Benjamini-Hochberg (BH) correction. Threshold effect analysis was conducted to explore potential nonlinear associations. Subsequently, innovative implementation of the principal component analysis (PCA), Weighted Quantile Sum (WQS) regression, and Bayesian Kernel Machine Regression (BKMR) approaches were employed to assess the joint impact of the various intake of FAs, as well as total saturated, monounsaturated, and polyunsaturated FAs on PRISm. To facilitate the prediction of PRISm, six distinct machine learning algorithms were constructed, followed by the application of SHAP analysis to elucidate the contribution of individual predictors. For improved clinical utility, the most effective model was further implemented as an online tool. RESULTS:The weighted prevalence of PRISm observed in this study was 8.81%. The results from the single-exposure models demonstrated that most FAs were negatively associated with PRISm, and these associations remained significant after BH correction. In all three models, saturated FAs revealed impressive protective associations with PRISm. LightGBM was identified as the most effective machine learning model. Among all variables, race was the most influential factor and butyric acid (SFA 4:0) was identified as the most critical FA subclass. CONCLUSIONS:Adequate dietary intake of FAs may reduce the prevalence of PRISm. Furthermore, an interactive Web-based application enables healthcare professionals to estimate individuals' odds of having PRISm and to design personalized dietary interventions based on their specific needs.

BACKGROUND:In recent years, robots specifically designed to assist localization have been developed. Nevertheless, limited studies have systematically investigated the integration of these systems into routine clinical practice. METHODS:This prospective, single-center, non-inferiority clinical study was conducted on patients with isolated lung nodules measuring less than 20 mm in diameter between June 2024 and December 2024. The primary outcome was the localization success rate, while secondary outcomes included procedural duration, the number of CT scans required for localization, total dose-length product (DLP), first-pass success rate, localization success rate within a single needle adjustment, and the complication rate. The current study is registered with ClinicalTrials.gov. RESULTS:A total of 100 patients successfully underwent CT-guided manual needle localization or robotic-assisted needle localization, and all subsequently underwent resection of pulmonary nodules via video-assisted thoracoscopic surgery (VATS). The operative times were similar between the two groups. However, it is worth noting that the robotic-assisted navigation group demonstrated smaller deviation (median [IQR], 8.00 [6.00, 9.00] vs. 5.72 [3.07, 7.06]; p < 0.001), fewer CT scans required for localization (median [IQR], 1.00 [1.00, 2.00] vs. 1.00 [1.00, 1.00], p < 0.001), higher first-pass success rate (60% vs. 100%, p < 0.001), higher localization success rate within one needle adjustment (80% vs. 100%, p = 0.001), and lower total DLP (median [IQR], 413.50 [332.50, 496.00] vs. 248.63 [198.07, 276.47] mGy*cm; p < 0.001) compared with traditional manual localization group. Additionally, no significant differences were observed between the two groups in terms of reported complications. CONCLUSION:The robotic-assisted navigation system demonstrated efficacy comparable to that of manual CT-guided percutaneous needle localization and may represent a novel alternative for isolated lung nodule localization.

Background Minimally invasive thoracic surgery has improved lung cancer outcomes but requires enhanced postoperative care. Traditionally, the episodic care model has limited timely and multidimensional monitoring of patients. Recent technological advances in multimodal digital devices, including wearable devices and electronic patient-reported outcomes (ePROs), offer a promising solution to these challenges. However, current studies focus on only a few parameters and limited application in thoracic surgery. Objective This study aims to propose a self-controlled study to evaluate the feasibility and reliability of multimodal digital devices, including wearables and ePROs, for continuous perioperative monitoring to enhance recovery after thoracic surgery. Methods We included 288 patients with non–small cell lung cancer from the Guangzhou Medical University cohort, which includes 2757 participants with various lung diseases. Digital data were collected during hospitalization using a commercial smartwatch combined with an ePROs questionnaire, while clinical data were obtained from electronic health records (EHRs). Agreement between the digital device and EHR was evaluated via Bland-Altman analysis. Time-series data were normalized for continuous outlier monitoring, and threshold analysis of ePROs scores were used to explore associations across different modules. Results Throughout hospitalization, digital devices provided a subjective overview of the patients’ recovery trajectories. Results of Bland-Altman analysis demonstrated a high level of agreement between the digital device and the EHR. For body temperature, the analysis revealed a minimal bias of 0.02 °C (95% CI –0.01 °C to 0.05 °C), the agreement for heart rate showed a bias of 0.26 beats per minute (bpm; 95% CI –0.49 bpm to 1.01 bpm), and the bias for oxygen saturation was –0.06% (95% CI –0.27% to 0.15%), indicating close alignment between the 2 measurement methods. Meanwhile, wearable devices demonstrate significant potential in outlier detection compared to the episodic care model, offering accurate and sensitive monitoring of outliers between traditional measurement intervals. Using a thresholding method, we found that wearable metrics were correlated with the severity of ePROs. Conclusions These findings highlight the reliability and clinical potential of digital device–based multimodal systems within the enhanced recovery after surgery framework, offering a novel approach for continuous perioperative monitoring.

Background:Lung cancer (LC) is a growing global health concern, characterized by a persistent static 5-year survival rate and a worrisome increase in LC-related deaths. Despite substantial research, the connection between gut microbiota and LC remains a topic of ongoing debate. Conventional observational studies are susceptible to potential confounders and inverse causation. This study aims to investigate the potential causal association between gut microbiota and LC by using Mendelian randomization (MR). Methods:There were 5,717,754 gut microbiota-related single-nucleotide polymorphisms (SNPs) identified from the MiBioGen consortium (18,340 participants from 24 cohorts) used as instrumental variables in our study. Gut microbiota composition was measured using 16S rRNA sequencing, and association estimates for 211 bacterial taxa were obtained after adjusting for age, gender, technical variables, and genetic principal components. Genetic statistics related to LC were obtained from the Integrative Epidemiology Unit (IEU) database, involving 11,348 cases and 15,861 controls. LC cases were diagnosed based on histopathological confirmation, which is the gold standard for LC diagnosis. The inverse variance-weighted method was applied to estimate the causation between gut microbiota composition and LC. Colocalization analysis was also performed within a 500 kb window of identified SNPs, with posterior probability (PP)4/(PP3 + PP4) >0.8 confirming colocalization signals. Results:There were 27,209 eligible studies involving 11,348 patients included. Inverse-variance weighted (IVW) analysis revealed significant associations for one taxonomic order, two families, and seven genera within the gut microbiota composition. Specifically, we observed significant associations with order Bifidobacteriales [odds ratio (OR) =0.81, 95% confidence interval (CI): 0.68-0.97, P=0.03], family Bifidobacteriaceae (OR =0.81, 95% CI: 0.69-0.97, P=0.03), family Peptococcaceae (OR =0.80, 95% CI: 0.67-0.96, P=0.02), genus Clostridium sensu stricto 1 (OR =0.75, 95% CI: 0.58-0.97, P=0.03), genus Collinsella (OR =0.78, 95% CI: 0.63-0.97, P=0.03), genus Coprococcus 3 (OR =1.37, 95% CI: 1.03-1.83, P=0.03), genus Erysipelatoclostridium (OR =1.20, 95% CI: 1.05-1.38, P=0.01), genus Holdemanella (OR =1.22, 95% CI: 1.06-1.40, P=0.01), genus Peptococcus (OR =1.15, 95% CI: 1.02-1.30, P=0.02), and genus Ruminiclostridium 6 (OR =0.83, 95% CI: 0.70-0.98, P=0.03). Notably, our reverse MR analysis did not reveal any causal influence of LC on the gut microbiota. No significant heterogeneity in instrumental variables or horizontal pleiotropy was found. Colocalization analysis highlighted key SNPs, suggesting shared genetic pathways contributing to LC. Multivariable MR (MVMR) was performed to evaluate the direct causal effects of gut microbiota on the risk of cancers. Colocalization analysis identified several key SNPs, such as rs12474093 and rs2213306, being linked to genes involved in immune modulation and cell proliferation, suggesting shared genetic pathways between gut microbiota and LC. Conclusions:These findings suggest that targeting gut microbiota-associated pathways, such as short-chain fatty acid (SCFA) production, may offer novel strategies for LC prevention and treatment. Future research should explore the clinical implications of modulating gut microbiota to improve LC outcomes, particularly in high-risk populations.

Background:Adjuvant epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) show promising outcomes in early-stage non-small cell lung cancer (NSCLC) with EGFR mutations, but accurately identifying patients who would derive the greatest benefit remains a clinical challenge. We compared the predictive performance of clinicopathological factors and the 14-gene assay to assess postoperative prognosis and predict the potential benefit of adjuvant EGFR-TKIs in stage I NSCLC. Methods:From March 2013 to February 2019, patients with completely resected stage I NSCLC [8th edition tumor-node-metastasis (TNM) classification staging] and EGFR mutation were included. The 14-gene assay, assessed through quantitative reverse transcription polymerase chain reaction (qPCR), was developed and subsequently validated across diverse international cohorts. Clinicopathological high-risk factors included any feature indicating a higher risk of recurrence based on the National Comprehensive Cancer Network (NCCN) guidelines. The primary endpoint of this study was the 5-year disease-free survival (DFS) rate. Results:Diagnostic values were evaluated in 180 stage I NSCLC patients. The 14-gene assay demonstrated superior performance compared to clinicopathological factors in predicting recurrence events. Patients with molecular high-risk, rather than clinicopathological high-risk factors, showed a more favorable response to adjuvant EGFR-TKIs. Specifically, adjuvant EGFR-TKIs benefited molecular high-risk patients, regardless of clinicopathological high-risk (DFS rate increased from 65.9% to 95.0%, P=0.02) or low-risk subgroups (80.0% to 100%, P=0.04). Patients with molecular low risk did not show any benefit from EGFR-TKIs, regardless of clinicopathological high-risk (DFS rate increased from 93.3% to 100%, P=0.37) or low-risk subgroups (97.0% to 100%, P=0.73). Conclusions:The 14-gene assay is proven to be superior to clinicopathological factors, offering valuable guidance for adjuvant EGFR-TKIs decisions in stage I NSCLC.

OBJECTIVE:Evidence from prior studies indicates that certain endocrine-disrupting chemicals (EDCs), such as phenols and phthalates, may serve as environmental risk factors for chronic obstructive pulmonary disease (COPD). However, no studies have examined the potential associations between EDCs and preserved ratio impaired spirometry (PRISm), a precursor to COPD. METHODS:Data from 1363 participants in the NHANES 2007-2012 dataset were analyzed. Multiple logistic regression was employed to investigate the associations between individual EDCs and PRISm. The mixed effects of multiple EDCs on PRISm were assessed using three mixture analysis models: weighted quantile sum (WQS) regression, quantile g-computation (Qgcomp), and Bayesian kernel machine regression (BKMR). Additionally, the mediating roles of uric acid and SII were examined. Furthermore, an innovative identification model for PRISm was developed using participants' demographic information and EDC exposure levels. RESULTS:WQS regression and Qgcomp demonstrated that each index rise in the EDC-mixture index increased the odds of PRISm by 63 % (OR=1.63, 95 % CI: 1.25-2.13, P < 0.001) and 41 % (OR=1.41, 95 % CI: 1.15-1.72, P < 0.001), and BKMR model confirmed the same positive direction. The overall mixture effect was primarily attributable to mono-isobutyl phthalate (MIBP), which also yielded the largest single-chemical odds ratio in multivariable logistic regression (OR=2.29, 95 % CI: 1.71-3.07, P < 0.001). Mediation analysis showed that SII and uric acid mediated 15.8 % and 15.6 % of the association between mixed EDCs and PRISm, respectively. The results of SHAP interpretability analysis based CatBoost model further highlighted MIBP as the most informative environmental predictor. CONCLUSION:These findings suggest that exposure to EDCs may be linked to the prevalence of PRISm. These results provide novel epidemiological evidence for PRISm.

Background:Studying the relationship between strenuous sports or other exercises (SSOE) and lung cancer risk remains underexplored. Traditional observational studies face challenges like confounders and inverse causation. However, Mendelian randomization (MR) provides a promising approach in epidemiology and genetics, using genetic variants as instrumental variables to investigate causal relationships. By leveraging MR, we have scrutinized the causal link between SSOE and lung cancer development. Methods:Twelve single-nucleotide polymorphisms (SNPs) associated with SSOE, as identified in previously published genome-wide association studies, were utilized as instrumental variables in our investigation. Summary genetic data at the individual level were obtained from relevant studies and cancer consortia. The study encompassed a total of 11,348 cases and 15,861 controls. The statistical technique of inverse variance-weighting (IVW), commonly employed in meta-analyses and MR studies, was employed to assess the causal relationship between SSOE and lung cancer risk. Results:The MR risk analysis indicated a causal relationship between SSOE and the incidence of lung cancer, with evidence of a reduced risk for overall lung cancer [odds ratio (OR) =0.129; 95% confidence interval (CI): 0.021-0.779; P=0.03], lung adenocarcinoma (OR =0.161; 95% CI: 0.012-2.102; P=0.16) and squamous cell lung cancer (OR =0.045; 95% CI: 0.003-0.677; P=0.03). The combined OR for lung cancer from SSOE (controlling for waist circumference and smoking status) was 0.054 (95% CI: 0.010-0.302, P<0.001). Conclusions:Our MR analysis findings indicate a potential correlation between SSOE and a protective effect against lung cancer development. Further investigation is imperative to uncover the precise mechanistic link between them.

Objectives: Mediastinal neoplasms are typical but uncommon thoracic diseases with increasing incidence and unfavorable prognoses. A comprehensive understanding of their spatiotemporal distribution is essential for accurate diagnosis and timely treatment. However, previous studies are limited in scale and data coverage. Therefore, this study aims to elucidate the distribution of mediastinal lesions, offering valuable insights into this disease. Materials and methods: This multi -center, hospital -based observational study included 20 nationwide institutions. A retrospective search of electronic medical records from January 1st, 2009, to December 31st, 2020, was conducted, collecting sociodemographic data, computed tomography images, and pathologic diagnoses. Analysis focused on age, sex, time, location, and geographical region. Comparative assessments were made with global data from a multi -center database. Results: Among 7,765 cases, thymomas (30.7%), benign mediastinal cysts (23.4%), and neurogenic tumors (10.0%) were predominant. Distribution varied across mediastinal compartments, with thymomas (39.6%), benign cysts (28.1%), and neurogenic tumors (51.9%) most prevalent in the prevascular, visceral, and paravertebral mediastinum, respectively. Age-specific variations were notable, with germ cell tumors prominent in patients under 18 and aged 18 -29, while thymomas were more common in patients over 30. The composition of mediastinal lesions across different regions of China remained relatively consistent, but it differs from that of the global population. Conclusion: This study revealed significant heterogeneity in the spatiotemporal distribution of mediastinal neoplasms. These findings provide useful demographic data when considering the differential diagnosis of mediastinal lesions, and would be beneficial for tailoring disease prevention and control strategies.

Background: Several studies have explored the potential relationship between fruit consumption and non-small cell lung cancer (NSCLC). However, the impact of dried fruit on NSCLC risk remains unclear. Additionally, the presence of confounding variables in these observational investigations could not be avoided. Therefore, the aim of this article was to explore the potential relationship between fruits intake and NSCLC.Methods: We extracted fruit intake data from the UK Biobank and utilized a genome-wide association study (GWAS) encompassing 218,792 individuals for NSCLC data. We employed a two-sample Mendelian randomization (MR) analysis to investigate the potential causal associations between fruit intake and the risk of NSCLC. The major method of analysis was the inverse variance weighted (IVW). Furthermore, we conducted sensitivity analyses to corroborate the robustness of our findings.Results: The result of our study showed convincing evidence suggesting that dried fruit intake is effective in reducing the risk of NSCLC. Specifically, the odds ratios (ORs) for NSCLC exhibited a noteworthy reduction at 0.32 [95% confidence interval (CI): 0.15, 0.67; P=0.003] with respect to dried fruit intake. Conclusions: Our study underscores a significant correlation between dried fruit consumption and reduced NSCLC risk. In contrast, the association with fresh fruit intake did not reach statistical significance. To substantiate and validate these findings, further prospective randomized controlled trials (RCTs) are warranted in the future

BackgroundLung cancer (LC), characterized by high incidence and mortality rates, presents a significant challenge in oncology. Despite advancements in treatments, early detection remains crucial for improving patient outcomes. The accuracy of screening for LC by detecting volatile organic compounds (VOCs) in exhaled breath remains to be determined.MethodsOur systematic review, following PRISMA guidelines and analyzing data from 25 studies up to October 1, 2023, evaluates the effectiveness of different techniques in detecting VOCs. We registered the review protocol with PROSPERO and performed a systematic search in PubMed, EMBASE and Web of Science. Reviewers screened the studies' titles/abstracts and full texts, and used QUADAS-2 tool for quality assessment. Then performed meta-analysis by adopting a bivariate model for sensitivity and specificity.ResultsThis study explores the potential of VOCs in exhaled breath as biomarkers for LC screening, offering a non-invasive alternative to traditional methods. In all studies, exhaled VOCs discriminated LC from controls. The meta-analysis indicates an integrated sensitivity and specificity of 85% and 86%, respectively, with an AUC of 0.93 for VOC detection. We also conducted a systematic analysis of the source of the substance with the highest frequency of occurrence in the tested compounds. Despite the promising results, variability in study quality and methodological challenges highlight the need for further research.ConclusionThis review emphasizes the potential of VOC analysis as a cost-effective, non-invasive screening tool for early LC detection, which could significantly improve patient management and survival rates.