A retrospective analysis was conducted on electronic medical record data of lung cancer patients at Shanghai Chest Hospital from March 2025 to December 2025. Patients were divided into an MDT group and a conventional care group based on whether they were admitted to the integrated oncology ward. Statistical analyses were performed using the Mann-Whitney U test, trend chi-square test, and chi-square test. Subgroup analyses were conducted according to tumor stage (Ⅰ-Ⅳ) and relative weight (RW) risk stratification. Gamma regression and negative binomial regression models with robust standard errors clustered by patient ID were used to analyze the effects of MDT on cost per hospitalization and hospitalization frequency during the observation period. To adjust for differences in observation time, the logarithm of observation days was used as an offset in the negative binomial regression, and calculate the incidence rate ratio. Heterogeneity across tumor stage and RW subgroups was assessed using interaction models. To enhance the robustness of causal inference, propensity score matching (PSM) and inverse probability of treatment weighting (IPTW) analyses were performed as sensitivity validations.

A total of 4, 758 patients with primary lung cancer were included, comprising 365 (7.7%) in the MDT group and 4, 393 (92.3%) in the conventional care group. After adjusting for confounding factors, the MDT model significantly reduced hospitalization frequency during the observation period by 48.8% (IRR=0.512, 95% CI: 0.463-0.567, P < 0.001). After further adjusting for observation days, MDT still significantly reduced hospitalization frequency (IRR=0.834, 95% CI: 0.698-0.997, P=0.046), with the effect direction consistent with the main analysis. No statistically significant difference was observed in cost per hospitalization (IRR=0.942, 95% CI: 0.865-1.027, P=0.178). Heterogeneity analysis revealed that the cost effect varied by tumor stage: cost increased in stage Ⅰ patients (IRR=2.002), decreased in stage Ⅱ(IRR=0.705) and stage Ⅳ (IRR=0.743) patients, and showed no change in stage Ⅲ patients. No significant cost effects were observed across RW subgroups. The reduction in hospitalization frequency was significant across all tumor stage and RW subgroups (IRR range: 0.450-0.680), with no significant interactions (all P > 0.05). Sensitivity analyses confirmed the robustness of the hospitalization frequency effect (PSM: IRR=0.581; IPTW: IRR=0.520). The PSM analysis showed a significant reduction in cost per hospitalization in the MDT group (IRR=0.878, 95% CI: 0.778-0.992, P=0.036), while the IPTW analysis was consistent with the main analysis and showed no statistical significance (IRR=0.943, 95% CI: 0.862-1.032, P=0.218).

The MDT model for lung cancer significantly reduces hospitalization frequency; however, its effect on cost per hospitalization is population-selective, with increased costs in early-stage (stage Ⅰ) patients and decreased costs in late-stage (stages Ⅱ and Ⅳ) patients. The implementation of the MDT model should adopt precise patient stratification management, prioritizing the optimal patient population to achieve the optimal allocation of medical resources.

This was a two-center randomized controlled trial enrolling patients undergoing thoracoscopic pulmonary resection. After enrollment, patients were randomly divided into an experimental group and a control group. The experimental group received nebulized ICG and fluorescence imaging in addition to the conventional water immersion test, while the control group underwent the water immersion test alone. Intraoperative air leak detection and postoperative air leak incidence were compared between the two groups. Multivariable logistic regression analysis was performed to assess the association between ICG nebulization intervention and postoperative air leaks after adjusting for confounding factors including age, sex, smoking history, history of respiratory disease, surgical procedure, and study center.

A total of 181 patients were enrolled(90 in the experimental group, 91 in the control group). The experimental group showed significantly higher intraoperative air leak detection rate(37.8% vs. 19.8%, P=0.003) and median number of detected air leaks [0(0, 1) vs. 0(0, 0), P=0.004] compared with the control group. The proportion of patients developing postoperative air leaks was significantly lower in the experimental group than in the control group(25.6% vs. 50.5%, P < 0.001), and the median chest tube duration was significantly shorter [3(2, 4) vs. 4(3, 5) days, P=0.008]. Multivariable logistic regression analysis showed that ICG nebulization intervention was an independent protective factor against postoperative air leaks(OR=0.34, 95% CI: 0.17-0.68, P=0.002). The mean time for ICG nebulization was 37 seconds, and no ICG-related adverse events occurred in any patient during the intraoperative period or within one month postoperatively.

Nebulized ICG combined with near-infrared fluorescence imaging significantly improves intraoperative detection of air leaks, reduces the incidence of postoperative air leaks, and shortens chest tube duration. This technique is convenient, safe, and holds important clinical value for application.

This was a retrospective diagnostic study consisting of two parts. The first part involved molecular biomarker screening. Elderly patients with early-stage lung cancer (early lung cancer group), patients with benign pulmonary nodules (benign nodule group), and contemporaneous healthy individuals undergoing physical examinations (healthy control group) were enrolled from Peking University People's Hospital between November 2023 and November 2024. In addition, early-stage lung cancer patients and healthy controls meeting the inclusion criteria from a previous study of our research group were included as an independent validation cohort. Plasma samples were collected from all subjects, and untargeted lipidomics analysis was performed using high-performance liquid chromatography-mass spectrometry. Principal component analysis and orthogonal partial least squares discriminant analysis were used to evaluate metabolic differences between groups. L1-regularized support vector machine combined with incremental feature selection was employed to screen diagnostic biomarkers for early-stage lung cancer. Model performance was assessed using receiver operating characteristic curves, calibration curves, Brier scores, and decision curve analysis. The second part involved functional validation of the molecular biomarkers using the human lung adenocarcinoma cell line A549, with palmitoylcarnitine (CAR 16∶0) selected as a representative biomarker for functional validation via CCK-8 and cell scratch assays.

A total of 36 patients in the early lung cancer group, 35 patients in the benign nodule group, and 41 healthy controls were enrolled, along with an independent validation cohort of 110 individuals (59 patients with early-stage lung cancer and 51 healthy controls). The principal component analysis results demonstrated that quality control samples were tightly aggregated at the centroid of all samples, reflecting robust instrument performance and dependable data quality.Orthogonal partial least squares discriminant analysis revealed significant metabolic differences between the early lung cancer group and the control group (benign nodule group + healthy control group) (R²X=0.406, R²Y=0.529, Q²Y=0.44). L1-regularized support vector machine identified five carnitine-related lipids-palmitoleoylcarnitine(CAR 16∶1), palmitoylcarnitine, α-linolenoylcarnitine(CAR 18∶3), linoleoylcarnitine(CAR 18∶2), and oleoylcarnitine(CAR 18∶1)-as diagnostic biomarkers for early-stage lung cancer, all with stability values > 98%. In the screening scenario (early lung cancer group vs. benign nodule group + healthy control group), the model based on these five biomarkers achieved an area under the curve(AUC) of 0.895 (95% CI: 0.700-1.000) for diagnosing early-stage lung cancer, with a sensitivity of 98.4%, specificity of 63.9%, and accuracy of 75.0%. For differentiating early-stage lung cancer from benign pulmonary nodules, the model yielded an AUC of 0.877(95% CI: 0.797-0.965), sensitivity of 86.1%, and specificity of 80.0%. For differentiating early-stage lung cancer from healthy controls, the model yielded an AUC of 0.929(95% CI: 0.877-0.988), sensitivity of 94.4%, and specificity of 85.4%. Calibration and decision curve analyses demonstrated good model calibration and overall net benefit for patients with early-stage lung cancer. In the independent validation cohort, the model achieved an AUC of 0.874(95% CI: 0.781-0.940) for diagnosing early-stage lung cancer, with a sensitivity of 86.4%, specificity of 82.4%, and accuracy of 84.5%. In vitro experiments showed that palmitoylcarnitine inhibited the proliferation and migration of A549 cells, with a half-maximal inhibitory concentration of 57.12 μmol/L.

The five plasma carnitine-related lipids screened based on untargeted lipidomics and machine learning may serve as potential molecular biomarkers for the diagnosis of early-stage lung cancer in elderly patients. The high-sensitivity characteristic of the model makes it particularly suitable for screening scenarios in early-stage lung cancer.

This study comprised three parts. Part 1 was a cross-sectional study that consecutively enrolled patients with imaging-confirmed pulmonary nodules at the First Hospital of China Medical University from November 2024 to December 2025. Nine sleep domains were constructed using items from the Pittsburgh sleep quality index (PSQI), with domain severity coded on a 0-6 scale according to the frequency of occurrence. Benign or malignant status of pulmonary nodules was determined based on pathological results or clinical follow-up. Multivariable Logistic regression models with progressive adjustment were constructed. Stratified, interaction, and dose-response analyses (including categorical grouping and restricted cubic splines) were performed focusing on the insomnia symptom domain to explore the association between sleep-related phenotypes and the risk of malignant pulmonary nodules. Part 2 was a prospective cohort study using the China Health and Retirement Longitudinal Study (CHARLS) to investigate the association between sleep duration and incident lung cancer risk in the general population. Part 3 comprised genetic causality analyses, including two-sample Mendelian randomization (MR) and linkage disequilibrium score regression (LDSC), using data from the OpenGWAS database, to assess whether directionally consistent genetic association signals exist between sleep-related phenotypes and lung cancer risk.

In the cross-sectional study, a total of 800 patients with pulmonary nodules were included, of whom 288 (36.0%) were in the malignant group. In the continuous-variable main model fully adjusted for baseline confounders, all nine sleep domains, imaging findings, and depression and anxiety status, the severity of the insomnia symptom domain showed a positive association signal with the risk of malignant pulmonary nodules (fully adjusted model: per 1-point increase, OR=1.147, 95% CI: 1.034-1.272, P=0.010). However, categorical dose-response analysis and restricted cubic spline analysis revealed no clear monotonic trend for this association. Stratified analyses showed that the direction of the association between insomnia symptom domain severity and malignant nodule risk was generally consistent across subgroups. In interaction analyses, the interaction term between insomnia symptom domain severity and smoking status reached nominal statistical significance, suggesting potential effect heterogeneity. The prospective cohort study included 35 159 participants aged ≥45 years, with a total follow-up of approximately 8.0×10⁴ person-years, during which 96 patients developed incident lung cancer. Using a sleep duration of 6-8 hours as the reference group, the short sleep duration group (< 6 hours) showed a trend toward increased lung cancer risk (HR=1.45, 95% CI: 0.55-3.83, P=0.452), but this did not reach statistical significance. The long sleep duration group (> 8 hours) did not show an increased risk of lung cancer (HR=0.82, 95% CI: 0.50-1.33, P=0.414). In the genetic causality analyses, the primary inverse-variance weighted MR analysis showed that genetically predicted tendency to insomnia/difficulty falling asleep(OR=1.76, 95% CI: 1.00-3.10, P=0.049) and longer sleep duration were both associated with an increased risk of lung cancer (OR=1.61, 95% CI: 1.02-2.56, P=0.042). However, multiple alternative methods did not provide consistent evidence to support these findings. LDSC analysis revealed a stable positive genetic correlation between insomnia/difficulty falling asleep and lung cancer risk.

In patients with pulmonary nodules, an association signal exists between insomnia-related symptoms and the risk of malignancy, but the dose-response relationship remains unclear. The CHARLS cohort and genetic analyses provide supplementary directional clues for the above associations, albeit with limited statistical strength and result consistency. Definitive conclusions regarding the association between sleep phenotypes and the risk of malignant pulmonary nodules require further validation in prospective studies.

We systematically searched PubMed, Embase, the Cochrane Library, Web of Science, ClinicalTrials.gov, and major international conference abstracts, and included clinical-trial cohorts enrolling patients with advanced or metastatic KRAS G12C-mutant NSCLC who were G12Ci-naïve and received recommended-dose G12Ci monotherapy. Random-effects models were used to pool objective response rate(ORR), disease control rate(DCR), median progression-free survival(mPFS), median overall survival(mOS), and the incidence of any-grade and grade ≥3 treatment-related adverse events(TRAEs). For biomarker stratification, pooled odds ratios(OR) were calculated to assess associations between co-mutations and programmed death-ligand 1 expression and ORR.

The single-arm meta-analysis included 11 independent study cohorts. The pooled ORR using a random-effects model was 44%(95% CI: 38%-49%) and the pooled DCR was 86%(95% CI: 82%-88%). The pooled mPFS was 7.70 months(95% CI: 5.82-10.20) and the pooled mOS was 12.63 months(95% CI: 10.07-15.83). For safety, the pooled incidence of any-grade TRAEs was 92%(95% CI: 86%-96%), and grade ≥3 TRAEs was 39%(95% CI: 33%-45%). The toxicity profile was dominated by hepatobiliary laboratory abnormalities, renal dysfunction/proteinuria, and gastrointestinal events. Exploratory stratified analyses suggested that KEAP1 co-mutation was significantly associated with a lower ORR(OR=0.37, 95% CI: 0.21-0.65); no significant stratification effect was observed for TP53 co-mutation or PD-L1 expression(at 1% and 50% cutoffs); and the association between STK11 co-mutation and ORR did not reach statistical significance(OR=0.63, 95% CI: 0.37-1.08).

In previously treated patients with advanced KRAS G12C -mutant NSCLC, KRAS G12Ci monotherapy achieves stable overall response rates and disease control rates, yielding an efficacy and safety benchmark range that may facilitate clinical interpretation and cross-study comparisons. During treatment, standardized monitoring of hepatobiliary biochemistry, renal function, proteinuria, and gastrointestinal toxicities should be emphasized. Exploratory evidence indicates that KEAP1 co-mutation may represent a more actionable negative stratification signal.

This cross-sectional study integrated data from 10 consecutive survey cycles of the NHANES database between 1999 and 2018. Multivariable logistic regression models were used to evaluate the association between SIRI and lung cancer prevalence, and a trend test was performed to assess whether there was a linear trend in lung cancer prevalence with increasing SIRI levels. Restricted cubic spline (RCS) analysis was further performed to examine the association and potential nonlinear relationship between SIRI and lung cancer prevalence. Subgroup analyses and sensitivity analyses were conducted to assess the robustness of the results.

A total of 35 372 participants were included. With increasing SIRI levels, the mean age of participants showed a significant increasing trend (P < 0.001). The overall prevalence of lung cancer was 0.21%, with the highest prevalence observed in the Q4 group (0.47%), and the difference among groups was statistically significant (P < 0.001). After adjustment for confounding factors, each 1-unit increase in SIRI was associated with an odds ratio (OR) for lung cancer prevalence of 1.36 (95% CI: 1.18-1.58, P < 0.001). Using the lowest SIRI quartile (Q1) as the reference, the Q4 group had a higher lung cancer prevalence, with an OR of 3.95 (95% CI: 1.65-9.45, P=0.002). The trend test showed a significant increasing trend in lung cancer prevalence with increasing SIRI quartiles (P_trend=0.003). RCS analysis revealed a significant overall association between SIRI and lung cancer prevalence (P < 0.001), with no significant nonlinear relationship detected (P=0.425). Subgroup analyses indicated no significant interactions across different population characteristics (all P_interaction > 0.05). Sensitivity analyses showed that the association between SIRI as a continuous variable and lung cancer prevalence remained significant (OR=1.34, 95% CI: 1.20-1.51, P < 0.001).

Elevated SIRI levels are positively associated with increased lung cancer prevalence, and this association is robust. SIRI, as a simple and cost-effective inflammatory marker, has potential value in risk stratification for lung cancer prevalence.