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Background: Identifying lymph node metastasis areas during surgery for early invasive lung adenocarcinoma remains challenging. The aim of this study was to develop a nomogram mathematical model before the end of surgery for predicting lymph node metastasis in patients with early invasive lung adenocarcinoma. Methods: In this study, we included patients with invasive lung adenocarcinoma measuring ≤ 2 cm who underwent pulmonary resection with definite pathology at Qilu Hospital of Shandong University from January 2020 to January 2022. Preoperative biomarker results, clinical features, and computed tomography characteristics were collected. The enrolled patients were randomized into a training cohort and a validation cohort in a 7:3 ratio. The training cohort was used to construct the predictive model, while the validation cohort was used to test the model independently. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors. The prediction model and nomogram were established based on the independent risk factors. Recipient operating characteristic (ROC) curves were used to assess the discrimination ability of the model. Calibration capability was assessed using the Hosmer–Lemeshow test and calibration curves. The clinical utility of the nomogram was assessed using decision curve analysis (DCA). Results: The overall incidence of lymph node metastasis was 13.23% (61/461). Six indicators were finally determined to be independently associated with lymph node metastasis. These six indicators were: age (P < 0.001), serum amyloid (SA) (P = 0.008); carcinoma antigen 125 (CA125) (P = 0. 042); mucus composition (P = 0.003); novel aspartic proteinase of the pepsin family A (Napsin A) (P = 0.007); and cytokeratin 5/6 (CK5/6) (P = 0.042). The area under the ROC curve (AUC) was 0.843 (95% CI: 0.779–0.908) in the training cohort and 0.838 (95% CI: 0.748–0.927) in the validation cohort. the P-value of the Hosmer–Lemeshow test was 0.0613 in the training cohort and 0.8628 in the validation cohort. the bias of the training cohort corrected C-index was 0.8444 and the bias-corrected C-index for the validation cohort was 0.8375. demonstrating that the prediction model has good discriminative power and good calibration. Conclusions: The column line graphs created showed excellent discrimination and calibration to predict lymph node status in patients with ≤ 2 cm invasive lung adenocarcinoma. In addition, the predictive model has predictive potential before the end of surgery and can inform clinical decision making. [ABSTRACT FROM AUTHOR]

Background: Non-small cell lung cancer (NSCLC) is the primary reason for cancer-related deaths globally. Tertiary lymphoid structure (TLS) is an organized collection of immune cells acquired in non-physiological, non-lymphoid tissues. High expression of TLS in tumor tissues is generally associated with better prognosis. This research aimed to investigate the prognostic and clinicopathological significance of TLS in patients with NSCLC., Methods: A comprehensive literature search was conducted based on Pubmed, EMBASE, and Cochrane Library databases to identify eligible studies published up to December 8, 2023. The prognostic significance and clinicopathological value of TLS in NSCLC were evaluated by calculating the combined hazard ratios (HRs) and odds ratios (ORs) and their 95% confidence intervals (CIs). Following that, additional analyses, including subgroup analysis and sensitivity analysis, were conducted., Results: This meta-analysis evaluated the prognostic and clinicopathological significance of TLS in 10 studies involving 1,451 patients with NSCLC. The results revealed that the high levels of TLS were strongly associated with better overall survival (OS) (HR = 0.48, 95% CI: 0.35-0.66, p < 0.001), disease-free survival (DFS)/recurrence-free survival (RFS) (HR = 0.37, 95% CI: 0.24-0.54, p < 0.001), and disease-specific survival (DSS) (HR = 0.45, 95% CI: 0.30-0.68, p < 0.001) in NSCLC patients. In addition, the increased expression of TLS was closely related to the Tumor Node Metastasis (TNM) stage of tumors (OR = 0.71, 95% CI: 0.51-1.00, p < 0.05) and neutrophil-lymphocyte ratio (NLR) (OR = 0.33, 95% CI: 0.17-0.62, p < 0.001)., Conclusions: The results revealed that highly expressed TLS is closely associated with a better prognosis in NSCLC patients. TLS may serve as a novel biomarker to predict the prognosis of NSCLC patients and guide the clinical treatment decisions., (© 2024. The Author(s).)