Risk factors and predictive model of lymph node metastasis in clinical stage IA peripheral non-small cell lung...

BACKGROUND: Accurate preoperative assessment of lymph node metastasis (LNM) is essential for determining the extent of lymphadenectomy in early-stage non-small cell lung cancer (NSCLC). Although clinical stage IA peripheral NSCLC generally shows a low risk of LNM, a significant number of cases are pathologically upstaged due to occult nodal involvement. This study aimed to identify risk factors associated with lymph node metastasis in patients with clinical stage IA peripheral NSCLC and to develop a predictive model to guide preoperative nodal evaluation and intraoperative lymph node dissection strategies. METHODS: We retrospectively reviewed 346 consecutive patients with clinical stage IA peripheral NSCLC who underwent surgical resection at Peking University First Hospital from January 2015 to September 2018. Clinical, pathological factors, serum tumor markers (CEA, SCC, CA19-9, CYFRA 21 − 1, NSE, TPA, ProGRP), and radiological characteristics were compared between the LNM and non-LNM groups. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors of LNM. A logistic regression model was constructed using independent predictors of lymph node metastasis. A nomogram was then developed based on the final model to facilitate individualized risk estimation. Model performance was evaluated using the area under the ROC curve (AUC), calibration curve, and decision curve analysis (DCA). RESULTS: Multivariate analysis identified three independent risk factors for LNM: tumor located in the middle or lower lobes (OR = 2.92, 95% CI: 1.15–7.41, p = 0.02), tumor size on CT (OR = 3.85, 95% CI: 1.67–8.87, p = 0.00), and elevated CA19-9 level (OR = 9.88, 95% CI: 1.62–60.09, p = 0.01). These factors were incorporated into a logistic regression model. The model demonstrated good calibration (Hosmer–Lemeshow test, p = 0.1), and an AUC of 0.78 (95% CI: 0.68–0.88, p < 0.00), indicating good discriminatory ability. A nomogram was constructed based on the model. Calibration plots showed good agreement between predicted and observed risks. Decision curve analysis confirmed the model’s net clinical benefit across a range of threshold probabilities. Subgroup analysis revealed that middle/lower lobe lesions (OR = 4.20, p = 0.02) and larger tumor size (OR = 4.60, p = 0.01) were also independent risk factors for mediastinal lymph node metastasis. CONCLUSION: A logistic regression–based clinical model was successfully developed to predict lymph node metastasis in patients with clinical stage IA peripheral NSCLC. The model, supported by a nomogram, calibration curve, and DCA, demonstrated good predictive accuracy and clinical utility. This model may assist thoracic surgeons in preoperative staging and decision-making for lymphadenectomy strategies.