A predictive nomogram and lobe-specific patterns of lymph node metastasis in patients with cT1b-c peripheral lung adenocarcinoma

Preoperative accurate prediction of lymph node metastasis remains challenging in patients with clinical T1b-c peripheral lung adenocarcinoma. Although lobe-specific lymph node dissection (L-SLND) has been proposed as an alternative to systematic lymph node dissection (SLND), the predictive factors and patterns of lymph node metastasis in this specific patient population have not been fully elucidated. This study retrospectively analyzed 419 patients with cT1b–c peripheral lung adenocarcinoma who underwent lobectomy and SLND at Hebei General Hospital between January 2022 and June 2024. Independent risk factors for lymph node metastasis (LNM) were identified using univariate and multivariate logistic regression analyses. Patients were randomly divided into training and validation cohorts at a 7:3 ratio. A nomogram predictive model was developed and internally validated. Furthermore, the lobe-specific patterns of lymph node metastasis in cT1b–c peripheral lung adenocarcinoma were analyzed. First, a univariate logistic regression analysis was conducted to identify relevant risk factors. Variables that showed statistical significance were then incorporated into a multivariate logistic regression model for further screening. As a result, the multivariate analysis identified smoking history (P=0.026), serum carcinoembryonic antigen (CEA) level (P<0.001), nodule diameter (P=0.027), and consolidation-to-tumor ratio (CTR, P<0.001) as independent risk factors for LNM. The nomogram incorporating these factors demonstrated excellent discriminative ability in both the training and validation cohorts, with area under the curve (AUC) values of 0.904(95% CI: 0.868-0.940) and 0.866(95% CI: 0.804-0.928), respectively. Calibration curves and decision curve analysis (DCA) confirmed its clinical utility. Analysis of lobe-specific lymph node metastasis revealed distinct patterns: station 2R/4R for right upper lobe tumors, station 4R/7 for right middle lobe tumors, station 4L/5/6/7 for left upper lobe tumors, and station 7 for tumors in both lower lobes. Subgroup analyses based on CTR and nodule diameter further refined the indications for L-SLND. In conclusion, we have developed a highly accurate nomogram for preoperative prediction of LNM in cT1b-c peripheral lung adenocarcinoma. The detailed lobe-specific nodal patterns identified in this work provide a strong rationale for implementing L-SLND to minimize surgical trauma.