Preoperative prediction of lymph node metastasis in lung adenocarcinoma based on tumor size and carcinoembryonic antigen
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Background:
Lymph node metastasis (LNM) is a critical determinant of staging and treatment decisions in lung adenocarcinoma. We aimed to develop and temporally validate a simple, clinically accessible preoperative model to predict LNM.
Methods:
A consecutive cohort of 6,406 patients with resected lung adenocarcinoma was chronologically divided into a training set (n=4,484) and a validation set (n=1,922). Independent predictors were identified using multivariable logistic regression. Model performance was assessed using the area under the curve (AUC), calibration analysis, and decision curve analysis (DCA). A nomogram was constructed.
Results:
The overall prevalence of lymph node (LN) positivity was 3.6% (233/6,406), with rates of 3.4% and 4.2% in the training and validation cohorts, respectively. In the multivariable analysis, only tumor size [odds ratio (OR) 1.09, 95% confidence interval (CI): 1.07-1.11, P<0.001] and carcinoembryonic antigen (CEA; OR 1.04, 95% CI: 1.02-1.06, P<0.001) were identified as independent predictors, whereas inflammatory and nutritional indices [the neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), prognostic nutritional index (PNI), and C-reactive protein-to-albumin ratio (CAR)] were not significant. The resulting two-variable model achieved strong discrimination, with AUC values of 0.873 (95% CI: 0.844-0.899) in the training cohort and 0.848 (95% CI: 0.806-0.886) in the validation cohort. At the Youden-derived threshold, the model yielded sensitivities of 81.0% and 83.8%, specificities of 80.1% and 71.2%, and negative predictive values (NPVs) of 99.2% and 99.0% in the training and validation cohorts, respectively. Calibration plots and the DCA demonstrated good agreement between predicted probabilities and observed outcomes and supported the clinical utility of the model.
Conclusions:
A two-variable model (incorporating tumor size and CEA) provides robust preoperative estimation of LN risk and may help reduce unnecessary invasive staging procedures.
