ArticleJournal of thoracic disease2026
Preoperative prediction of lymph node metastasis in lung adenocarcinoma based on tumor size and carcinoembryonic antigen.
Article in Journal of thoracic disease, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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.
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