Development and Validation of a Clinical Prediction Model for N2 Lymph Node Metastasis in Non-Small Cell Lung Cancer

Background. The true incidence of occult N2 lymph node metastasis in patients with clinical N0 non-small cell lung cancer (NSCLC) remains controversial. Estimation of the probability of N2 lymph node metastasis can assist physicians when making diagnosis and treatment decisions. Methods. We reviewed the medical records of 605 patients (group A) and 211 patients (group B) with computed tomography-defined N0 NSCLC that had an exact tumor-node-metastasis stage after surgery. Logistic regression analysis of group A's clinical characteristics was used to estimate the independent predictors of N2 lymph node metastasis. A prediction model was then built and internally validated by using cross validation and externally validated in group B. The model was also compared with 2 previously described models. Results. We identified 4 independent predictors of N2 disease: a younger age; larger tumor size; central tumor location; and adenocarcinoma or adenosquamous carcinoma pathology. The model showed good calibration (Hosmer-Lemeshow test: p = 0.96) with an area under the receiver operating characteristic curve (AUC) of 0.756 (95% confidence interval, 0.699 to 0.813). The AUC of our model was better than those of the other models when validated with independent data. Conclusions. Our prediction model estimated the pretest probability of N2 disease in computed tomography-defined N0 NSCLC and was more accurate than the existing models. Use of our model can be of assistance when making clinical decisions about invasive or expensive mediastinal staging procedures. (C) 2013 by The Society of Thoracic Surgeons