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.