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[A clinical prediction model for N2 lymph node metastasis in clinical stage I non-small cell lung cancer].

OBJECTIVE
To estimate the probability of N2 lymph node metastasis and to assist physicians in making diagnosis and treatment decisions.


METHODS
We reviewed the medical records of 739 patients with computed tomography-defined stage I non-small cell lung cancer (NSCLC) that had an exact tumor-node-metastasis stage after surgery. A random subset of three fourths of the patients (n=554) were selected to develop the prediction model. Logistic regression analysis of the clinical characteristics was used to estimate the independent predictors of N2 lymph node metastasis. A prediction model was then built and externally validated by the remaining one fourth (n=185) patients which made up the validation data set. 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=0.923) with an area under the receiver operating characteristic curve (AUC) of 0.748 (95% confidence interval, 0.710-0.784). When validated with all the patients of group B, the AUC of our model was 0.781 (95% CI: 0.715-0.839) and the VA model was 0.677 (95% CI: 0.604-0.744) (P =0.04). When validated with T1 patients of group B, the AUC of our model was 0.837 (95% CI: 0.760-0.897) and Fudan model was 0.766 (95% CI: 0.681-0.837) (P < 0.01).


CONCLUSION
Our prediction model estimated the pretest probability of N2 disease in computed tomography-defined stage I 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.