A prediction model for N2 disease in T1 non-small cell lung cancer

Abstract

Objective: Controversy remains over the routine use of mediastinoscopy or positron emission tomography in T1 non-small cell lung cancer without lymph node enlargement on computed tomography because the risk of N2 involvement is comparatively low. We aimed to develop a prediction model for N2 disease in cT1N0 non-small cell lung cancer to aid in the decision-making process.

Methods: We reviewed the records of 530 patients with computed tomography-defined T1N0 non-small cell lung cancer who underwent surgical resection with systematic lymph node dissection. Correlations between N2 involvement and clinicopathologic parameters were assessed using univariate analysis and binary logistic regression analysis. A prediction model was built on the basis of logistic regression analysis and was internally validated using bootstrapping.

Results: The incidence of N2 disease was 16.8%. Four independent predictors were identified in multivariate logistic regression analysis and included in the prediction model: younger age at diagnosis (odds ratio, 0.974; 95% confidence interval, 0.952-0.997), larger tumor size (odds ratio, 2.769; 95% confidence interval, 1.818-4.217), central tumor location (odds ratio, 3.204; 95% confidence interval, 1.512-6.790), and invasive adenocarcinoma histology (odds ratio, 3.537; 95% confidence interval, 1.740-7.191). This model shows good calibration (Hosmer-Lemeshow test: P = .784), reasonable discrimination (area under the receiver operating characteristic curve, 0.726; 95% confidence interval, 0.669-0.784), and minimal overfitting demonstrated by bootstrapping.

Conclusions: We developed a 4-predictor model that can estimate the probability of N2 disease in computed tomography-defined T1N0 non-small cell lung cancer. This prediction model can help to determine the cost-effective use of mediastinal staging procedures.