目的：研究临床Ⅰ期非小细胞肺癌患者发生纵膈淋巴结转移的独立危险因素，建立临床预测模型，以协助医生做出合理的诊疗策略。方法：回顾性分析739例术前胸部CT诊断为临床Ⅰ期，并通过手术治疗的非小细胞肺癌患者的临床资料，随机分为建模组和验证组。采用建模组病例资料，通过多因素分析筛选出N2淋巴结转移的独立危险因素，建立数学预测模型。采用验证组病例资料对该模型进行外部验证，并与已有的两组数学预测模型进行比较。结果：多因素分析结果显示，年龄、肿瘤大小、肿瘤位置以及病理类型是N2淋巴结转移的独立危险因素，数学预测模型为：N2淋巴结转移的可能性=ex/(1+ex)，其中x=-2.983+(0.456×肿瘤直径)+(1.753×位置)+(1.787×病理类型)-(0.032×年龄)。 Hosmer-Lemeshow拟合优度检验显示，预测值和观察值间差异无统计学意义（P=0.923），受试者工作特征曲线的曲线下面积为0.748（95%CI： 0.710~0.784）。外部验证结果显示，与VA模型相比，本研究模型的曲线下面积为0.781（95%CI： 0.715~0.839），高于VA模型0.677（95%CI： 0.604~0.744）（P=0.04）。与Fudan模型相比，本研究模型的曲线下面积0.837（95%CI： 0.760~0.897），  高于Fudan模型0.766（95%CI： 0.681~0.837）（P<0.01）。 结论：本研究对术前CT检查判断为临床Ⅰ期非小细胞肺癌的患者建立了N2淋巴结转移的数学预测模型，其准确性高于现有的其他模型，通过该模型可以对是否进行进一步的纵膈淋巴结的分期检查做出更合理的临床决策。

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.