A prediction model for N2 disease in T1 non-small cell lung cancer
A prediction model for N2 disease in T1 non-small cell lung cancer
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DOI：
10.1016/j.jtcvs.2012.06.050
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发表时间：
2012-12-01
影响因子：
6
通讯作者：
Chen, Haiquan
中科院分区：
文献类型：
--
作者：
Zhang, Yang;Sun, Yihua;Chen, Haiquan
目的：对于常规使用纵隔镜检查或正电子发射断层扫描在T1期非小细胞肺癌中无淋巴结肿大的计算机断层扫描仍然存在争议，因为N2参与的风险相对较低。我们的目的是开发一个预测模型，N2疾病的cT 1 N 0非小细胞肺癌，以帮助决策process.Methods：我们回顾了530例计算机断层扫描定义的T1 N 0非小细胞肺癌谁接受手术切除系统淋巴结清扫的记录。采用单变量分析和二元Logistic回归分析评估N2受累与临床病理参数之间的相关性。结果：N2病发病率为16.8%，N2病发病率为16.8%。在多变量逻辑回归分析中确定了四个独立的预测因素，并将其纳入预测模型：诊断时年龄较小（比值比，0.974; 95%置信区间，0.952-0.997），较大的肿瘤大小（比值比，2.769; 95%置信区间，1.818-4.217），中央肿瘤位置（比值比，3.204; 95%可信区间，1.512-6.790）和浸润性腺癌组织学（比值比，3.537; 95%可信区间，1.740-7.191）。该模型显示出良好的校准（Hosmer-Lemeshow检验：P = .784），合理区分（受试者工作特征曲线下的面积，0.726; 95%置信区间，0.669-0.784），以及通过自举证明的最小过拟合。我们开发了一个4预测模型，可以估计计算机断层扫描定义的T1 N 0非小细胞肺癌中N2疾病的概率。这个预测模型可以帮助确定纵隔分期手术的成本效益。（《胸血管外科杂志》2012; 144：1360-4）
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. (J Thorac Cardiovasc Surg 2012; 144:1360-4)