title: 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.
authors: Department of Thoracic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.
authors: Yang
authors: Y
authors: Zhang
authors: Yihua
authors: Y
authors: Sun
authors: Jiaqing
authors: J
authors: Xiang
authors: Yawei
authors: Y
authors: Zhang
authors: Hong
authors: H
authors: Hu
authors: Haiquan
authors: H
authors: Chen
mesh_terms: D000230:Adenocarcinoma / Q000175:diagnosis* / Q000000981:diagnostic imaging / Q000473:pathology / Q000601:surgery; D000077192:Adenocarcinoma of Lung; D000367:Age Factors; D000368:Aged; D002289:Carcinoma, Non-Small-Cell Lung / Q000175:diagnosis* / Q000000981:diagnostic imaging / Q000473:pathology / Q000601:surgery; D016009:Chi-Square Distribution; D003661:Decision Support Techniques; D005260:Female; D006801:Humans; D016015:Logistic Models; D008175:Lung Neoplasms / Q000175:diagnosis* / Q000000981:diagnostic imaging / Q000473:pathology / Q000601:surgery; D008197:Lymph Node Excision; D008198:Lymph Nodes / Q000473:pathology / Q000601:surgery; D008207:Lymphatic Metastasis; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D060787:Neoplasm Grading; D009361:Neoplasm Invasiveness; D009367:Neoplasm Staging; D016017:Odds Ratio; D011237:Predictive Value of Tests; D011379:Prognosis; D015203:Reproducibility of Results; D012189:Retrospective Studies; D018570:Risk Assessment; D012307:Risk Factors; D014057:Tomography, X-Ray Computed; D047368:Tumor Burden