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.
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: Farhood Farjah; Leah M Backhus; Thomas K Varghese; James P Manning; Aaron M Cheng; Michael S Mulligan; Douglas E Wood Journal: J Thorac Dis Date: 2015-04 Impact factor: 2.895
Authors: Francys C Verdial; David K Madtes; Billanna Hwang; Michael S Mulligan; Katherine Odem-Davis; Rachel Waworuntu; Douglas E Wood; Farhood Farjah Journal: Ann Thorac Surg Date: 2019-01-30 Impact factor: 4.330
Authors: Oisin J O'Connell; Francisco A Almeida; Michael J Simoff; Lonny Yarmus; Ray Lazarus; Benjamin Young; Yu Chen; Roy Semaan; Timothy M Saettele; Joseph Cicenia; Harmeet Bedi; Corrine Kliment; Liang Li; Sonali Sethi; Javier Diaz-Mendoza; David Feller-Kopman; Juhee Song; Thomas Gildea; Hans Lee; Horiana B Grosu; Michael Machuzak; Macarena Rodriguez-Vial; George A Eapen; Carlos A Jimenez; Roberto F Casal; David E Ost Journal: Am J Respir Crit Care Med Date: 2017-06-15 Impact factor: 21.405