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Background: We characterized the performance characteristics of guideline-recommended invasive mediastinal staging (IMS) for lung cancer and developed a prediction model for nodal disease as a potential alternative approach to staging., Methods: We conducted a prospective cohort study of adults with suspected/confirmed non-small cell lung cancer without evidence of distant metastatic disease (by computed tomography/positron emission tomography) who underwent nodal evaluation by IMS and/or at the time of resection. The true-positive rate was the proportion of patients with true nodal disease selected to undergo IMS based on guideline recommendations, and the false-positive rate was the proportion of patients without true nodal disease selected to undergo IMS. Logistic regression was used to predict nodal disease using radiographic predictors., Results: Among 123 eligible subjects, 31 (25%) had pathologically confirmed nodal disease. A guideline-recommended invasive staging strategy had a true-positive rate and false-positive rate of 100% and 65%, respectively. The prediction model fit the data well (goodness-of-fit test, p = 0.55) and had excellent discrimination (optimism corrected c-statistic, 0.78; 95% confidence interval, 0.72 to 0.89). Exploratory analysis revealed that use of the prediction model could achieve a false-positive rate of 44% and a true-positive rate of 97%., Conclusions: A guideline-recommended strategy for IMS selects all patients with true nodal disease and most patients without nodal disease for IMS. Our prediction model appears to maintain (within a margin of error) the sensitivity of a guideline-recommended invasive staging strategy and has the potential to reduce the use of invasive procedures., (Copyright © 2019 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.)

Background: One in 5 patients with completely resected early-stage non-small cell lung cancer will recur within 2 years. Risk stratification may facilitate a personalized approach to the use of adjuvant therapy and surveillance imaging. We developed a prediction model for recurrence based on five clinical variables (tumor size and grade, visceral pleural and lymphovascular invasion, and sublobar resection), and tested the hypothesis that the addition of several new molecular markers of poor long-term outcome (vascular endothelial growth factor C; microRNA precursors 486 and 30d) would enhance prediction., Methods: We performed a retrospective cohort study of patients with completely resected, node-negative non-small cell lung cancer from 2011 to 2014 (follow-up through 2016) using the Lung Cancer Biospecimen Resource Network. Cox regression was used to estimate the 2-year risk of recurrence. Our primary measure of model performance was the optimism-corrected C statistic., Results: Among 173 patients (mean tumor size, 3.6 cm; 12% sublobar resection, 32% poorly differentiated, 16% lymphovascular invasion, 26% visceral pleural invasion), the 2-year recurrence rate was 23% (95% confidence interval, 17% to 31%). A prediction model using five known risk factors for recurrence performed only slightly better than chance in predicting recurrence (optimism-corrected C statistic, 0.54; 95% confidence interval, 0.51 to 0.68). The addition of biomarkers did not improve the model's ability to predict recurrence (corrected C statistic, 0.55; 95% confidence interval, 0.52 to 0.71)., Conclusions: We were unable to predict lung cancer recurrence using a risk-prediction model based on five well-known clinical risk factors and several biomarkers. Further research should consider novel predictors of recurrence to stratify patients with completely resected early-stage non-small cell lung cancer according to their risk of recurrence., (Copyright © 2018 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.)