A Prediction Model for Nodal Disease among Patients with Non-Small Cell Lung Cancer

Abstract Background We characterized the performance characteristics of guideline-recommended invasive mediastinal staging 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 invasive mediastinal staging and/or at the time of resection. The true-positive rate (TPR) was the proportion of patients with true nodal disease selected to undergo invasive mediastinal staging based on guideline recommendations, and the false-positive rate (FPR) was the proportion of patients without true nodal disease selected to undergo invasive mediastinal staging. 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 TPR and FPR 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 0.72-0.89). Exploratory analysis revealed that use of the prediction model could achieve a FPR of 44% at a TPR of 97%. Conclusions A guideline-recommended strategy for invasive mediastinal staging selects all patients with true nodal disease and a majority of patients without nodal disease for invasive mediastinal staging. 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.