Francys C Verdial1, David K Madtes2, Billanna Hwang3, Michael S Mulligan3, Katherine Odem-Davis4, Rachel Waworuntu3, Douglas E Wood1, Farhood Farjah5. 1. Department of Surgery, University of Washington School of Medicine, Seattle, Washington. 2. Division of Pulmonary and Critical Care Medicine, University of Washington, Seattle, Washington; Division of Pulmonary and Critical Care Medicine, Fred Hutchinson Cancer Research Center, Seattle, Washington. 3. Department of Surgery, University of Washington School of Medicine, Seattle, Washington; Center for Lung Biology, University of Washington, Seattle, Washington. 4. Seattle Children's Research Institute, Seattle, Washington. 5. Department of Surgery, University of Washington School of Medicine, Seattle, Washington. Electronic address: ffarjah@uw.edu.
Abstract
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.
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.
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