Challenges in Predicting Recurrence After Resection of Node-Negative Non-Small Cell Lung Cancer

Abstract

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.

Fig 1.

Diagram of cohort selection. (LCBRN = Lung Cancer Biospecimen Resource Network.)

Fig 2.

(A) Kaplan-Meier survival estimates for recurrence-free survival among patients with completely resected, node-negative non-small cell lung cancer and for five pre-specified clinical risk-factors: (B) tumor size, (C) lymphovascular invasion (LVI), (D) visceral pleural invasion (VPI), (E) tumor grade, and (F) sublobar resection.

Fig 3.

Receiver operating characteristic curves (left) and calibration plots (right) for (A) model A (visceral pleural invasion, lymphovascular invasion, log-tumor size, tumor grade, and sublobar resection and (B) model B (model A plus vascular endothelial growth factor C, micro-RNA [miR]-486, and miR-30d). The time-dependent receiver operating characteristic curve was evaluated at 2 years using nearest neighbor estimator smoothing with a span equal to 0.25 × n^−0.2, with n equal to the number of patients. The calibration plot reflects “predicted” survival probabilities at 24 months (recurrence-free survival) and corresponding “observed” survival probabilities by Kaplan-Meier (fraction recurrence-free at 24 months). The tick marks at the top of this figure indicate points observed with corresponding predicted survival. (AUC = area under the receiver operating characteristic curve; CI = confidence interval.)