A Method for Analyzing and Reporting Patterns of In-Field Recurrence after Definitive Concurrent Chemoradiation in Locally Advanced Non-Small Cell Lung Cancer

Predictive model of the first failure pattern in patients receiving definitive chemoradiotherapy for inoperable locally advanced non-small cell lung cancer (LA-NSCLC)

Purpose

To analyze patterns of failure in patients with LA-NSCLC who received definitive chemoradiotherapy (CRT) and to build a nomogram for predicting the failure patterns in this population of patients.

Materials and methods

Clinicopathological data of patients with LA-NSCLC who received definitive chemoradiotherapy and follow-up between 2013 and 2016 in our hospital were collected. The endpoint was the first failure after definitive chemoradiotherapy. With using elastic net regression and 5-fold nested cross-validation, the optimal model with better generalization ability was selected. Based on the selected model and corresponding features, a nomogram prediction model was built. This model was also validated by ROC curves, calibration curve and decision curve analysis (DCA).

Results

With a median follow-up of 28 months, 100 patients experienced failure. There were 46 and 54 patients who experience local failure and distant failure, respectively. Predictive model including 9 factors (smoking, pathology, location, EGFR mutation, age, tumor diameter, clinical N stage, consolidation chemotherapy and radiation dose) was finally built with the best performance. The average area under the ROC curve (AUC) with 5-fold nested cross-validation was 0.719, which was better than any factors alone. The calibration curve revealed a satisfactory consistency between the predicted distant failure rates and the actual observations. DCA showed most of the threshold probabilities in this model were with good net benefits.

Conclusion

Clinicopathological factors could collaboratively predict failure patterns in patients with LA-NSCLC who are receiving definitive chemoradiotherapy. A nomogram was built and validated based on these factors, showing a potential predictive value in clinical practice.