A nomogram for predicting postoperative overall survival of patients with lung squamous cell carcinoma: A SEER-based study

Abstract

Background: Lung squamous cell carcinoma (LSCC) is a common subtype of non-small cell lung cancer. Our study aimed to construct and validate a nomogram for predicting overall survival (OS) for postoperative LSCC patients.

Methods: A total of 8,078 patients eligible for recruitment between 2010 and 2015 were selected from the Surveillance, Epidemiology, and End Results database. Study outcomes were 1-, 2- and 3-year OS. Analyses performed included univariate and multivariate Cox regression, receiver operating characteristic (ROC) curve construction, calibration plotting, decision curve analysis (DCA) and Kaplan-Meier survival plotting.

Results: Seven variables were selected to establish our predictive nomogram. Areas under the ROC curves were 0.658, 0.651 and 0.647 for the training cohort and 0.673, 0.667 and 0.658 for the validation cohort at 1-, 2- and 3-year time-points, respectively. Calibration curves confirmed satisfactory consistencies between nomogram-predicted and observed survival probabilities, while DCA confirmed significant clinical usefulness of our model. For risk stratification, patients were divided into three risk groups with significant differences in OS on Kaplan-Meier analysis (P < 0.001).

Conclusion: Here, we designed and validated a prognostic nomogram for OS in postoperative LSCC patients. Application of our model in the clinical setting may assist clinicians in evaluating patient prognosis and providing highly individualized therapy.

Keywords: end results (SEER); epidemiology; lung squamous cell carcinoma (LSCC); nomogram; prognosis; surgery; surveillance.

Figure 1

Nomograms to predict 1-, 2- and 3-year OS for postoperative LSCC patients. OS, overall survival; LSCC, lung squamous cell carcinoma.

Figure 2

ROC curves of nomogram prediction of prognoses in training and validation cohort patients. (A–D) ROC curve for 1-, 2- and 3-year timepoints in the training cohort. (E–H) ROC curve for 1-, 2- and 3-year timepoints in the validation cohort. ROC, receiver operating characteristic curve; AUC, area under the ROC curve; TP, true positive rate; FP, false positive rate.

Figure 3

Calibration curves for predicting patient OS at 1-, 2- and 3-year timepoints in training (A–C) and validation (D–F) cohorts, respectively. The 45-degree line represents an ideal match between actual (y-axis) and predicted (x-axis) survival. OS, overall survival.

Figure 4

Nomogram DCA for survival prediction of LSCC patients. (A–C) 1-, 2- and 3-year survival benefit in training cohort patients. (D–F) 1-, 2- and 3-year survival benefit in validation cohort patients. DCA, decision curve analysis; LSCC, lung squamous cell carcinoma.

Figure 5

Kaplan–Meier OS curves for risk stratification based on total nomogram scores in the training (A) and external validation (B) cohorts. OS, overall survival.