Prognostic Model for Resected Squamous Cell Lung Cancer: External Multicenter Validation and Propensity Score Analysis exploring the Impact of Adjuvant and Neoadjuvant Treatment

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Abstract

Introduction:

We developed one of the first clinicopathological prognostic nomograms for resected squamous cell lung cancer (SQLC). Herein, we validate the model in a larger multicenter cohort and we explore the impact of adjuvant and neoadjuvant treatment (ANT).

Methods:

Patients with resected SQLC from January 2002 to December 2012 in six institutions were eligible. Each patient was assigned a prognostic score based on the clinicopathological factors included in the model (age, T descriptor according to seventh edition of the TNM classification, lymph node status, and grading). Kaplan-Meier analysis for disease-free survival, cancer-specific survival (CSS), and overall survival was performed according to a three-class risk model. Harrell's C-statistics were adopted for model validation. The effect of ANT was adjusted with propensity score.

Results:

Data on 1375 patients were gathered (median age, 68 years; male sex, 86.8%; T descriptor 1 or 2 versus 3 or 4, 71.7% versus 24.9%; nodes negative versus positive, 53.4% versus 46.6%; and grading of 1 or 2 versus 3, 35.0% versus 41.1%). Data for survival analysis were available for 1097 patients. With a median follow-up of 55 months, patients at low risk had a significantly longer disease-free survival than did patients at intermediate risk (hazard ratio [HR] = 1.67, 95% confidence interval [CI]: 1.40–2.01) and patients at high risk (HR = 2.46, 95% CI: 1.90–3.19); they also had a significantly longer CSS (HR = 2.46, 95% CI: 1.80–3.36 versus HR = 4.30, 95% CI: 2.92–6.33) and overall survival (HR = 1.79, 95% CI: 1.48–2.17 versus HR = 2.33, 95% CI: 1.76–3.07). A trend in favor of ANT was observed for intermediate-risk/high-risk patients, particularly for CSS (p = 0.06 [5-year CSS 72.7% versus 60.8%]).

Conclusions:

A model based on a combination of easily available clinicopathological factors effectively stratifies patients with resected SQLC into three risk classes.

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