Time trends and predictors of survival in surgically resected early-stage non-small cell lung cancer patients

J Surg Oncol. 2020 Sep;122(3):495-505. doi: 10.1002/jso.25966. Epub 2020 Apr 30.

Abstract

Background: The improvement in the management of lung cancer have the potential to improve survival in patients undergoing resection for early-stage (stage I and II) non-small cell lung cancer (NSCLC), but few studies have evaluated time trends and identified predictors of overall survival (OS).

Methods: We identified surgically resected early-stage NSCLC between 1998 and 2016. The 3-year OS (1998-2014) and 5-year OS (1998-2012) rates were calculated for each year. Joinpoint regression was used to calculate annual percentage changes (APC) and to test time trends in OS. Multivariable Cox regression was used to identify predictors of OS.

Results: There was a significant upward trend in the 3-year (1998, 56%; 2014, 83%; APC = 1.8) and 5-year (1998, 47%; 2012, 76%; APC = 3.1) OS. Older age; male sex; history of diabetes, coronary artery disease, and chronic obstructive pulmonary disease; high ASA score; smoking pack-years; high-grade tumor; pneumonectomy; thoracotomy; neoadjuvant therapy; nodal disease; and positive tumor margin were predictors of poor OS.

Conclusion: The upward time trend in OS suggests that improved staging, patient selection, and management have conferred a survival benefit in early-stage NSCLC patients. The prediction model of OS could be used to refine selection criteria for resection and improve survival outcomes.

Keywords: early-stage NSCLC; lung resection; nomogram; overall survival; prediction model; temporal trend.

MeSH terms

  • Adult
  • Age Factors
  • Aged
  • Aged, 80 and over
  • Carcinoma, Non-Small-Cell Lung / mortality*
  • Carcinoma, Non-Small-Cell Lung / pathology
  • Carcinoma, Non-Small-Cell Lung / surgery*
  • Female
  • Humans
  • Lung Neoplasms / mortality*
  • Lung Neoplasms / pathology
  • Lung Neoplasms / surgery*
  • Male
  • Middle Aged
  • Neoplasm Staging
  • Nomograms
  • Proportional Hazards Models
  • Retrospective Studies
  • Sex Factors
  • Survival Rate / trends
  • Young Adult