Correlation of Tumor Size and Survival in Patients With Stage IA Non-Small Cell Lung Cancer

Chest. 2000 Jun;117(6):1568-71. doi: 10.1378/chest.117.6.1568.


Objective: The purpose of this study was to determine the relationship between tumor size and survival in patients with stage IA non-small cell lung cancer (non-small cell lung cancer; ie, lesions < 3 cm).

Method: Five hundred ten patients with pathologic stage IA (T1N0M0) non-small cell lung cancer were identified from our tumor registry over an 18-year period (from 1981 to 1999). There were 285 men and 225 women, with a mean age of 63 years (range, 31 to 90 years). The Cox proportional model was used to examine the effect on survival. Tumor size was incorporated into the model as a linear effect and as categorical variables. The Kaplan-Meier product limit estimator was used to graphically display the relationship between the tumor size and survival.

Results: The Cox proportional hazards model did not show a statistically significant relationship between tumor size and survival (p = 0.701) as a linear effect. Tumor size was then categorized into quartiles, and again there was no statistically significant difference in survival between groups (p = 0.597). Tumor size was also categorized into deciles, and there was no statistical relationship between tumor size and survival (p = 0.674).

Conclusions: This study confirms stratifying patients with stage IA non-small cell lung cancer in the same TNM classification, given no apparent difference in survival. Unfortunately, these data caution that improved small nodule detection with screening CT may not significantly improve lung cancer mortality. The appropriate prospective randomized trial appears warranted.

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Carcinoma, Non-Small-Cell Lung / mortality*
  • Carcinoma, Non-Small-Cell Lung / pathology
  • Female
  • Humans
  • Lung / pathology
  • Lung Neoplasms / mortality*
  • Lung Neoplasms / pathology
  • Male
  • Middle Aged
  • Neoplasm Staging
  • North Carolina
  • Prognosis
  • Proportional Hazards Models
  • Registries / statistics & numerical data
  • Retrospective Studies
  • Survival Analysis