A prognostic model of survival in surgically resected squamous cell carcinoma of the lung using clinical, pathologic, and biologic markers

Mod Pathol. 1997 Oct;10(10):992-1000.

Abstract

The biologic behavior of tumoral cells plays a significant role in the progression of the neoplasia, because 30 to 35% of patients with Stage I squamous cell carcinoma relapse. The present study was designed to determine whether age, pathologic parameters, DNA ploidy, and a cell proliferation index (the area of nucleolar organizer regions, AgNOR), could be used to predict survival in patients who undergo resection for limited squamous cell carcinoma of the lung. For histopathologic analysis, the parameters of histologic grading, pleural involvement, vascular invasion, and residual disease were considered. The cell proliferation index was evaluated by mitotic index, AgNOR quantification, and DNA ploidy by means of digital image analysis. Fifty-two patients (median age, 60 yr +/- 8.6 yr) were staged according to the TNM staging system. Cox univariate analysis showed that stage, residual disease, vascular invasion, histologic grading, DNA ploidy, and AgNOR were significant predictors of survival. Many of the univariate predictors of cancer death, however were eliminated when Cox multivariate models were computed. The variable that exhibited the most robust predictive value for overall survival was AgNOR. We conclude that measurement of cell proliferation might serve as a prognostic marker in squamous cell carcinoma of the lung.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Biomarkers, Tumor*
  • Carcinoma, Squamous Cell / mortality*
  • Carcinoma, Squamous Cell / pathology
  • Carcinoma, Squamous Cell / surgery
  • DNA, Neoplasm / analysis
  • Female
  • Humans
  • Lung Neoplasms / mortality*
  • Lung Neoplasms / pathology
  • Lung Neoplasms / surgery
  • Male
  • Middle Aged
  • Multivariate Analysis
  • Nucleolus Organizer Region / chemistry
  • Ploidies
  • Prognosis
  • Proportional Hazards Models
  • Silver Staining
  • Survival Rate

Substances

  • Biomarkers, Tumor
  • DNA, Neoplasm