Prognostic Impact of Intra-alveolar Tumor Spread in Pulmonary Adenocarcinoma

Am J Surg Pathol. 2015 Jun;39(6):793-801. doi: 10.1097/PAS.0000000000000409.


Tumor spread, in general, is the most important factor determining outcome in almost all malignant tumors. Lung tumors are unique with respect to potential routes for tumor dissemination, as apart from vascular, nodal, and distant spread of tumor cells, tumor spread through air spaces (STAS) might also occur. However, morphologic criteria for STAS and its prognostic impact have not been defined yet. We evaluated a series of 569 resected pulmonary adenocarcinomas (ADCs) for predefined morphologic criteria of limited and extensive STAS and correlated our findings with clinical, morphologic, molecular, and outcome data. Limited (21.6%) or extensive (29%) STAS was present in roughly half of all ADCs. The presence and type of STAS was tightly linked to specific growth patterns (P<0.001). STAS was much more prevalent in high-stage (P<0.001), nodal-positive (P<0.001) ADC with distant metastasis (P=0.010). STAS was associated with lower rates of EGFR (P=0.009) but higher rates of BRAF (P=0.016) mutations. Furthermore, STAS was associated with significantly reduced overall (P=0.020) and disease-free survival (P=0.004), which was growth pattern but not stage independent. We analyzed morphologic characteristics of a yet underestimated type of tumor spread of pulmonary ADC through air spaces. STAS is a novel morphologic prognosticator, which should be further validated and considered for implementation in routine diagnostic evaluation and reporting.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adenocarcinoma / mortality
  • Adenocarcinoma / pathology*
  • Adenocarcinoma of Lung
  • Adult
  • Aged
  • Biomarkers, Tumor / analysis
  • Female
  • Humans
  • Immunohistochemistry
  • Lung Neoplasms / mortality
  • Lung Neoplasms / pathology*
  • Male
  • Middle Aged
  • Prognosis
  • Proportional Hazards Models
  • Pulmonary Alveoli / pathology*
  • Survival Analysis


  • Biomarkers, Tumor