Endobronchial ultrasound-guided fine-needle aspiration of mediastinal lymph nodes: a single institution's early learning curve

Ann Thorac Surg. 2008 Oct;86(4):1104-9; discussion 1109-10. doi: 10.1016/j.athoracsur.2008.06.042.


Background: The gold standard for mediastinal lymph node evaluation is mediastinoscopy, which is invasive and allows access to only a limited number of mediastinal lymph node (MLN) stations (1, 2, 3, 4, and 7). Endobronchial ultrasound-guided fine-needle aspiration (EBUS-FNA) is emerging as a useful, less invasive technique that offers access to a wider range of MLN stations (2, 3, 4, 7, 10, and 11). We report our initial experience with this procedure.

Methods: Using our prospectively maintained database, we performed a single-institution retrospective chart review. Our study group consisted of all patients at the University of Minnesota who underwent EBUS-FNA for evaluation of mediastinal lymphadenopathy or for thoracic malignancy staging from September 1, 2006, through December 15, 2007. To assess our learning curve, we plotted the cumulative sensitivity (%) and accuracy (%) of our EBUS-FNA results as a function of the number of procedures we performed.

Results: During the study period, 100 patients underwent EBUS, 92 with FNA. Of these, 56 patients (34 women, 22 men; mean age, 60.4 +/- 13.7 years) met our inclusion criteria. We found no complications. After our first 10 procedures, the sensitivity of our EBUS-FNA results was 96.2%; accuracy was 97.8% (rates comparable with other large series in the literature).

Conclusions: We conclude that the learning curve for EBUS-FNA for thoracic surgeons is about 10 procedures.

MeSH terms

  • Adult
  • Biopsy, Fine-Needle / methods*
  • Clinical Competence*
  • Education, Medical, Continuing
  • Endosonography / methods*
  • Female
  • Humans
  • Lymph Nodes / pathology*
  • Male
  • Mediastinal Neoplasms / diagnosis
  • Mediastinal Neoplasms / pathology*
  • Mediastinum
  • Neoplasm Invasiveness / pathology
  • Probability
  • Registries
  • Sensitivity and Specificity