title: The sonographic features of malignant mediastinal lymph nodes and a proposal for an algorithmic approach for sampling during endobronchial ultrasound.
abstract: BACKGROUND
  There are several papers on the sonographic features of mediastinal lymph nodes affected by several diseases, but none gives the importance and clinical utility of the features.
  
  
  OBJECTIVE
  In order to find out which lymph node should be sampled in a particular nodal station during endobronchial ultrasound, we investigated the diagnostic performances of certain sonographic features and proposed an algorithmic approach.
  
  
  METHODS
  We retrospectively analyzed 1051 lymph nodes and randomly assigned them into a preliminary experimental and a secondary study group. The diagnostic performances of the sonographic features (gray scale, echogeneity, shape, size, margin, presence of necrosis, presence of calcification and absence of central hilar structure) were calculated, and an algorithm for lymph node sampling was obtained with decision tree analysis in the experimental group. Later, a modified algorithm was applied to the patients in the study group to give the accuracy.
  
  
  RESULTS
  The demographic characteristics of the patients were not statistically significant between the primary and the secondary groups. All of the features were discriminative between malignant and benign diseases. The modified algorithm sensitivity, specificity, and positive and negative predictive values and diagnostic accuracy for detecting metastatic lymph nodes were 100%, 51.2%, 50.6%, 100% and 67.5%, respectively.
  
  
  CONCLUSIONS
  In this retrospective analysis, the standardized sonographic classification system and the proposed algorithm performed well in choosing the node that should be sampled in a particular station during endobronchial ultrasound.
authors.affiliation: Lung Transplantation Center, Turkiye Yuksek Ihtisas Education and Research Hospital, Ankara, Turkey. ioalici@hotmail.com.
authors.forename: Ibrahim Onur
authors.initials: IO
authors.lastname: Alici
authors.affiliation: Department of Pulmonary Diseases, Gazi University, Ankara, Turkey.
authors.forename: Nilgün
authors.identifier: http://orcid.org/0000-0001-6160-3778
authors.initials: N
authors.lastname: Yılmaz Demirci
authors.affiliation: Interventional Pulmonology Unit, Atatürk Chest Diseases and Thoracic Surgery Education and Research Hospital, Ankara, Turkey.
authors.forename: Aydın
authors.initials: A
authors.lastname: Yılmaz
authors.affiliation: Department of Biostatistics, Hacettepe University Faculty of Medicine, Ankara, Turkey.
authors.forename: Jale
authors.initials: J
authors.lastname: Karakaya
authors.affiliation: Department of Pathology, Atatürk Chest Diseases and Thoracic Surgery Education and Research Hospital, Ankara, Turkey.
authors.forename: Esra
authors.initials: E
authors.lastname: Özaydın
mesh_terms: D000368:Aged; D003663:Decision Trees; D061765:Endoscopic Ultrasound-Guided Fine Needle Aspiration / Q000379:methods; D005260:Female; D006801:Humans; D008198:Lymph Nodes / Q000000981:diagnostic imaging* / Q000473:pathology*; D008297:Male; D008479:Mediastinal Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology*; D008875:Middle Aged; D011897:Random Allocation; D012189:Retrospective Studies; D018084:Ultrasonography, Interventional / Q000379:methods
keywords: algorithm; endobronchial ultrasound; lung cancer; mediastinum