Open AccessThe sonographic features of malignant mediastinal lymph nodes and a proposal for an algorithmic approach for sampling during endobronchial ultrasound
Author(s) -
Alici Ibrahim Onur,
Yılmaz Demirci Nilgün,
Yılmaz Aydın,
Karakaya Jale,
Özaydın Esra
Publication year - 2016
Publication title -
the clinical respiratory journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.789
H-Index - 33
eISSN - 1752-699X
pISSN - 1752-6981
DOI - 10.1111/crj.12267
Subject(s) - medicine , endobronchial ultrasound , lymph node , radiology , lymph , ultrasound , sampling (signal processing) , calcification , bronchoscopy , pathology , computer science , filter (signal processing) , computer vision
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.