Literature DB >> 30007831

Differentiating malignant and benign lymph nodes using endobronchial ultrasound elastography.

Ching-Kai Lin1, Kai-Lun Yu1, Lih-Yu Chang1, Hung-Jen Fan2, Yueh-Feng Wen1, Chao-Chi Ho3.   

Abstract

BACKGROUND/
PURPOSE: Endobronchial ultrasound (EBUS) elastography is a new technique that provides information on tissue compressibility during endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA). The purposes of this study were to evaluate the utility of EBUS elastography in differentiating malignant and benign mediastinal lymph nodes (LNs) and to explore the factors that influence its accuracy.
METHODS: A retrospective chart review of patients who underwent EBUS-TBNA from October 2016 to July 2017 was performed. EBUS with conventional B-mode features and elastographic patterns were compared with the final pathology results or clinical follow-up. We used the following EBUS elastographic patterns for classification: type 1, predominantly non-blue (green, yellow and red); type 2, part blue, part non-blue; type 3, predominantly blue. The potential impacts of the characteristics of LNs, the underlying lung diseases and obtaining fibrotic components from EBUS-TBNA specimens were evaluated relative to the accuracy of EBUS elastography.
RESULTS: A total of 206 LNs from 94 patients were retrospectively evaluated. In classifying type 1 as 'benign' and type 3 as 'malignant,' the sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy rate were 90.6, 82.6, 71.6, 94.7 and 85.2%, respectively. The EBUS elastographic patterns had higher diagnostic yields and negative predictive values than conventional B-mode features. Logistic regression analysis revealed that central necrosis was a factor that influenced the accuracy of elastography in malignant LNs. The fibrotic component within benign LNs could cause an incorrect elastographic pattern.
CONCLUSION: EBUS elastography is a valuable tool in discriminating benign and malignant mediastinal LNs.
Copyright © 2018. Published by Elsevier B.V.

Entities:  

Keywords:  Elastography; Endobronchial ultrasound-guided transbronchial needle; Mediastinal lymph node

Mesh:

Year:  2018        PMID: 30007831     DOI: 10.1016/j.jfma.2018.06.021

Source DB:  PubMed          Journal:  J Formos Med Assoc        ISSN: 0929-6646            Impact factor:   3.282


  6 in total

1.  CT texture analysis of mediastinal lymphadenopathy: Combining with US-based elastographic parameter and discrimination between sarcoidosis and lymph node metastasis from small cell lung cancer.

Authors:  Eriko Koda; Tsuneo Yamashiro; Rintaro Onoe; Hiroshi Handa; Shinya Azagami; Shoichiro Matsushita; Hayato Tomita; Takeo Inoue; Masamichi Mineshita
Journal:  PLoS One       Date:  2020-12-02       Impact factor: 3.240

2.  Diagnostic value of endobronchial ultrasound elastography combined with rapid onsite cytological evaluation in endobronchial ultrasound-guided transbronchial needle aspiration.

Authors:  Jing Huang; Yuan Lu; Xihua Wang; Xiaoli Zhu; Ping Li; Jing Chen; Pingsheng Chen; Ming Ding
Journal:  BMC Pulm Med       Date:  2021-12-20       Impact factor: 3.317

3.  The role of endobronchial ultrasonography elastography for predicting malignancy.

Authors:  Benan Çağlayan; Sinem İliaz; Pınar Bulutay; Ayşe Armutlu; Işıl Uzel; Ayşe Bilge Öztürk
Journal:  Turk Gogus Kalp Damar Cerrahisi Derg       Date:  2020-01-23       Impact factor: 0.332

4.  Automatic Image Selection Model Based on Machine Learning for Endobronchial Ultrasound Strain Elastography Videos.

Authors:  Xinxin Zhi; Jin Li; Junxiang Chen; Lei Wang; Fangfang Xie; Wenrui Dai; Jiayuan Sun; Hongkai Xiong
Journal:  Front Oncol       Date:  2021-05-31       Impact factor: 6.244

5.  Quantitative analysis of endobronchial ultrasound elastography in computed tomography-negative mediastinal and hilar lymph nodes.

Authors:  Keigo Uchimura; Kei Yamasaki; Shinji Sasada; Sachika Hara; Issei Ikushima; Yosuke Chiba; Takashi Tachiwada; Toshinori Kawanami; Kazuhiro Yatera
Journal:  Thorac Cancer       Date:  2020-07-21       Impact factor: 3.500

6.  Deep learning with convex probe endobronchial ultrasound multimodal imaging: A validated tool for automated intrathoracic lymph nodes diagnosis.

Authors:  Jin Li; Xinxin Zhi; Junxiang Chen; Lei Wang; Mingxing Xu; Wenrui Dai; Jiayuan Sun; Hongkai Xiong
Journal:  Endosc Ultrasound       Date:  2021 Sep-Oct       Impact factor: 5.628

  6 in total

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