Evidence map›Paper›PMID 32691537›Full record

ArticleThoracic cancer2020

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

Keigo Uchimura, Kei Yamasaki, Shinji Sasada, Sachika Hara, Issei Ikushima, Yosuke Chiba, Takashi Tachiwada, Toshinori Kawanami, Kazuhiro Yatera

Open access · goldAbstract read
In one paragraph

Article in Thoracic cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
1.4field-weighted citation impact, top 15% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 16 citations in OpenAlex.

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  11. Influential Factors for Assessing Endobronchial Ultrasound Elastography.Respiration; international review of thoracic diseases · 2020
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 2 institutions in 1 country.

Keigo UchimuraDepartment of Respiratory Medicine, University of Occupational and Environmental Health, Fukuoka, Japan.ORCID 0000-0001-6379-9583
Kei YamasakiDepartment of Respiratory Medicine, University of Occupational and Environmental Health, Fukuoka, Japan.
Shinji SasadaDepartment of Respiratory Medicine, Tokyo Saiseikai Central Hospital, Tokyo, Japan.
Sachika HaraDepartment of Respiratory Medicine, University of Occupational and Environmental Health, Fukuoka, Japan.
Issei IkushimaDepartment of Respiratory Medicine, University of Occupational and Environmental Health, Fukuoka, Japan.
Yosuke ChibaDepartment of Respiratory Medicine, University of Occupational and Environmental Health, Fukuoka, Japan.
Takashi TachiwadaDepartment of Respiratory Medicine, University of Occupational and Environmental Health, Fukuoka, Japan.
Toshinori KawanamiDepartment of Respiratory Medicine, University of Occupational and Environmental Health, Fukuoka, Japan.
Kazuhiro YateraDepartment of Respiratory Medicine, University of Occupational and Environmental Health, Fukuoka, Japan.
University of Occupational and Environmental Health Japan · JPSaiseikai Central Hospital · JP

Funding

JSPS JP19K16966
6 · The paper itself

Abstract

backgroundEndobronchial ultrasound (EBUS) elastography assists in the differentiation of benign and malignant lymph nodes (LNs) during transbronchial needle aspiration (TBNA). However, previous studies have not compared B-mode sonographic images (BSIs) and EBUS elastography images (EEIs) with final pathological diagnoses in radiologically normal-sized (computed tomography [CT]-negative) LNs.

methodsConsecutive patients with CT-negative LNs, who received EBUS-TBNA, were retrospectively reviewed. Images of BSIs and EEIs of each LN were stored and independently evaluated. EEIs were assessed by calculating the stiffness area ratio (SAR, blue/overall areas). The receiver operating characteristic curve was used to calculate the cutoff value for the SAR. Diagnostic test parameters were evaluated for each EBUS finding.

resultsA total of 132 patients (149 LNs) were enrolled, and the median SAR of malignant LNs was significantly higher than that of benign LNs (0.58 vs. 0.32, P < 0.001). At the SAR cutoff of 0.41, the sensitivity, specificity, positive predictive value, negative predictive value (NPV), and diagnostic accuracy rate (DAR) of elastography were 88.2%, 80.2%, 78.9%, 89.0%, and 83.9%, respectively. The logistic regression analysis showed that elastography was the strongest predictor of malignancy (odds ratio, 18.5; 95% confidence interval [CI]: 6.48-52.6; P < 0.001). The highest NPV (96.6%) was achieved with a combination of BSIs and EEIs.

conclusionsEBUS elastography predicted malignant LNs with a high DAR and NPV in CT-negative LNs. The NPV was highest when EEIs were combined with BSIs. Therefore, the combined evaluation of CT-negative LNs using EEIs and BSIs may help bronchoscopists perform EBUS-TBNA more efficiently. KEY POINTS: SIGNIFICANT FINDINGS OF THE STUDY: Endobronchial ultrasound elastography accurately predicted malignancy with a high diagnostic accuracy rate and negative predictive value in radiologically normal-sized lymph nodes. The additional use of B-mode sonographic features resulted in a higher negative predictive value. WHAT THIS STUDY ADDS: Endobronchial ultrasound elastography can guide the accurate collection of specimens with transbronchial needle aspiration, even in radiologically normal-sized lymph nodes. It can also readily distinguish benign and malignant lymph nodes, thus avoiding unnecessary punctures.

Indexed as

AdultAgedAged, 80 and overBronchoscopyEndoscopic Ultrasound-Guided Fine Needle AspirationFemaleHumansLymph NodesMaleMiddle AgedTomography, X-Ray ComputedBronchoscopyelastographyendobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA)lung cancer

Identifiers

PMID32691537
PMCPMC7471032
OpenAlexW3045343593

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.