Evidence map›Paper›PMID 32979158›Full record

SynthesisDigestive diseases and sciences2021

High Diagnostic Accuracy and Safety of Endoscopic Ultrasound-Guided Fine-Needle Aspiration in Malignant Lymph Nodes: A Systematic Review and Meta-Analysis.

Linbin Chen, Yin Li, Xiaoyan Gao, Shiyong Lin, Longjun He, Guangyu Luo, Jianjun Li, Chunyu Huang, Guobao Wang, Qing Yang and 1 more

Abstract readMeta-AnalysisSystematic Review
PubMed Publisher
In one paragraph

Synthesis in Digestive diseases and sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 1 pooled it
2.7field-weighted citation impact, top 8% 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

22 citing papers in PubMed, 1 synthesis or guideline pooled it, 39 citations in OpenAlex.

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  13. Contrast-enhanced endoscopic ultrasound for swollen lymph nodes.Journal of medical ultrasonics (2001) · 2023
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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

11 authors at 3 institutions in 1 country.

Linbin ChenDepartment of Endoscopy, Sun Yat-sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, 510060, People's Republic of China.
Yin LiDepartment of Endoscopy, Sun Yat-sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, 510060, People's Republic of China.
Xiaoyan GaoDepartment of Endoscopy, Sun Yat-sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, 510060, People's Republic of China.
Shiyong LinDepartment of Endoscopy, Sun Yat-sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, 510060, People's Republic of China.
Longjun HeDepartment of Endoscopy, Sun Yat-sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, 510060, People's Republic of China.
Guangyu LuoDepartment of Endoscopy, Sun Yat-sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, 510060, People's Republic of China.
Jianjun LiDepartment of Endoscopy, Sun Yat-sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, 510060, People's Republic of China.
Chunyu HuangDepartment of Endoscopy, Sun Yat-sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, 510060, People's Republic of China.
Guobao WangDepartment of Endoscopy, Sun Yat-sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, 510060, People's Republic of China.
Qing YangDepartment of Endoscopy, Sun Yat-sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, 510060, People's Republic of China.
Hongbo ShanDepartment of Endoscopy, Sun Yat-sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, 510060, People's Republic of China. shanhb@sysucc.org.cn.
Sun Yat-sen University · CNSun Yat-sen University Cancer Center · CNState Key Laboratory of Oncology in South China

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

aimsEndoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) is increasingly being used for diagnosing lymphadenopathy. We aim to systematically review the accuracy of EUS-FNA in differentiating benign and malignant mediastinal and abdominal lymph nodes (LNs).

methodsA comprehensive literature search was performed on multiple electronic databases through February 2020. A random or fixed effect model generated the pooled sensitivity, specificity, likelihood ratio (LR), and diagnostic odds ratio (DOR) of EUS-FNA. Subgroup analyses and meta-regression were used to explore sources of heterogeneity.

resultsTwenty-six studies involving 2753 patients with 2833 LNs were included. In the differential diagnosis of benign and malignant LNs, EUS-FNA had a pooled sensitivity, specificity, positive LR, and negative LR of 87% (95% confidence interval [CI] 86-90%), 100% (95% CI 99-100%), 68.98 (95% CI 42.10-113.02), and 0.14 (95% CI 0.11-0.17), respectively. The pooled rate of adverse events associated with EUS-FNA was 1.57% (95% CI 1.06-2.24%). The summary receiver operating characteristic (SROC) yielded an area under the curve (AUC) of 0.9912. EUS-FNA performed in mediastinal LNs gained a sensitivity of 85% (95% CI 81-88%), while in abdominal LNs, it reached 87% (95% CI 82-91%). The sensitivity of the subgroup with rapid on-site evaluation (ROSE) was 91% (95% CI 89-93%), while non-ROSE was 85% (95% CI 82-87%).

conclusionsEUS-FNA is a sensitive, highly specific, and safe method for distinguishing benign and malignant mediastinal or abdominal LNs. However, the sensitivity of EUS-FNA still varies significantly among different centers.

Indexed as

Endoscopic Ultrasound-Guided Fine Needle AspirationHumansLymphadenopathyLymph NodesDifferential diagnosisEndoscopic ultrasoundFine-needle aspirationLymphadenopathyLymph nodesMeta-analysis

Identifiers

PMID32979158
OpenAlexW3088126706

What OpenQuestion holds

Textmetadata
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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.