Evidence map›Paper›PMID 30636881›Full record

ArticleOncoTargets and therapy2019

The diagnostic accuracy of liquid exosomes for lung cancer detection: a meta-analysis.

Zhipeng Song, Saisai Wang, Yang Liu

Abstract read
In one paragraph

Article in OncoTargets and therapy, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 2 pooled it
–field-weighted citation impact
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

11 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

3 authors.

Zhipeng SongDepartment of Epidemiology, Beijing Chest Hospital, Capital Medical University, Beijing, China, lygyl1973@126.com.
Saisai WangDepartment of Epidemiology, Beijing Chest Hospital, Capital Medical University, Beijing, China, lygyl1973@126.com.
Yang LiuDepartment of Epidemiology, Beijing Chest Hospital, Capital Medical University, Beijing, China, lygyl1973@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeSeveral studies have suggested that liquid exosomes can be used as biomarkers for the diagnosis of lung cancer (LC). The purpose of this meta-analysis was to investigate the comprehensive diagnostic value of liquid exosomes for LC. MATERIALS AND

methodsRelevant studies were searched from multiple electronic databases. The quality of the studies was assessed by the Quality Assessment of Diagnostic Accuracy Studies-2 criteria in RevMan 5.3 software. Stata 14.0 software and Meta-disc 1.4 software were used to synthesize the diagnostic parameters. Publication bias was judged according to the Deeks' funnel plot asymmetry test.

resultsThere were 13 eligible articles that comprised 1,338 LC patients and 1,075 paired controls for the meta-analysis. The pooled sensitivity (SEN), specificity (SPE), diagnostic likelihood ratio positive (DLR+), diagnostic likelihood ratio negative (DLR-), diagnostic OR (DOR), and area under the curve (AUC) of liquid exosomes in diagnosing LC were 0.82 (95% CI: 0.76-0.87), 0.84 (95% CI: 0.77-0.89), 5.27 (95% CI: 3.58-7.75), 0.21 (95% CI: 0.15-0.29), 25.14 (95% CI: 14.25-44.33), and 0.90 (95% CI: 0.87-0.92), respectively. Research based on serum, miRNA, the isolation kit method, one index in exosomes, patient sample size of 50 or greater, and control group size of 50 or greater obtained higher AUC values when the LC type was small cell lung cancer.

conclusionLiquid exosomes have shown potential as novel biomarkers that could facilitate LC diagnosis. Further prospective studies are still needed to confirm the diagnostic value of liquid exosomes.

Indexed as

diagnosisexosomesliquidlung neoplasmmeta-analysis

Identifiers

PMID30636881
PMCPMC6309778

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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.