Evidence map›Paper›PMID 30271314›Full record

ReviewCurrent respiratory medicine reviews2018

Liquid Biopsy as Surrogate to Tissue in Lung Cancer for Molecular Profiling: A Meta-Analysis.

Mona Mlika, Chadli Dziri, Mohamed Majdi Zorgati, Mehdi Ben Khelil, Faouzi Mezni

Abstract readReview
In one paragraph

Review in Current respiratory medicine reviews, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–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

10 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. UnearthingJCO global oncology · 2020
    Article
  10. Article
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

5 authors.

Mona Mlika1Department of Pathology, Abderrahman Mami Hospital, Ariana, Tunisia; 2University Tunis El Manar, Faculty of Medicine of Tunis, Tunis, Tunisia; 3Department of General Surgery B, Charles Nicolle Hospital, Tunis, Tunisia; 4Medical Center of ABM, Military College, Qatar.
Chadli Dziri1Department of Pathology, Abderrahman Mami Hospital, Ariana, Tunisia; 2University Tunis El Manar, Faculty of Medicine of Tunis, Tunis, Tunisia; 3Department of General Surgery B, Charles Nicolle Hospital, Tunis, Tunisia; 4Medical Center of ABM, Military College, Qatar.
Mohamed Majdi Zorgati1Department of Pathology, Abderrahman Mami Hospital, Ariana, Tunisia; 2University Tunis El Manar, Faculty of Medicine of Tunis, Tunis, Tunisia; 3Department of General Surgery B, Charles Nicolle Hospital, Tunis, Tunisia; 4Medical Center of ABM, Military College, Qatar.
Mehdi Ben Khelil1Department of Pathology, Abderrahman Mami Hospital, Ariana, Tunisia; 2University Tunis El Manar, Faculty of Medicine of Tunis, Tunis, Tunisia; 3Department of General Surgery B, Charles Nicolle Hospital, Tunis, Tunisia; 4Medical Center of ABM, Military College, Qatar.
Faouzi Mezni1Department of Pathology, Abderrahman Mami Hospital, Ariana, Tunisia; 2University Tunis El Manar, Faculty of Medicine of Tunis, Tunis, Tunisia; 3Department of General Surgery B, Charles Nicolle Hospital, Tunis, Tunisia; 4Medical Center of ABM, Military College, Qatar.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe accurate microscopic diagnosis of lung cancer has become insufficient due to the concept of personalized medicine. Tissue samples are used not only for microscopic diagnosis but also for the assessment of the different targets. Biopsies are performed in 80% of the patients and they are not sufficient for molecular diagnosis in 30% of the cases. Liquid biopsy (LB) has been reported as a possible surrogate to tissue samples and has been introduced in the management scheme of the patients since 2014. We aimed to highlight the diagnostic value of liquid biopsy in assessing the molecular profile of non small cell carcinomas in comparison with tissue biopsy.

methodsWe retracted eligible articles from PubMed, Embase and Cochrane databases. We calculated the pooled sensitivity (SEN), specificity (SPE), positive likelihood ratio (PLR), negative likelihood ratio (NLR) and diagnostic odds ratio (DOR). A summary receiver operating characteristic curve (SROC) and area under curve (AUC) were used to evaluate the overall diagnostic performance using the Meta-Disc software 5.1.32. The heterogeneity was assessed using I square statistics. A meta-regression was performed in case of heterogeneity. In case of absence of covariates, a sensitivity analysis was done in order to assess publications that induced a statistical bias.

results39 eligible studies involving 4782 patients were included. The overall statistical studies showed heterogeneity in the SEN, SPE, PLR, NLR and DOR. No threshold effect was revealed. The meta-regression incorporating the ethnicity, the test, the technique used in tissue and plasma and the use of plasma or serum as covariates showed no impact of these factors. A sensitivity analysis allowed achieving the homogeneity in the SPE and DOR. The overall pooled SEN and SPE were 0.61 and 0.95 respectively. The PLR was 9.51, the NLR was 0.45 and DOR was 24.58. The SROC curve with AUC of 0,93 indicated that the liquid biopsy is capable of identifying wild type samples from mutated ones with a relatively high accuracy.

conclusionThis meta-analysis suggested that detection of molecular mutations by cfDNA is of adequate diagnostic accuracy in association to tissues. The high specificity and the moderate sensitivity highlight the value of LB as a screening test.

Indexed as

cfDNAlung cancersensitivityspecificitySpecific liquid biopsytissue

Identifiers

PMID30271314
PMCPMC6128071

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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.