Evidence map›Paper›PMID 40391063›Full record

ArticleERJ open research2025

The electronic nose in lung cancer diagnostics: a systematic review and meta-analysis.

Eline G M Steenhuis, Oke D Asmara, Sharina Kort, Mirell H G Papenhuijzen, Nic J G M Veeger, Michel M Van den Heuvel, Wouter H Van Geffen

Abstract read
In one paragraph

Article in ERJ open research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Review
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

7 authors.

Eline G M SteenhuisDepartment of Pulmonology, Isala, Zwolle, The Netherlands.
Oke D AsmaraDepartment of Pulmonology, Medisch Centrum Leeuwarden, Leeuwarden, The Netherlands.
Sharina KortDepartment of Pulmonology, Medisch Spectrum Twente, Enschede, The Netherlands.ORCID https://orcid.org/0000-0002-4628-1472
Mirell H G PapenhuijzenIsala Academy, Isala, Zwolle, The Netherlands.
Nic J G M VeegerDepartment of Epidemiology and Statistics, Medisch Centrum Leeuwarden, Leeuwarden, The Netherlands.
Michel M Van den HeuvelDepartment of Pulmonology, Radboud University Medical Centre, Nijmegen, The Netherlands.
Wouter H Van GeffenDepartment of Pulmonology, Medisch Centrum Leeuwarden, Leeuwarden, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Lung cancer remains a leading cause of cancer-related deaths. Early-stage detection is pivotal for curative interventions, yet most cases are diagnosed at advanced stages. Noninvasive point-of-care diagnostic strategies are needed to reduce lung cancer mortality rates. We assess the potential of electronic nose (eNose) devices as a tool for lung cancer detection. Study design and methods: All online available databases were searched. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we reviewed studies using eNose for Results: We included 35 studies, comprising 4483 patients. Pooled sensitivity and specificity were 0.90 (95% CI 0.87-0.93) and 0.89 (95% CI 0.85-0.93), respectively. The median negative predictive value was 0.93 and the median area under the curve was 0.91. Both the likelihood of bias and concerns regarding applicability were minimal. Interpretation: eNose devices hold promise as noninvasive, point-of-care tools for lung cancer diagnosis. Owing to the high sensitivity and negative predictive values and great ease of use, they could be valuable additions to the diagnostic toolbox. Further research and validation efforts are critical to maximise their impact on early lung cancer detection.

Identifiers

PMID40391063
PMCPMC12086828

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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.