Evidence map›Paper›PMID 29060925›Full record

SynthesisCancer biomarkers : section A of Disease markers2017

Breath based volatile organic compounds in the detection of breast, lung, and colorectal cancers: A systematic review.

Ingrid Oakley-Girvan, Sharon Watkins Davis

Abstract readSystematic Review
In one paragraph

Synthesis in Cancer biomarkers : section A of Disease markers, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed, 1 pooled it
2.6field-weighted citation impact, top 10% 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

30 citing papers in PubMed, 1 synthesis or guideline pooled it, 70 citations in OpenAlex.

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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

2 authors.

Ingrid Oakley-Girvan
Sharon Watkins Davis

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDetecting volatile organic compounds (VOCs) could provide a rapid, noninvasive, and inexpensive screening tool for detecting cancer.

objectiveIn this systematic review, we identified specific exhaled breath VOCs correlated with lung, colorectal, and breast cancer.

methodsWe identified relevant studies published in 2015 and 2016 by searching Pubmed and Web of Science. The protocol for this systematic review was registered in PROSPERO and the PRISMA guidelines were used in reporting. VOCs and performance data were extracted.

resultsThree hundred and thirty three records were identified and 43 papers were included in the review, of which 20 were review articles themselves. We identified 17 studies that listed the VOCs with at least a subset of statistics on detection cutoff levels, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and gradient.

conclusionsBreath analysis for cancer screening and early detection shows promise, because samples can be collected easily, safely, and frequently. While gas chromatography-mass spectrometry is considered the gold standard for identifying specific VOCs, breath analysis has moved into analyzing patterns of VOCs using a variety of different multiple sensor techniques, such as eNoses and nanomaterials. Further development of VOCs for early cancer detection requires clinical trials with standardized breath sampling methods.

Indexed as

ExhalationBreast NeoplasmsColorectal NeoplasmsEarly Detection of CancerFemaleHumansLung NeoplasmsMaleSensitivity and SpecificityVolatile Organic CompoundsVolatile Organic Compoundsbiomarkersbreast cancercolorectal cancerExhaled breathlung cancervolatile organic compounds (VOCs)

Identifiers

PMID29060925
PMCPMC13075741
OpenAlexW2766881057

What OpenQuestion holds

Textmetadata
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.