Evidence map›Paper›PMID 22071780›Full record

Trial reportJournal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer2012

Exhaled breath analysis with a colorimetric sensor array for the identification and characterization of lung cancer.

Peter J Mazzone, Xiao-Feng Wang, Yaomin Xu, Tarek Mekhail, Mary C Beukemann, Jie Na, Jonathan W Kemling, Kenneth S Suslick, Madhu Sasidhar

Abstract readClinical Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
76citing papers in PubMed, 6 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

76 citing papers in PubMed, 6 syntheses or guidelines pooled it.

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  15. Discovery and Validation of a Volatile Signature of Eosinophilic Airway Inflammation in Asthma.American journal of respiratory and critical care medicine · 2024
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16 more citing papers are in PubMed but not listed here.

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

9 authors.

Peter J MazzoneRespiratory Institute, Cleveland Clinic, Cleveland, Ohio 44195, USA. mazzonp@ccf.org
Xiao-Feng Wang
Yaomin Xu
Tarek Mekhail
Mary C Beukemann
Jie Na
Jonathan W Kemling
Kenneth S Suslick
Madhu Sasidhar

Funding

A VOC Dosimeter Based On A Colorimetric Sensor ArrayU01ES016011 · NIEHS · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI SUSLICK, KENNETH S · 2007 to 2010
$2.7M
NIEHS NIH HHS U01 ES016011NIEHS NIH HHS U01ES016011
6 · The paper itself

Abstract

introductionThe pattern of exhaled breath volatile organic compounds represents a metabolic biosignature with the potential to identify and characterize lung cancer. Breath biosignature-based classification of homogeneous subgroups of lung cancer may be more accurate than a global breath signature. Combining breath biosignatures with clinical risk factors may improve the accuracy of the signature.

objectivesTo develop an exhaled breath biosignature of lung cancer using a colorimetric sensor array and to determine the accuracy of breath biosignatures of lung cancer characteristics with and without the inclusion of clinical risk factors.

methodsThe exhaled breath of 229 study subjects, 92 with lung cancer and 137 controls, was drawn across a colorimetric sensor array. Logistic prediction models were developed and statistically validated based on the color changes of the sensor. Age, sex, smoking history, and chronic obstructive pulmonary disease were incorporated in the prediction models.

resultsThe validated prediction model of the combined breath and clinical biosignature was moderately accurate at distinguishing lung cancer from control subjects (C-statistic 0.811). The accuracy improved when the model focused on only one histology (C-statistic 0.825-0.890). Individuals with different histologies could be accurately distinguished from one another (C-statistic 0.864 for adenocarcinoma versus squamous cell carcinoma). Moderate accuracies were noted for validated breath biosignatures of stage and survival (C-statistic 0.785 and 0.693, respectively).

conclusionsA colorimetric sensor array is capable of identifying exhaled breath biosignatures of lung cancer. The accuracy of breath biosignatures can be optimized by evaluating specific histologies and incorporating clinical risk factors.

Indexed as

Breath TestsColorimetryAdenocarcinomaAdultAgedCarcinoma, Non-Small-Cell LungCarcinoma, Small CellCarcinoma, Squamous CellHumansLogistic ModelsLung NeoplasmsMiddle AgedNeoplasm StagingPredictive Value of TestsProspective StudiesROC Curve

Identifiers

PMID22071780
PMCPMC3241851

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Registered trials

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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.