Prediction of lung cancer using volatile biomarkers in breath.

Prediction of lung cancer using volatile biomarkers in breath. Research Abstract Details 

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Prediction of lung cancer using volatile biomarkers in breath. Abstract Text:

Michael Phillips,Nasser Altorki,John H M Austin,Robert B Cameron,Renee N Cataneo,Joel Greenberg,Robert Kloss,Roger A Maxfield,Muhammad I Munawar,Harvey I Pass,Asif Rashid,William N Rom,Peter Schmitt,

BACKGROUND: Normal metabolism generates several volatile organic compounds (VOCs) that are excreted in the breath (e.g. alkanes). In patients with lung cancer, induction of high-risk cytochrome p450 genotypes may accelerate catabolism of these VOCs, so that their altered abundance in breath may provide biomarkers of lung cancer. METHODS: VOCs in 1.0 L alveolar breath were analyzed in 193 subjects with primary lung cancer and 211 controls with a negative chest CT. Subjects were randomly assigned to a training set or to a prediction set in a 2:1 split. A fuzzy logic model of breath biomarkers of lung cancer was constructed in the training set and then tested in subjects in the prediction set by generating their typicality scores for lung cancer. RESULTS: Mean typicality scores employing a 16 VOC model were significantly higher in lung cancer patients than in the control group (p<0.0001 in all TNM stages). The model predicted primary lung cancer with 84.6% sensitivity, 80.0% specificity, and 0.88 area under curve (AUC) of the receiver operating characteristic (ROC) curve. Predictive accuracy was similar in TNM stages 1 through 4, and was not affected by current or former tobacco smoking. The predictive model achieved near-maximal performance with six breath VOCs, and was progressively degraded by random classifiers. Predictions with fuzzy logic were consistently superior to multilinear analysis. If applied to a population with 2% prevalence of lung cancer, a screening breath test would have a negative predictive value of 0.985 and a positive predictive value of 0.163 (true positive rate =0.277, false positive rate =0.029). CONCLUSIONS: A two-minute breath test predicted lung cancer with accuracy comparable to screening CT of chest. The accuracy of the test was not affected by TNM stage of disease or tobacco smoking. Alterations in breath VOCs in lung cancer were consistent with a non-linear pathophysiologic process, such as an off-on switch controlling high-risk cytochrome p450 activity. Further research is needed to determine if detection of lung cancer with this test will reduce mortality.

Prediction of lung cancer using volatile biomarkers in breath. Publishing Authors By Initials

M Phillips,N Altorki,JH Austin,RB Cameron,RN Cataneo,J Greenberg,R Kloss,RA Maxfield,MI Munawar,HI Pass,A Rashid,WN Rom,P Schmitt,

For similar biological factors: biological markers: tumor markers, biological research abstracts see: biological factors: biological markers: tumor markers, biological research

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Prediction of lung cancer using volatile biomarkers in breath. Journal Published:

PUBLICATION TYPE: Research Support, N.I.H., Extr

Journal: Cancer biomarkers : section A of Disease markers

VOLUME: 3

Page Numbers: 95-109

Journal Abbreviation:

ISSN: 1574-0153

DAY: 3

MONTH: 12

YEAR: 2007

Prediction of lung cancer using volatile biomarkers in breath. Information

Number of References:

LANGUAGE: eng

NlmUniqueID: 101256509

Prediction of lung cancer using volatile biomarkers in breath. Keywords Mesh Terms:

KEYWORDS: Tumor Markers, Biological

MESH TERMS: analysis

Chemical & Substance for Abstract: Prediction of lung cancer using volatile biomarkers in breath. Information

Substance Name: Tumor Markers, Biological

Registry Number: 0

Grant and Affiliation Information for Prediction of lung cancer using volatile biomarkers in breath.

AFFILIATION: Menssana Research Inc, Fort Lee, NJ 07024-6510, USA. mphillips@menssanaresearch.com

Country: Netherlands

AGENCY: United States NCI

GRANT: U01 CA086137

ACRONYM: CA

MEDLINETA: Cancer Biomark

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