Bedoukian   RussellIPM   RussellIPM   Piezoelectric Micro-Sprayer


Home
Animal Taxa
Plant Taxa
Semiochemicals
Floral Compounds
Semiochemical Detail
Semiochemicals & Taxa
Synthesis
Control
Invasive spp.
References

Abstract

Guide

Alphascents
Pherobio
InsectScience
E-Econex
Counterpart-Semiochemicals
Print
Email to a Friend
Kindly Donate for The Pherobase

« Previous AbstractPrediction of breast cancer using volatile biomarkers in the breath    Next AbstractVolatile biomarkers of pulmonary tuberculosis in the breath »

Cancer Biomark


Title:Prediction of lung cancer using volatile biomarkers in breath
Author(s):Phillips M; Altorki N; Austin JH; Cameron RB; Cataneo RN; Greenberg J; Kloss R; Maxfield RA; Munawar MI; Pass HI; Rashid A; Rom WN; Schmitt P;
Address:"Menssana Research Inc, Fort Lee, NJ 07024-6510, USA. mphillips@menssanaresearch.com"
Journal Title:Cancer Biomark
Year:2007
Volume:3
Issue:2
Page Number:95 - 109
DOI: 10.3233/cbm-2007-3204
ISSN/ISBN:1574-0153 (Print) 1574-0153 (Linking)
Abstract:"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"
Keywords:"Aged Area Under Curve Biomarkers, Tumor/*analysis Breath Tests Female Humans Lung Neoplasms/*diagnosis Male *Models, Statistical Neoplasm Staging Organic Chemicals/analysis ROC Curve Sensitivity and Specificity;"
Notes:"MedlinePhillips, Michael Altorki, Nasser Austin, John H M Cameron, Robert B Cataneo, Renee N Greenberg, Joel Kloss, Robert Maxfield, Roger A Munawar, Muhammad I Pass, Harvey I Rashid, Asif Rom, William N Schmitt, Peter eng 5R44HL070411-03/HL/NHLBI NIH HHS/ U01 CA086137/CA/NCI NIH HHS/ Research Support, N.I.H., Extramural Netherlands 2007/05/25 Cancer Biomark. 2007; 3(2):95-109. doi: 10.3233/cbm-2007-3204"

 
Back to top
 
Citation: El-Sayed AM 2024. The Pherobase: Database of Pheromones and Semiochemicals. <http://www.pherobase.com>.
© 2003-2024 The Pherobase - Extensive Database of Pheromones and Semiochemicals. Ashraf M. El-Sayed.
Page created on 19-12-2024