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 AbstractDetermination of volatile organic compounds in exhaled breath of patients with lung cancer using solid phase microextraction and gas chromatography mass spectrometry    Next AbstractAnalysis of VOCs in Urine Samples Directed towards of Bladder Cancer Detection »

J Breath Res


Title:Application of an artificial neural network model for selection of potential lung cancer biomarkers
Author(s):Ligor T; Pater L; Buszewski B;
Address:"Department of Environmental Chemistry and Bioanalytics, Faculty of Chemistry, Nicolaus Copernicus University, 7 Gagarin St, 87-100 Torun, Poland"
Journal Title:J Breath Res
Year:2015
Volume:20150506
Issue:2
Page Number:27106 -
DOI: 10.1088/1752-7155/9/2/027106
ISSN/ISBN:1752-7163 (Electronic) 1752-7155 (Linking)
Abstract:"Determination of volatile organic compounds (VOCs) in the exhaled breath samples of lung cancer patients and healthy controls was carried out by SPME-GC/MS (solid phase microextraction- gas chromatography combined with mass spectrometry) analyses. In order to compensate for the volatile exogenous contaminants, ambient air blank samples were also collected and analyzed. We recruited a total of 123 patients with biopsy-confirmed lung cancer and 361 healthy controls to find the potential lung cancer biomarkers. Automatic peak deconvolution and identification were performed using chromatographic data processing software (AMDIS with NIST database). All of the VOCs sample data operation, storage and management were performed using the SQL (structured query language) relational database. The selected eight VOCs could be possible biomarker candidates. In cross-validation on test data sensitivity was 63.5% and specificity 72.4% AUC 0.65. The low performance of the model has been mainly due to overfitting and the exogenous VOCs that exist in breath. The dedicated software implementing a multilayer neural network using a genetic algorithm for training was built. Further work is needed to confirm the performance of the created experimental model"
Keywords:"Adenocarcinoma/diagnosis/*metabolism Adult Aged Aged, 80 and over Biomarkers, Tumor/*metabolism Breath Tests/methods Carcinoma, Non-Small-Cell Lung/diagnosis/*metabolism Case-Control Studies Exhalation Female Gas Chromatography-Mass Spectrometry/methods H;"
Notes:"MedlineLigor, Tomasz Pater, Lukasz Buszewski, Boguslaw eng Research Support, Non-U.S. Gov't England 2015/05/07 J Breath Res. 2015 May 6; 9(2):027106. doi: 10.1088/1752-7155/9/2/027106"

 
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 22-11-2024