Application of an artificial neural network model for selection of potential lung cancer biomarkers. (6th May 2015)

• Record Type: Journal Article
• Authors: Ligor, Tomasz; Pater, Łukasz; Buszewski, Bogusław
• Is Part Of: Journal of breath research. Volume 9:Number 2(2015:Jun.)
• Publication Date: 2015-05-06
• Subjects: volatile organic compounds -- breath analysis -- artificial neural network -- relational sql database -- genetic algorithm; Volatile organic compounds -- Analysis -- Periodicals; Clinical chemistry -- Periodicals; Bad breath -- Periodicals; Bad breath -- Treatment -- Periodicals; Bad breath -- Diagnosis -- Periodicals; 616.0756
• DOI: 10.1088/1752-7155/9/2/027106
• Languages: English
• ISSNs: 1752-7155

Abstract

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.