Application of an artificial neural network model for selection of potential lung cancer biomarkers.
Journal:
Journal of breath research
Published Date:
May 6, 2015
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.
Authors
Keywords
Adenocarcinoma
Adult
Aged
Aged, 80 and over
Biomarkers, Tumor
Breath Tests
Carcinoma, Non-Small-Cell Lung
Case-Control Studies
Exhalation
Female
Gas Chromatography-Mass Spectrometry
Humans
Lung Neoplasms
Male
Mass Spectrometry
Middle Aged
Neural Networks, Computer
Sensitivity and Specificity
Small Cell Lung Carcinoma
Smoking
Solid Phase Microextraction
Volatile Organic Compounds
Young Adult