Volatile Organic Compound Detection with FET Sensors and Neural Network Data Processing as a Preliminary Step to Early Lung Cancer Diagnosis

John C. Cancilla, Bin Wang, Pablo Diaz-Rodriguez, Gemma Matute, Hossam Haick, Jose S. Torrecilla

2014

Abstract

Cancer is currently one of deadliest and most feared diseases in the developed world, and, particularly, lung cancer (LC) is one of the most common types and has one of the highest death/incidence ratios. An early diagnosis for LC is probably the most accessible possibility to try and save patients and lower this ratio. Recently, research concerning LC-related breath biomarkers has provided optimistic results and has become a real option to try and obtain a fast, reliable, and early LC diagnosis. In this paper, a combination of field-effect transistor (FET) sensors and artificial neural networks (ANNs) has been employed to classify and estimate the partial pressures of a series of polar and nonpolar volatile organic compounds (VOCs) present in prepared gaseous mixtures. The objective of these preliminary tests is to give an idea of how well this technology can be used to analyze artificial or real breath samples by quantifying the LC-related VOCs or biomarkers. The results of this step are very promising and indicate that this methodology deserves further research using more complex samples to find the existing limitations of the FET-ANN combination.

References

  1. Anand, P., Kunnumakara, A. B., Sundaram, C., Harikumar, K.B., Tharakan, S. T., Lai, O. S., Sung, B., Aggarwa, B. B. (2008) 'Cancer is a Preventable Disease that Requires Major Lifestyle Changes', Pharm. Res., vol. 25, no. 92, pp.097-2116.
  2. Blase, X., Serra-Fernández, M. V. (2008) 'Preserved Conductance in Covalently Functionalized Silicon Nanowires', Physical Review Letters, vol. 100, no. 4.
  3. Cancilla, J. C., Torrecilla, J. S., Matute, G. (2014) 'Current Applications of Artificial Neural Networks in Biochemistry with Emphasis on Cancer Research', Curr. Biochem. Eng., vol. 1.
  4. Chen, K. I., Li, B. R., Chen, Y. T. (2011) 'Silicon Nanowire Field-Effect Transistor-Based Biosensors for Biomedical Diagnosis and Cellular Recording Investigation', Nano Today, vol. 6, pp. 131-154.
  5. Cui, Y., Zhong, Z., Wang, D., Wang, W.U., Lieber, C.M. (2003) 'High Performance Silicon Nanowire Field Effect Transistors', Nano Letters, vol. 3, no. 2.
  6. Demuth, H., Beale, M., Hagan, M. (2005) 'Neural Network Toolbox for Use with MATLAB® User's Guide'. Version 4.0.6. Ninth printing Revised for Version 4.0.6 (Release 14SP3), Natick, MA (USA).
  7. Desai, K. M., Survase, S. A., Saudagar, P. S., Lele, S. S., Singhal, P.S. (2008) 'Comparison of Artificial Neural Network (ANN) and Response Surface Methodology (RSM) in Fermentation Media Optimization: Case Study of Fermentative Production of Scleroglucan', Biochem. Eng. J., vol. 41, num. 3, pp. 266-273.
  8. Farrington, S. M., Lin-Goerke, J., Ling, J., Wang, Y., Burczak, J. D., Robbins, D. J., Dunlop, M. G. (1998) 'Systematic Analysis of hMSH2 and hMLH1 in Young Colon Cancer Patients and Controls', Am. J. Hum. Genet., vol. 63, pp. 749-759.
  9. Flores-Fernández, J. M., Herrera-López, E. J., SánchezLlamas, F., Rojas-Calvillo, A., Cabrera-Galeana, P.A., Leal-Pacheco, G., González-Palomar, M. G., Femat, R., Martínez-Velázquez, M. (2012) 'Development of an Optimized Multi-biomarker Panel for the Detection of Lung Cancer Based on Principal Component Analysis and Artificial Neural Network Modeling', Expert Syst. Appl., vol. 39, no. 12, pp. 10851-10856.
  10. Gueguim-Kana, E. B., Oloke, J. K., Lateef, A., Adesiyan, M.O. (2012) 'Modeling and optimization of biogas production on saw dust and other co-substrates using Artificial Neural network and Genetic Algorithm', Renew. Energy., vol. 46, pp. 276-281.
  11. Jain, A. K., Mao, J., Mohiuddin, K. M. (1996) 'Artificial Neural Networks: A Tutorial', Computer, vol. 29, no. 3, pp. 31-44.
  12. Jemal, A., Bray, F., Center, M. M., Ferlay, J., Ward, E., Forman, D. (2011) 'Global Cancer Statistics', CA Cancer J. Clin., vol. 61, pp. 69-90.
  13. Knoerzer, K., Juliano, P., Roupas, P., Versteeg, C. (2011) 'Innovative Food Processing Technologies: Advances in Multiphysics Simulation', Oxford (UK), WileyBlackwell.
  14. Li, Y., Qian, F., Xiang, J., Lieber, C. M. (2006) 'Nanowire Electronic and Optoelectronic Devices', Materials Today, vol. 9, no. 10.
  15. McGrath, M., Lee, I. M., Buring, J., De-Vivo, I. (2011) 'Common Genetic Variation Within IGFI, IGFII, IGFBP-1, and IGFBP-3 and Endometrial Cancer Risk' Gynecol. Oncol., vol. 120 no. 2, pp. 174-178.
  16. Oliferenko, A. A., Oliferenko, P. V., Torrecilla, J. S., Katritzkya, A. R. (2013) 'Rebuttal to “comments on “Boiling Points of Ternary Azeotropic Mixtures Modeled with the Use of Universal Solvation Equation and Neural Networks”78, Industrial & Engineering Chemistry Research, vol. 52, pp. 545-546.
  17. Palancar, M. C., Aragon, J. M., Torrecilla, J. S. (1998) 'pH-Control System Based on Artificial Neural Networks; Industrial & Engineering Chemistry Research', vol. 37, no. 7, pp. 2729-2740.
  18. Parmigiani, G., Berry, D. A., Aguilar, O. (1998) 'Determining Carrier Probabilities for Breast CancerSusceptibility Genes BRCA1 and BRCA278, Am. J. of Hum. Genet., vol. 62, no. 1, pp. 145-158.
  19. Paska, Y., Haick, H. (2009) 'Controlling properties of field effect transistors by intermolecular cross-linking of molecular dipoles', Applied Physics Letters, vol. 95.
  20. Peled, N., Hakim, M., Bunn, P. A., Miller, Y. E., Kennedy, T.C., Mattei, J., Mitchell, J. D., Hirsch, F. R., Haick, H. (2012) 'Non-invasive Breath Analysis of Pulmonary Nodules', J. Thorac. Oncol., vol. 7, pp. 1528-1533.
  21. Peng, G., Trock, E., Haick, H. (2008) 'Detecting Simulated Patterns of Lung Cancer Biomarkers by Random Network of Single-Walled Carbon Nanotubes Coated with Nonpolymeric Organic Materials', Nano Letters, vol. 8, no. 11, pp. 3631-3635.
  22. Peng, G., Tisch, U., Adams, O., Hakim, M., Shehada, N., Broza, Y. Y., Billan, S., Abdah-Bortnyak, R., Kuten, A., Haick, H. (2009) 'Diagnosing lung cancer in exhaled breath using gold nanoparticles', Nature Nanotechnology.
  23. Plass, K. E., Liu, X., Brunschwig, B. S., Lewis, N. S. (2008) 'Passivation and Secondary Functionalization of Allyl-Terminated Si(111) Surfaces', Chem. Mater., vol. 20, pp. 2228-2233.
  24. Sze, S. M. (2001) 'Semiconductor Devices; Physics and Technology', New York (USA), 2, John Wiley & Sons Inc.
  25. Tisch, U., Billan, S., Ilouze, M., Phillips, M., Peled, N., Haick, H. (2012) 'Volatile Organic Compounds in Exhaled Breath as Biomarkers for the Early Detection and Screening of Lung Cancer', CML - Lung Cancer, vol. 5, no. 4, pp. 107-117.
  26. Torrecilla, J. S., Aragón, J. M., Palancar, M. C. (2008) 'Optimization of an Artificial Neural Network by Selecting the Training Function. Application to Solid Drying', Industrial & Engineering Chemistry Research, vol. 47, pp. 7072-7080.
  27. Torrecilla, J. S., Sanz, P. D. (2011) 'Neural Networks: Their Role in High-Pressure Processing. Book Title: Innovative Food Processing Technologies: Advances in Multiphysics Simulation' (Eds. Kai Knoerzer, Pablo Juliano, Peter Roupas, Cornelis Versteeg) John Wiley & Sons, Ltd. and Institute of Food Technologists.
  28. Torrecilla, J. S., Tortuero, C., Cancilla, J. C., DíazRodríguez, P. (2013) 'Estimation with Neural Networks of the Water Content in Imidazolium-Based Ionic Liquids Using their Experimental Density and Viscosity Values', Talanta, vol. 113, pp. 93-98.
  29. Wang, B., Haick, H. (2013) 'Effect of Functional Groups on the Sensing Properties of Silicon Nanowires toward Volatile Compounds', ACS Appl. Mater. Interfaces, vol. 5, pp. 2289-2299.
  30. Wu, Yo., Wu, Yi., Wang, J., Yan, Z., Qu, L., Xiang, B., Zhang, Y. (2011) 'An Optimal Tumor Marker GroupCoupled Artificial Neural Network for Diagnosis of Lung Cancer', Expert Syst. Appl., vol. 38, no. 9, pp. 11329-11334.
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Paper Citation


in Harvard Style

C. Cancilla J., Wang B., Diaz-Rodriguez P., Matute G., Haick H. and Torrecilla J. (2014). Volatile Organic Compound Detection with FET Sensors and Neural Network Data Processing as a Preliminary Step to Early Lung Cancer Diagnosis . In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014) ISBN 978-989-758-054-3, pages 56-64. DOI: 10.5220/0005068700560064


in Bibtex Style

@conference{ncta14,
author={John C. Cancilla and Bin Wang and Pablo Diaz-Rodriguez and Gemma Matute and Hossam Haick and Jose S. Torrecilla},
title={Volatile Organic Compound Detection with FET Sensors and Neural Network Data Processing as a Preliminary Step to Early Lung Cancer Diagnosis},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)},
year={2014},
pages={56-64},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005068700560064},
isbn={978-989-758-054-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)
TI - Volatile Organic Compound Detection with FET Sensors and Neural Network Data Processing as a Preliminary Step to Early Lung Cancer Diagnosis
SN - 978-989-758-054-3
AU - C. Cancilla J.
AU - Wang B.
AU - Diaz-Rodriguez P.
AU - Matute G.
AU - Haick H.
AU - Torrecilla J.
PY - 2014
SP - 56
EP - 64
DO - 10.5220/0005068700560064