Breath sensors for lung cancer diagnosis Yekbun Adiguzel a,n , Haluk Kulah b,c a Department of Biophysics, School of Medicine, Istanbul Kemerburgaz University, Mahmutbey Dilmenler Caddesi, No. 26, 34217 Bagcilar, Istanbul, Turkey b METU-MEMS Research and Application Center, Middle East Technical University (METU), Ankara, Turkey c METU BioMEMS, Electrical and Electronics Engineering Department, METU, Universiteler Mah., Dumlupınar Bulv. No. 1, 06800 Çankaya, Ankara, Turkey article info Article history: Received 26 June 2014 Received in revised form 9 October 2014 Accepted 10 October 2014 Available online 19 October 2014 Keywords: Breath sensor Breath analysis Electronic nose (e-nose) Lung cancer Disease diagnosis abstract The scope of the applications of breath sensors is abundant in disease diagnosis. Lung cancer diagnosis is a well-fitting health-related application of this technology, which is of utmost importance in the health sector, because lung cancer has the highest death rate among all cancer types, and it brings a high yearly global burden. The aim of this review is first to provide a rational basis for the development of breath sensors for lung cancer diagnostics from a historical perspective, which will facilitate the transfer of the idea into the rapidly evolving sensors field. Following examples with diagnostic applications include colorimetric, composite, carbon nanotube, gold nanoparticle-based, and surface acoustic wave sensor arrays. These select sensor applications are widened by the state-of-the-art developments in the sensors field. Coping with sampling sourced artifacts and cancer staging are among the debated topics, along with the other concerns like proteomics approaches and biomimetic media utilization, feature selection for data classification, and commercialization. & 2014 Elsevier B.V. All rights reserved. Contents 1. Introduction ........................................................................................................ 122 2. Breath analysis ...................................................................................................... 123 2.1. Exhaled breath content ......................................................................................... 124 3. Sensor-based diagnosis ............................................................................................... 124 3.1. Colorimetric-sensor array ....................................................................................... 124 3.2. Carbon-polymer array .......................................................................................... 125 3.3. Single-walled carbon nanotubes array ............................................................................. 126 3.4. Surface acoustic wave sensor .................................................................................... 127 3.5. Gold nanoparticle sensor-based array ............................................................................. 127 3.6. Metalloporphyrins-coated quartz microbalance array ................................................................. 128 3.7. Optical fiber sensor ............................................................................................ 129 4. Considerations for State of the Art ...................................................................................... 129 4.1. Utilization of carbon nanotubes .................................................................................. 129 4.1.1. Utilization of functionalized surfaces ....................................................................... 130 4.2. Metal oxide semiconductor sensors ............................................................................... 131 4.2.1. Utilization of functionalized semiconductor metal oxide fibers .................................................. 131 4.2.2. Utilization of zeolites .................................................................................... 131 4.3. Utilization of gold nanorod-metalloporphyrins and pH sensitive dyes .................................................... 132 5. Future perspectives .................................................................................................. 132 5.1. Proteomics approaches ......................................................................................... 132 5.2. Utilization of biomimetic media .................................................................................. 132 5.3. Sampling related issues ......................................................................................... 133 5.4. Distinction of the cancer stages .................................................................................. 135 5.5. Feature selection for data classification ............................................................................ 135 Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/bios Biosensors and Bioelectronics http://dx.doi.org/10.1016/j.bios.2014.10.023 0956-5663/& 2014 Elsevier B.V. All rights reserved. n Corresponding author. E-mail addresses: yekbun.adiguzel@kemerburgaz.edu.tr (Y. Adiguzel), kulah@metu.edu.tr (H. Kulah). Biosensors and Bioelectronics 65 (2015) 121–138