Electronic nose

Electronic nose detecting lung cancer

Early detection of lung cancer

Lung cancer is one of the most common types of cancer among men and women and causes the most cancer-related deaths (second most common for women). Overall, a small percentage of lung cancer patients survive more than five years after their diagnosis. With this in mind, it is of great importance to find a way to detect lung cancer in as early a stage as possible. Micronit joined the LCAOS (Lung Cancer Artificial Olfactory System) project to contribute to this cause. 

The LCAOS project

Together with a team of specialists from different fields, Micronit worked on the development of an electronic nose to detect volatile biomarkers that are indicative of early stage lung cancer in the breath of patients. Within the LCAOS project novel diagnostic tools were developed to:

  • detect the presence of lung cancer;
  • calculate the risk of a patient developing lung cancer in the future.

Conventional diagnostic tests were considered unsuitable for widespread screening because they were costly, occasionally missed tumors, were not time-efficient, nor free of complications. The electronic nose would overcome these problems by using an approach based on the detection of volatile biomarkers emitted from the lung tissue into the exhaled breath using state of the art sensor technology. 

Micronit’s involvement

We developed a microfluidic exposure cell into which breath is pumped and then distributed to an array of silicon nanowire or gold nanoparticle based sensor chips. These sensor chips were manufactured by project partners Technion (Israel) and the Max Planck Institute for the Science of Light (Germany) and shipped to Micronit where the assembly onto the exposure cell took place using a room temperature flip chip bonding process. For reading out the sensors, JLM Innovation (Germany) developed the electronics and the Universidad Complutense De Madrid (Spain) developed a neural network and advanced pattern recognition algorithms to process the sensor data. 

The research receives funding from the FP7-Health Program (grant agreement no. 258868).