Early diagnosis of lung cancer using a sensor gas analysis complex: case report

Background. Currently, low-dose computed tomography (LDCT) is the only screening test that reduces the risk of death from lung cancer. However, there are a number of disadvantages, such as lack of widespread use, high cost, high false-positive rate and the need to conduct studies only in high-risk groups, which significantly limit mass screening. exhaled breath analysis, which uses sensitive breath sensors, is a promising method to improve early diagnosis of lung cancer. Cancer Research Institute of Tomsk National Research Medical Center together with Tomsk State University and Tomsk Polytechnic Research Institute has developed a gas analysis complex capable of analyzing the gas composition of exhaled air with remote sampling from bags. during the study, data obtained by digitizing signals from gas analysis system sensors and patient metadata are recorded in a database for subsequent automated processing and analysis using a neural network. Case description. A 48-year-old female patient with a long history of smoking came to the clinic of the Cancer Research Institute for consultation with suspected pathological infiltration around the celiac trunk detected by abdominal CT. As a clinical trial of the developed gas analytical complex for cancer detection, a sample of exhaled air was taken, and the comparison of the composition of volatile organic compounds (VOCs) with that in the control group (healthy individuals) revealed abnormalities characteristic of lung cancer. the patient underwent a chest CT scan, which revealed stage IIB peripheral cancer of the lower lobe of the left lung. the original sensor gas analysis complex, which has no analogues in Russia, was used for the first time in the detection of lung cancer. the data obtained allowed us to suspect the presence of lung tumor in the patient and perform radical surgical treatment. the composition of VOCs in exhaled air was assessed on day 10 after surgery, and no significant changes in the composition of exhaled air were observed. Conclusion. Machine learning algorithms are actively used to diagnose socially significant diseases. the platforms being developed based on arrays of chemical sensors with data analysis using a neural network are promising candidates for implementation in screening activities.

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