Rodionov, E. O., Chernov, V., Kulbakin, D. E., Obkhodskaya, E. V., Obkhodsky, A. V., Sachkov, V. I., & Miller, S. V. (2023). Sensor-Based Gas Analysis System for Lung Cancer Diagnosis. Voprosy Onkologii, 69(5), 855–862. https://doi.org/10.37469/0507-3758-2023-69-5-855-862

Abstract

Aim. To examine exhaled air samples from lung cancer patients and identify shared signal markers detectable through an artificial neural network that ensures uniformity in the sampling process using a sensor-based gas analysis system.

Materials and methods. During the study, samples of exhaled air were collected from 90 individuals aged 22 to 95 years for the period of 2020-2021. All participants in the study were divided into two groups: the test and the control group. The main group included patients with morphologically verified lung malignancies at stage T1-4N0-3M0-1 (n = 21). The control group included individuals with no clinical data of malignant pathology at the time of the study (based on medical history or previous examination data, if available). A gas analysis system capable of analyzing gas samples in two modes - direct inhalation into the chamber or the use of gas sample bags was developed. This study used remote sampling from bags due to the COVID-19 pandemic.

Results. The accuracy of lung cancer diagnosis was 85.71 %, sensitivity was 95.24 %, and specificity was 76.19 %. The lung cancer diagnosis accuracy reached 85.71 %, with sensitivity at 95.24 % and specificity at 76.19 %. Key attributes of our method comprise equipment mobility, adaptability to various medical facilities, simplicity, cost-effectiveness, and unhindered tumor screening potential in a broad population.

https://doi.org/10.37469/0507-3758-2023-69-5-855-862

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