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Literature review analyzes lung cancer screening methods and highlights the role of biomarkers and AI in detection.
The incidence and mortality from lung cancer is increasing worldwide, which makes screening for this disease not only relevant but also extremely necessary. This review analyzes articles published in the databases of the electronic scientific library e-library.ru, the electronic scientific library cyberleninka.ru, and PubMed. The most important articles in English and Russian on lung cancer screening are selected. Low-dose computed tomography is considered the reference screening method, the disadvantages of which may include incorrect results and human radiation. Research is being conducted to find out how effective low-dose computed tomography is in detecting lung cancer in people who do not smoke. Artificial intelligence can significantly improve the accuracy of lung cancer screening. Endobronchial optical coherence tomography is proposed as an alternative to low-dose computed tomography. However, large-scale clinical trials are required to confirm the effectiveness of endobronchial optical coherence tomography. To overcome these problems, it is recommended to use lung cancer biomarkers. Such biomarkers include: circulating tumor DNA and fragments of extracellular DNA, clonal hematopoietic mutations, carcinoembryonic antigen, carbohydrate antigen 125, blood metabolites, volatile organic compounds. The use of biomarkers will significantly improve the results of lung cancer screening and increase the detection of this dangerous disease at early stages. Modern forms and methods of lung cancer screening, based on the latest scientific achievements and best practices, can significantly increase its effectiveness.
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Radygina et al. (2026) studied this question.
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