Рак лёгкого остаётся одной из ведущих причин онкологической смертности в мире, при этом большинство случаев диагностируется на поздних стадиях, что обусловливает неблагоприятный прогноз. В связи с этим актуальным направлением исследований является разработка эффективных методов диагностики заболевания. Целью настоящей работы является обобщение и критический анализ современных данных о диагностической эффективности биомаркерной диагностики рака легкого с акцентом на мультимаркерные панели и использование моделей искусственного интеллекта, а также оценка факторов, ограничивающих их клиническое внедрение. Выполнен нарративный обзор публикаций, отобранных в базах PubMed, MEDLINE, Scopus и Web of Science за период с 2010 по 2025 год. Проанализированы более 60 клинических исследований и метаанализов, посвящённых циркулирующей опухолевой ДНК, микроРНК, белковым маркерам, аутоантителам и циркулирующим опухолевым клеткам, а также применению алгоритмов машинного обучения для диагностики рака лёгкого. В список литературы включено 27 источников. Установлено, что использование мультимаркерных панелей, особенно в сочетании с клиническими данными, обеспечивает более высокую диагностическую ценность по сравнению с отдельными биомаркерами. Показано, что применение методов машинного обучения способствует повышению точности диагностики и стратификации риска. В то же время выявлено, что преобладание ретроспективных исследований с внутренней валидацией ограничивает воспроизводимость результатов и их клиническую интерпретацию. Таким образом, анализ циркулирующих биомаркеров крови в сочетании с методами машинного обучения представляет перспективное направление неинвазивной диагностики рака лёгкого, однако его внедрение в клиническую практику и популяционный скрининг требует дальнейшего подтверждения в проспективных многоцентровых исследованиях.
Lung cancer remains one of the leading causes of cancer-related mortality worldwide, with the majority of cases diagnosed at advanced stages, resulting in a poor prognosis. In this regard, the development of effective diagnostic methods is a highly relevant area of research. The aim of this study is to summarize and critically analyze current data on the diagnostic performance of biomarker-based diagnostics for lung cancer, with a focus on multimarker panels and the use of artificial intelligence models, as well as to assess the factors limiting their clinical implementation. A narrative review of publications selected from PubMed, MEDLINE, Scopus, and Web of Science databases for the period from 2010 to 2025 was conducted. More than 60 clinical studies and meta-analyses were analyzed, focusing on circulating tumor DNA, microRNAs, protein markers, autoantibodies, and circulating tumor cells, as well as on the application of machine learning algorithms for lung cancer diagnosis. A total of 26 sources were included in the reference list. It was found that the use of multimarker panels, especially when combined with clinical data, provides higher diagnostic value compared to individual biomarkers. The application of machine learning methods was shown to improve diagnostic accuracy and risk stratification. At the same time, it was revealed that the predominance of retrospective studies with internal validation limits the reproducibility of results and their clinical interpretation. Thus, the analysis of circulating blood biomarkers in combination with machine learning methods represents a promising direction for non-invasive lung cancer diagnosis; however, its implementation in clinical practice and population screening requires further validation in prospective multicenter studies.
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