Differential diagnostics of non-small-cell and small-cell lung cancer: modern approaches and promising technologies

Cover Page

Cite item

Full Text

Abstract

Lung cancer represents a heterogeneous group of malignant neoplasms, among which two main forms can be distinguished — the non-small-cell and the small-cell lung cancer. These subtypes significantly differ by the histological, the molecular-genetic and the clinical characteristics, which defines the necessity of precise differential diagnostics for selecting the optimal treatment tactics. The review highlights the modern methods of diagnostics for the non-small-cell and the small-cell lung cancer, including the instrumental diagnostics, the histological and immunohistochemical examinations. Special attention was paid to the pros and cons of the promising non- and minimally invasive approaches, such as the analysis of circulating tumor cells, of the extracellular DNA, of the miRNA, of the marker proteins, of the volatile organic compounds and of the modern medical visualization (radiomics). Despite the significant progress in developing new diagnostic approaches, the problems remain that are related to the heterogeneity of tumors, the limited accessibility of the materials of small-cell lung cancer and the necessity of standardizing the new methods. The promising direction seems to the integration of multimodal approaches, combining the fluid biopsy, radiomics and the algorithms of machine learning, which can increase the precision of diagnostics and optimize the personalized treatment of the patients with various subtypes of lung cancer.

About the authors

Maria Yu. Konoshenko

Institute of Chemical Biology and Fundamental Medicine; Pulmonology Scientific Research Institute under Federal Medical and Biological Agency of Russsian Federation

Email: lacyjewelrymk@gmail.com
ORCID iD: 0000-0003-2925-9350
SPIN-code: 9374-8489

PhD

Russian Federation, Novosibirsk; Moscow

Ekaterina V. Shutko

Institute of Chemical Biology and Fundamental Medicine; Pulmonology Scientific Research Institute under Federal Medical and Biological Agency of Russsian Federation

Author for correspondence.
Email: katshutko@gmail.com
ORCID iD: 0009-0004-3004-8969
SPIN-code: 3627-2494
Russian Federation, 8 Lavrentyeva ave, Novosibirsk, 630090; Moscow

Olga E. Bryzgunova

Institute of Chemical Biology and Fundamental Medicine; Pulmonology Scientific Research Institute under Federal Medical and Biological Agency of Russsian Federation

Email: olga.bryzgunova@niboch.nsc.ru
ORCID iD: 0000-0003-3433-7261
SPIN-code: 9752-3241

PhD

Russian Federation, Novosibirsk; Moscow

Antonina A. Ilyushchenko

Pulmonology Scientific Research Institute under Federal Medical and Biological Agency of Russsian Federation

Email: Kdlmedwans@gmail.com
ORCID iD: 0009-0003-9068-5401
Russian Federation, Moscow

Yaroslava M. Danilova

Pulmonology Scientific Research Institute under Federal Medical and Biological Agency of Russsian Federation

Email: yaroslava.danilova.82@mail.ru
ORCID iD: 0009-0003-6679-9185
Russian Federation, Moscow

Stanislav D. Gorbunkov

Pulmonology Scientific Research Institute under Federal Medical and Biological Agency of Russsian Federation

Email: sdgorbunkov@mail.ru
ORCID iD: 0000-0002-8899-4294
SPIN-code: 7473-0530

MD, PhD, Assistant Professor

Russian Federation, Moscow

Kirill A. Zykov

Pulmonology Scientific Research Institute under Federal Medical and Biological Agency of Russsian Federation

Email: kirillaz@inbox.ru
ORCID iD: 0000-0003-3385-2632
SPIN-code: 6269-7990

MD, PhD, corresponding member of the Russian Academy of Sciences, Professor of the Russian Academy of Sciences

Russian Federation, Moscow

Pavel P. Laktionov

Institute of Chemical Biology and Fundamental Medicine; Pulmonology Scientific Research Institute under Federal Medical and Biological Agency of Russsian Federation

Email: lakt@1bio.ru
ORCID iD: 0000-0002-0866-0252
SPIN-code: 4114-3170

PhD

Russian Federation, Novosibirsk; Moscow

References

  1. International Agency for Research on Cancer [Internet]. WHO Classification of Tumours Editorial Board. 5th ed. Vol. 5. Thoracic tumours. Lyon; 2021. 565 p. ISBN: 13.978-92-832-4506-3
  2. Клинические рекомендации. Злокачественное новообразование бронхов и лёгкого. Кодирование по Международной статистической классификации болезней и проблем, связанных со здоровьем: C34. Ассоциация онкологов России, Российское общество клинической онкологии, 2022. [Clinical recommendations. Malignant neoplasm of the bronchi and lungs. Coding according to the International Statistical Classification of Diseases and Related Health Problems: C34. Association of Oncologists of Russia, Russian Society of Clinical Oncology; 2022. (In Russ.)]. Режим доступа: https://cr.minzdrav.gov.ru/preview-cr/30_4 Дата обращения: 15.07.2025.
  3. Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229–263. doi: 10.3322/caac.21834 EDN: FRJDQH
  4. Siegel RL, Miller KD, Fuchs HE, Jemal A. Erratum to “Cancer statistics, 2021”. CA Cancer J Clin. 2021;71(4):359. doi: 10.3322/caac.21669 EDN: CQUTZD
  5. Lu DN, Jiang Y, Zhang WC, et al. Lung cancer incidence in both sexes across global areas: data from 1978 to 2017 and predictions up to 2035. BMC Pulm Med. 2025;25(1):281. doi: 10.1186/s12890-025-03748-0
  6. GBD 2019 Tobacco Collaborators. Spatial, temporal, and demographic patterns in prevalence of smoking tobacco use and attributable disease burden in 204 countries and territories, 1990-2019: a systematic analysis from the Global Burden of Disease Study 2019. Lancet. 2021;397(10292):2337–2360. doi: 10.1016/S0140-6736(21)01282-4
  7. Ha SY, Choi SJ, Cho JH, et al. Lung cancer in never-smoker Asian females is driven by oncogenic mutations, most often involving EGFR. Oncotarget. 2015;6(7):5465–5474. doi: 10.18632/oncotarget.2925
  8. Islami F, Goding Sauer A, Miller KD, et al. Proportion and number of cancer cases and deaths attributable to potentially modifiable risk factors in the United States. CA Cancer J Clin. 2018;68(1):31–54. doi: 10.3322/caac.21440 EDN: YDXVDN
  9. Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–249. doi: 10.3322/caac.21660
  10. Злокачественные новообразования в России в 2022 году / под ред. А.Д. Каприна, В.В. Старинского, А.О. Шахзадовой, И.В. Лисичниковой. Москва, 2023. 275 с. [Kaprin AD, Starinsky VV, Shakhzadova AO, Lisichnikova IV, editors. Malignant neoplasms in Russia in 2022. Moscow; 2023. 275 p. (In Russ.)]
  11. Zhang Y, Vaccarella S, Morgan E, et al. Global variations in lung cancer incidence by histological subtype in 2020. Lancet Oncol. 2023;24(11):1206–1218. doi: 10.1016/S1470-2045(23)00444-8
  12. Nooreldeen R, Bach H. Current and future development in lung cancer diagnosis. Int J Mol Sci. 2021;22(16):8661. doi: 10.3390/ijms22168661
  13. ESMO Рекомендации для пациентов. Немелкоклеточный рак лёгкого (НМРЛ). 2019. 65 с. [ESMO Recommendations for patients. Non-small cell lung cancer (NSCLC). 2019. 65 p. (In Russ.)]. Режим доступа: https://www.rosoncoweb.ru/patients/guidelines/NSCLC/ Дата обращения: 15.07.2025.
  14. Nanavaty P, Alvarez MS, Alberts WM. Lung cancer screening: advantages, controversies, and applications. Cancer Control. 2014;21(1):9–14. doi: 10.1177/107327481402100102
  15. Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73(1):17–48. doi: 10.3322/caac.21763 EDN: SUTYDV
  16. George J, Lim JS, Jang SJ, et al. Comprehensive genomic profiles of small cell lung cancer. Nature. 2015;524(7563):47–53. doi: 10.1038/nature14664 EDN: UOSZYD
  17. Melosky B, Kambartel K, Häntschel M, et al. Worldwide prevalence of epidermal growth factor receptor mutations in non-small cell lung cancer: a meta-analysis. Mol Diagn Ther. 2022;26(1):7–18. doi: 10.1007/s40291-021-00563-1 EDN: IBRURO
  18. Bironzo P, Cani M, Jacobs F, et al. Real-world retrospective study of KRAS mutations in advanced non-small cell lung cancer. Cancer. 2023;129(11):1662–1671. doi: 10.1002/cncr.34731 EDN: OQEYMZ
  19. Lin HM, Wu Y, Yin Y, et al. Real-world ALK testing trends in patients with advanced non-small-cell lung cancer in the United States. Clin Lung Cancer. 2023;24(1):e39–e49. doi: 10.1016/j.cllc.2022.09.010 EDN: OKLRIF
  20. Yuan H, Zou Z, Hao X, et al. A real-world study: therapeutic outcomes of ROS1-positive advanced NSCLC. Thorac Cancer. 2025;16(9):e70086. doi: 10.1111/1759-7714.70086
  21. Papavassiliou KA, Sofianidi AA, Gogou VA, et al. P53 and Rb aberrations in small cell lung cancer. Int J Mol Sci. 2024;25(5):2479. doi: 10.3390/ijms25052479 EDN: LSBPUA
  22. Pelosof LC, Gerber DE. Paraneoplastic syndromes: an approach to diagnosis and treatment. Mayo Clin Proc. 2010;85(9):838–854. doi: 10.4065/mcp.2010.0099
  23. Hamilton G, Rath B, Stickler S. Significance of circulating tumor cells in lung cancer: a narrative review. Transl Lung Cancer Res. 2023;12(4):877–894. doi: 10.21037/tlcr-22-712 EDN: TNMTHA
  24. Брызгунова О.Е., Лактионов П.П. Формирование пула циркулирующих ДНК крови: источники, особенности строения и циркуляции // Биомедицинская химия. 2015. Т. 61, № 4. С. 409–426. [Bryzgunova OE, Laktionov PP. Generation of blood circulating dnas: sources, features of struction and circulation. Biomedical Chemistry. 2015;61(4):409–426]. doi: 10.18097/PBMC20156104409 EDN: UIJMTL
  25. Wang L, Dumenil C, Julié C, et al. Molecular characterization of circulating tumor cells in lung cancer: moving beyond enumeration. Oncotarget. 2017;8(65):109818–109835. doi: 10.18632/oncotarget.22651 EDN: YEBMGD
  26. Hou JM, Krebs MG, Lancashire L, et al. Clinical significance and molecular characteristics of circulating tumor cells and circulating tumor microemboli in patients with small-cell lung cancer. J Clin Oncol. 2012;30(6):525–532. doi: 10.1200/JCO.2010.33.3716
  27. Hou JM, Greystoke A, Lancashire L, et al. Evaluation of circulating tumor cells and serological cell death biomarkers in small cell lung cancer patients undergoing chemotherapy. Am J Pathol. 2009;175(2):808–816. doi: 10.2353/ajpath.2009.090078
  28. Devriese LA, Bosma AJ, van de Heuvel MM, et al. Circulating tumor cell detection in advanced non-small cell lung cancer patients by multi-marker QPCR analysis. Lung Cancer. 2012;75(2):242–247. doi: 10.1016/j.lungcan.2011.07.003
  29. Wu C, Hao H, Li L, et al. Preliminary investigation of the clinical significance of detecting circulating tumor cells enriched from lung cancer patients. J Thorac Oncol. 2009;4(1):30–36. doi: 10.1097/JTO.0b013e3181914125
  30. O’Shannessy DJ, Davis DW, Anderes K, Somers EB. Isolation of circulating tumor cells from multiple epithelial cancers with ApoStream® for detecting (or monitoring) the expression of folate receptor alpha. Biomark Insights. 2016;11:7–18. doi: 10.4137/BMI.S35075
  31. Vona G, Sabile A, Louha M, et al. Isolation by size of epithelial tumor cells: a new method for the immunomorphological and molecular characterization of circulating tumor cells. Am J Pathol. 2000;156(1):57–63. doi: 10.1016/S0002-9440(10)64706-2
  32. Brägelmann J, Böhm S, Guthrie MR, et al. Family matters: how MYC family oncogenes impact small cell lung cancer. Cell Cycle. 2017;16(16):1489–1498. doi: 10.1080/15384101.2017.1339849 EDN: YHVLWD
  33. Dammert MA, Brägelmann J, Olsen RR, et al. MYC paralog-dependent apoptotic priming orchestrates a spectrum of vulnerabilities in small cell lung cancer. Nat Commun. 2019;10(1):3485. doi: 10.1038/s41467-019-11371-x EDN: EMKAMU
  34. Peifer M, Fernández-Cuesta L, Sos ML, et al. Integrative genome analyses identify key somatic driver mutations of small-cell lung cancer. Nat Genet. 2012;44(10):1104–1110. doi: 10.1038/ng.2396
  35. Rudin CM, Durinck S, Stawiski EW, et al. Comprehensive genomic analysis identifies SOX2 as a frequently amplified gene in small-cell lung cancer. Nat Genet. 2012;44(10):1111–1116. doi: 10.1038/ng.2405
  36. Bass AJ, Watanabe H, Mermel CH, et al. SOX2 is an amplified lineage-survival oncogene in lung and esophageal squamous cell carcinomas. Nat Genet. 2009;41(11):1238–1242. doi: 10.1038/ng.465
  37. Ramos AH, Dutt A, Mermel C, et al. Amplification of chromosomal segment 4q12 in non-small cell lung cancer. Cancer Biol Ther. 2009;8(21):2042–2050. doi: 10.4161/cbt.8.21.9764
  38. Rekhtman N, Paik PK, Arcila ME, et al. Clarifying the spectrum of driver oncogene mutations in biomarker-verified squamous carcinoma of lung: lack of EGFR/KRAS and presence of PIK3CA/AKT1 mutations. Clin Cancer Res. 2012;18(4):1167–1176. doi: 10.1158/1078-0432.CCR-11-2109 EDN: PLFQRF
  39. Jibiki T, Nishimura H, Sengoku S, Kodama K. Regulations, open data and healthcare innovation: a case of MSK-IMPACT and its implications for better cancer care. Cancers (Basel). 2021;13(14):3448. doi: 10.3390/cancers13143448 EDN: XJYAIR
  40. De Alves RC, Meurer RT, Roehe AV. MYC amplification is associated with poor survival in small cell lung cancer: a chromogenic in situ hybridization study. J Cancer Res Clin Oncol. 2014;140(12):2021–2025. doi: 10.1007/s00432-014-1769-1 EDN: IVJKOC
  41. Wali A. FHIT: doubts are clear now. Sci World J. 2010;10:1142–1151. doi: 10.1100/tsw.2010.110
  42. Karachaliou N, Rosell R, Viteri S. The role of SOX2 in small cell lung cancer, lung adenocarcinoma and squamous cell carcinoma of the lung. Transl Lung Cancer Res. 2013;2(3):172-179. doi: 10.3978/j.issn.2218-6751.2013.01.01
  43. Ruiz-Patiño A, Castro CD, Ricaurte LM, et al. EGFR amplification and sensitizing mutations correlate with survival in lung adenocarcinoma patients treated with erlotinib (MutP-CLICaP). Targ Oncol. 2018;13(5):621–629. doi: 10.1007/s11523-018-0594-x EDN: MPQGSJ
  44. Yang M, Mandal E, Liu FX, et al. Non-small cell lung cancer with MET amplification: review of epidemiology, associated disease characteristics, testing procedures, burden, and treatments. Front Oncol. 2024;13:1241402. doi: 10.3389/fonc.2023.1241402 EDN: VJJBZH
  45. Chen Y, Huang Y, Gao X, et al. CCND1 amplification contributes to immunosuppression and is associated with a poor prognosis to immune checkpoint inhibitors in solid tumors. Front Immunol. 2020;11:1620. doi: 10.3389/fimmu.2020.01620 EDN: FYKKBZ
  46. Wang S, Lai JC, Li Y, et al. Loss of CDKN2A enhances the efficacy of immunotherapy in EGFR-mutant non-small cell lung cancer. Cancer Res. 2025;85(3):585–601. doi: 10.1158/0008-5472.CAN-24-1817 EDN: VORDYW
  47. Rolfo C, Mack P, Scagliotti GV, et al. Liquid biopsy for advanced NSCLC: a consensus statement from the international association for the study of lung cancer. J Thorac Oncol. 2021;16(10):1647–1662. doi: 10.1016/j.jtho.2021.06.017 EDN: VCZCQW
  48. Брызгунова О.Е., Лактионов П.П. Современные методы исследования метилирования внеклеточных ДНК // Молекулярная биология. 2017. Т. 51, № 2. С. 195–214. [Bryzgunova OE, Laktionov PP. Current methods of extracellular DNA methylation analysis. Molecular Biology. 2017;51(2):195–214]. doi: 10.7868/S0026898417010074 EDN: VXNTAJ
  49. Nunes SP, Diniz F, Moreira-Barbosa C, et al. Subtyping lung cancer using DNA methylation in liquid biopsies. J Clin Med. 2019;8(9):1500. doi: 10.3390/jcm8091500
  50. Toyooka S, Toyooka KO, Maruyama R, et al. DNA methylation profiles of lung tumors. Mol Cancer Ther. 2001;1(1):61–67.
  51. Heeke S, Gay CM, Estecio MR, et al. Tumor- and circulating-free DNA methylation identifies clinically relevant small cell lung cancer subtypes. Cancer Cell. 2024;42(2):225–237.e5. doi: 10.1016/j.ccell.2024.01.001 EDN: DOXQPA
  52. Poirier JT, Gardner EE, Connis N, et al. DNA methylation in small cell lung cancer defines distinct disease subtypes and correlates with high expression of EZH2. Oncogene. 2015;34(48):5869–5878. doi: 10.1038/onc.2015.38 EDN: VFAHJH
  53. Walter K, Holcomb T, Januario T, et al. DNA methylation profiling defines clinically relevant biological subsets of non-small cell lung cancer. Clin Cancer Res. 2012;18(8):2360–2373. doi: 10.1158/1078-0432.CCR-11-2635-T EDN: YCPGLL
  54. Gao X, Jia M, Zhang Y, et al. DNA methylation changes of whole blood cells in response to active smoking exposure in adults: a systematic review of DNA methylation studies. Clin Epigenetics. 2015;7(1):113. doi: 10.1186/s13148-015-0148-3 EDN: ZLFZQF
  55. Locke WJ, Guanzon D, Ma C, et al. DNA methylation cancer biomarkers: translation to the clinic. Front Genet. 2019;10:1150. doi: 10.3389/fgene.2019.01150 EDN: NLPPCO
  56. Cui S, Ye L, Wang H, et al. Use of superARMS EGFR mutation detection kit to detect EGFR in plasma cell-free DNA of patients with lung adenocarcinoma. Clin Lung Cancer. 2018;19(3):e313–e322. doi: 10.1016/j.cllc.2017.12.009
  57. Claus J, de Smet D, Breyne J, et al. Patient-centric thresholding of Cobas® EGFR mutation Test v2 for surveillance of EGFR-mutated metastatic non-small cell lung cancer. Sci Rep. 2024;14(1):18191. doi: 10.1038/s41598-024-68350-6 EDN: HRXGJZ
  58. Zhang Q, Zheng K, Gao Y, et al. Plasma exosomal miR-1290 and miR-29c-3p as diagnostic biomarkers for lung cancer. Heliyon. 2023;9(10):e21059. doi: 10.1016/j.heliyon.2023.e21059 EDN: PHAFOU
  59. Poroyko V, Mirzapoiazova T, Nam A, et al. Exosomal miRNAs species in the blood of small cell and non-small cell lung cancer patients. Oncotarget. 2018;9(28):19793–19806. doi: 10.18632/oncotarget.24857
  60. Valadi H, Ekström K, Bossios A, et al. Exosome-mediated transfer of mRNAs and microRNAs is a novel mechanism of genetic exchange between cells. Nat Cell Biol. 2007;9(6):654–659. doi: 10.1038/ncb1596
  61. Kumar MA, Baba SK, Sadida HQ, et al. Extracellular vesicles as tools and targets in therapy for diseases. Signal Transduct Target Ther. 2024;9(1):27. doi: 10.1038/s41392-024-01735-1 EDN: EPOTHG
  62. Lin J, Wang Y, Zou YQ, et al. Differential miRNA expression in pleural effusions derived from extracellular vesicles of patients with lung cancer, pulmonary tuberculosis, or pneumonia. Tumour Biol. 2016;37(12):15835–15845. doi: 10.1007/s13277-016-5410-6 EDN: WARYQA
  63. Müller Bark J, Kulasinghe A, Amenábar JM, Punyadeera C. Exosomes in cancer. Adv Clin Chem. 2021;101:1–40. doi: 10.1016/bs.acc.2020.06.006 EDN: IBIWAZ
  64. Casagrande GM, Silva MO, Reis RM, Leal LF. Liquid biopsy for lung cancer: up-to-date and perspectives for screening programs. Int J Mol Sci. 2023;24(3):2505. doi: 10.3390/ijms24032505 EDN: NARGMH
  65. Коношенко М.Ю., Лактионов П.П., Ланцухай Ю.А., и др. Малоинвазивная диагностика рака легкого на основе анализа внеклеточной микроРНК крови // Успехи молекулярной онкологии. 2023. Т. 10, № 2. С. 78–89. [Konoshenko MYu, Laktionov PP, Lancuhaj YuA, et al. Cell-free plasma miRNAs analysis for low invasive lung cancer diagnostics. Advances Molecular Oncology. 2023;10(2):78–89]. doi: 10.17650/2313-805X-2023-10-2-78-89 EDN: FSUWHT
  66. Davenport ML, Kulkarni A, Wang J, et al. miRNA-31 is a genomic biomarker of molecular heterogeneity in lung adenocarcinoma. Cancer Res. 2021;81(7):1788–1800. doi: 10.1158/0008-5472.CAN-20-2769
  67. Du L, Schageman JJ, Subauste MC, et al. miR-93, miR-98, and miR-197 regulate expression of tumor suppressor gene FUS1. Mol Cancer Res. 2010;8(6):873–883. doi: 10.1186/1756-9966-29-75
  68. Gilad S, Lithwick-Yanai G, Barshack I, et al. Multicenter validation of a microRNA-based assay for diagnosing indeterminate thyroid nodules. J Mol Diagn. 2012;14(5):517–524. doi: 10.1016/j.jmoldx.2012.03.004
  69. Powrózek T, Krawczyk P, Kowalski DM, et al. Plasma circulating microRNA-944 and microRNA-3662 as potential histologic type-specific early lung cancer biomarkers. Transl Res. 2015;166(4):315–323. doi: 10.1016/j.trsl.2015.05.009
  70. Abdipourbozorgbaghi M, Vancura A, Radpour R, Haefliger S. Circulating miRNA panels as a novel non-invasive diagnostic, prognostic, and potential predictive biomarkers in non-small cell lung cancer (NSCLC). Br J Cancer. 2024;131(8):1350–1362. doi: 10.1038/s41416-024-02831-3 EDN: DLZGNR
  71. Molina R, Filella X, Augé JM. ProGRP: a new biomarker for small cell lung cancer. Clin Biochem. 2004;37(7):505–511. doi: 10.1016/j.clinbiochem.2004.05.007
  72. Isgrò MA, Bottoni P, Scatena R. Neuron-specific enolase as a biomarker: biochemical and clinical aspects. Adv Exp Med Biol. 2015;867:125–143. doi: 10.1007/978-94-017-7215-0_9
  73. Dhanurdhar Y, Jagaty SK, Subhankar S, Behera D. Diagnostic and prognostic significance of serum biomarkers: serum amyloid A and CYFRA 21-1 in lung cancer. Int J Appl Basic Med Res. 2023;13(2):89–94. doi: 10.4103/ijabmr.ijabmr_639_22
  74. Grunnet M, Sorensen JB. Carcinoembryonic antigen (CEA) as tumor marker in lung cancer. Lung Cancer. 2012;76(2):138–143. doi: 10.1016/j.lungcan.2011.11.012
  75. Molina R, Auge JM, Escudero JM, et al. Mucins CA 125, CA 19.9, CA 15.3 and TAG-72.3 as tumor markers in patients with lung cancer: comparison with CYFRA 21-1, CEA, SCC and NSE. Tumour Biol. 2008;29(6):371–380. doi: 10.1159/000181180
  76. Bi H, Yin L, Fang W, et al. Association of CEA, NSE, CYFRA 21-1, SCC-Ag, and ProGRP with clinicopathological characteristics and chemotherapeutic outcomes of lung cancer. Lab Med. 2023;54(4):372–379. doi: 10.1093/labmed/lmac122 EDN: WNNQGG
  77. Zamay GS, Kolovskaya OS, Zukov RA, et al. Current and prospective protein biomarkers of lung cancer. Cancers (Basel). 2017;9(11):155. doi: 10.3390/cancers9110155 EDN: XNRJBS
  78. Sandfeld-Paulsen B, Jakobsen KR, Bæk R, et al. Exosomal proteins as diagnostic biomarkers in lung cancer. J Thorac Oncol. 2016;11(10):1701–1710. doi: 10.1016/j.jtho.2016.05.034
  79. Kondo K, Harada Y, Nakano M, et al. Identification of distinct N-glycosylation patterns on extracellular vesicles from small-cell and non-small-cell lung cancer cells. J Biol Chem. 2022;298(6):101950. doi: 10.1016/j.jbc.2022.101950 EDN: PZEZXF
  80. Papakonstantinou D, Roumeliotou A, Pantazaka E, et al. Integrative analysis of circulating tumor cells (CTCs) and exosomes from small-cell lung cancer (SCLC) patients: a comprehensive approach. Mol Oncol. 2025;19(7):2038–2055. doi: 10.1002/1878-0261.13765 EDN: SSRPXY
  81. Bao M, Huang Y, Lang Z, et al. Proteomic analysis of plasma exosomes in patients with non-small cell lung cancer. Transl Lung Cancer Res. 2022;11(7):1434–1452. doi: 10.21037/tlcr-22-467 EDN: TXPXHO
  82. Hu Q, Li K, Yang C, et al. The role of artificial intelligence based on PET/CT radiomics in NSCLC. Front Oncol. 2023;13:1133164. doi: 10.3389/fonc.2023.1133164
  83. Manafi-Farid R, Askari E, Shiri I, et al. [18F]FDG-PET/CT radiomics and artificial intelligence in lung cancer. Semin Nucl Med. 2022;52(6):759–780. doi: 10.1053/j.semnuclmed.2022.04.004 EDN: PWBTEU
  84. Safarian A, Mirshahvalad SA, Nasrollahi H, et al. Impact of [18F]FDG PET/CT radiomics and artificial intelligence in clinical decision making in lung cancer. Semin Nucl Med. 2025;55(2):156–166. doi: 10.1053/j.semnuclmed.2025.02.006 EDN: BILMAT
  85. Yang L, Xu P, Li M, et al. PET/CT radiomic features: a potential biomarker for EGFR mutation status and survival outcome prediction in NSCLC patients treated with TKIs. Front Oncol. 2022;12:894323. doi: 10.3389/fonc.2022.894323 EDN: LKPLSQ
  86. Guo Y, Song Q, Jiang M, et al. Histological subtypes classification of lung cancers on CT images using 3D deep learning and radiomics. Acad Radiol. 2021;28(9):e258–e266. doi: 10.1016/j.acra.2020.06.010 EDN: PVFKUC
  87. Shah RP, Selby HM, Mukherjee P, et al. Machine learning radiomics model for early identification of small-cell lung cancer on computed tomography scans. JCO Clin Cancer Inform. 2021;5:746–757. doi: 10.1200/CCI.21.00021 EDN: RVAUEI
  88. E L, Lu L, Li L, et al. Radiomics for classification of lung cancer histological subtypes based on nonenhanced computed tomography. Acad Radiol. 2019;26(9):1245–1252. doi: 10.1016/j.acra.2018.10.013
  89. Yang L, Yang J, Zhou X, et al. Development of a radiomics nomogram based on the 2D and 3D CT features to predict the survival of non-small cell lung cancer patients. Eur Radiol. 2019;29(5):2196–2206. doi: 10.1007/s00330-018-5770-y EDN: XDWKUS
  90. Saalberg Y, Wolff M. VOC breath biomarkers in lung cancer. Clin Chim Acta. 2016;459:5–9. doi: 10.1016/j.cca.2016.05.013
  91. Lv W, Shi W, Zhang Z, et al. Identification of volatile biomarkers for lung cancer from different histological sources: a comprehensive study. Anal Biochem. 2024;690:115527. doi: 10.1016/j.ab.2024.115527 EDN: WQNEIT
  92. Fan X, Zhong R, Liang H, et al. Exhaled VOC detection in lung cancer screening: a comprehensive meta-analysis. BMC Cancer. 2024;24(1):775. doi: 10.1186/s12885-024-12537-7 EDN: UHYNDS
  93. Jia Z, Zhang H, Ong CN, et al. Detection of lung cancer: concomitant volatile organic compounds and metabolomic profiling of six cancer cell lines. ACS Omega. 2018;3(5):5131–5140. doi: 10.1021/acsomega.7b02035
  94. Oguma T, Nagaoka T, Kurahashi M, et al. Clinical contributions of exhaled volatile organic compounds in the diagnosis of lung cancer. PLoS One. 2017;12(4):e0174802. doi: 10.1371/journal.pone.0174802
  95. Fuchs P, Loeseken C, Schubert JK, Miekisch W. Breath gas aldehydes as biomarkers of lung cancer. Int J Cancer. 2010;126(11):2663–2670. doi: 10.1002/ijc.24970 EDN: NYUUWF
  96. Steenhuis EG, Asmara OD, Kort S, et al. The electronic nose in lung cancer diagnostics: a systematic review and meta-analysis. ERJ Open Res. 2025;11(3):00723–2024. doi: 10.1183/23120541.00723-2024 EDN: QQRQLW
  97. Kort S, Tiggeloven MM, Brusse-Keizer M, et al. Multi-centre prospective study on diagnosing subtypes of lung cancer by exhaled-breath analysis. Lung Cancer. 2018;125:223–229. doi: 10.1016/j.lungcan.2018.09.022
  98. Monedeiro F, Monedeiro-Milanowski M, Ratiu IA, et al. Needle trap device-GC-MS for characterization of lung diseases based on breath VOC profiles. Molecules. 2021;26(6):1789. doi: 10.3390/molecules26061789 EDN: RVIHQL
  99. Amann A, Costello BD, Miekisch W, et al. The human volatilome: volatile organic compounds (VOCs) in exhaled breath, skin emanations, urine, feces and saliva. J Breath Res. 2014;8(3):034001. doi: 10.1088/1752-7155/8/3/034001 EDN: YARLEG
  100. Rondanelli M, Perdoni F, Infantino V, et al. Volatile organic compounds as biomarkers of gastrointestinal diseases and nutritional status. J Anal Methods Chem. 2019;2019:7247802. doi: 10.1155/2019/7247802

Supplementary files

Supplementary Files
Action
1. JATS XML
Download
2. Fig. 1. Classification of lung cancer [8].

Download (1MB)
Indexing metadata
3. Fig. 2. The methods of differential diagnostics of non-small-cell and small-cell lung cancer. NSCLC/SCLC — non-small-cell/small-cell lung cancer; CT — computed tomography; PET-CT — positron-emission tomography, combined with computed tomography.

Download (1MB)
Indexing metadata

Copyright (c) 2025 Eco-Vector

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Согласие на обработку персональных данных с помощью сервиса «Яндекс.Метрика»

1. Я (далее – «Пользователь» или «Субъект персональных данных»), осуществляя использование сайта https://journals.rcsi.science/ (далее – «Сайт»), подтверждая свою полную дееспособность даю согласие на обработку персональных данных с использованием средств автоматизации Оператору - федеральному государственному бюджетному учреждению «Российский центр научной информации» (РЦНИ), далее – «Оператор», расположенному по адресу: 119991, г. Москва, Ленинский просп., д.32А, со следующими условиями.

2. Категории обрабатываемых данных: файлы «cookies» (куки-файлы). Файлы «cookie» – это небольшой текстовый файл, который веб-сервер может хранить в браузере Пользователя. Данные файлы веб-сервер загружает на устройство Пользователя при посещении им Сайта. При каждом следующем посещении Пользователем Сайта «cookie» файлы отправляются на Сайт Оператора. Данные файлы позволяют Сайту распознавать устройство Пользователя. Содержимое такого файла может как относиться, так и не относиться к персональным данным, в зависимости от того, содержит ли такой файл персональные данные или содержит обезличенные технические данные.

3. Цель обработки персональных данных: анализ пользовательской активности с помощью сервиса «Яндекс.Метрика».

4. Категории субъектов персональных данных: все Пользователи Сайта, которые дали согласие на обработку файлов «cookie».

5. Способы обработки: сбор, запись, систематизация, накопление, хранение, уточнение (обновление, изменение), извлечение, использование, передача (доступ, предоставление), блокирование, удаление, уничтожение персональных данных.

6. Срок обработки и хранения: до получения от Субъекта персональных данных требования о прекращении обработки/отзыва согласия.

7. Способ отзыва: заявление об отзыве в письменном виде путём его направления на адрес электронной почты Оператора: info@rcsi.science или путем письменного обращения по юридическому адресу: 119991, г. Москва, Ленинский просп., д.32А

8. Субъект персональных данных вправе запретить своему оборудованию прием этих данных или ограничить прием этих данных. При отказе от получения таких данных или при ограничении приема данных некоторые функции Сайта могут работать некорректно. Субъект персональных данных обязуется сам настроить свое оборудование таким способом, чтобы оно обеспечивало адекватный его желаниям режим работы и уровень защиты данных файлов «cookie», Оператор не предоставляет технологических и правовых консультаций на темы подобного характера.

9. Порядок уничтожения персональных данных при достижении цели их обработки или при наступлении иных законных оснований определяется Оператором в соответствии с законодательством Российской Федерации.

10. Я согласен/согласна квалифицировать в качестве своей простой электронной подписи под настоящим Согласием и под Политикой обработки персональных данных выполнение мною следующего действия на сайте: https://journals.rcsi.science/ нажатие мною на интерфейсе с текстом: «Сайт использует сервис «Яндекс.Метрика» (который использует файлы «cookie») на элемент с текстом «Принять и продолжить».