Обзор современных методов диагностики рака легких с помощью радиогеномики

Проведен обзор отечественных и зарубежных публикаций, посвященных радиомике и радиогеномике с использованием данных компьютерной томографии и позитронно-эмиссионной томографии при раке легких. Рассматривались этапы методологии: получение изображений, сегментация опухоли, извлечение признаков, применение методов машинного обучения и искусственного интеллекта, а также подходы к валидации моделей. Радиогеномные модели демонстрируют различную прогностическую эффективность для разных мутаций. Наиболее высокие показатели точности получены при прогнозировании мутаций EGFR и ALK, в то время как для KRAS результаты остаются менее воспроизводимыми. Интеграция радиомики с клиническими и патоморфологическими данными, а также использование методов глубокого обучения значительно повышают точность прогнозов, однако сохраняются ограничения, связанные с интерпретируемостью моделей и отсутствием стандартизации. В целом радиогеномика представляет собой перспективный неинвазивный инструмент для стратификации риска, мониторинга ответа на лечение и поддержки клинических решений при немелкоклеточном раке легких. В то же время для внедрения в практику необходимы стандартизация методологии, крупные многоцентровые исследования и внешняя валидация.

1. Wang Y, Yang G, Gao X, et al. Subregion-specific 18F-FDG PET-CT radiomics for the pre-treatment prediction of EGFR mutation status in solid lung adenocarcinoma. Am J Nucl Med Mol Imaging. 2024; 14(2): 134–43. https://doi.org/10.62347/DDRR4923.

2. Hendriks LEL, Remon J, Faivre-Finn C, et al. Non-small-cell lung cancer. Nat Rev Dis Primers. 2024; 10(1): 71. https://doi.org/10.1038/s41572-024-00551-9.

3. Zuo Y, Liu Q, Li N, et al. Optimal 18F-FDG PET/CT radiomics model development for predicting EGFR mutation status and prognosis in lung adenocarcinoma: a multicentric study. Front Oncol. 2023; 13: 1173355. https://doi.org/10.3389/fonc.2023.1173355.

4. Schabath MB, Cote ML. Cancer progress and priorities: lung cancer. Cancer Epidemiol Biomarkers Prev. 2019; 28(10): 1563– 79. https://doi.org/10.1158/1055-9965.EPI-19-0221.

5. PDQ Adult Treatment Editorial Board. Non-small cell lung cancer treatment (PDQ®): health professional version. In: PDQ Cancer Information Summaries [Internet]. Bethesda (MD): National Cancer Institute (US); 2002. 2025 May 7.

6. Daylan AEC, Miao E, Tang K, et al. Lung cancer in never smokers: delving into epidemiology, genomic and immune landscape, prognosis, treatment, and screening. Lung. 2023; 201(6): 521–9. https://doi.org/10.1007/s00408-023-00661-3.

7. Mahajan A, Gurukrishna B, Wadhwa S, et al. Deep learning based automated epidermal growth factor receptor and anaplastic lymphoma kinase status prediction of brain metastasis in non-small cell lung cancer. Explor Target Antitumor Ther. 2023; 4(4): 657–68. https://doi.org/10.37349/etat.2023.00158.

8. Peng L, Lidan H, Cuicui Z, et al. DNA double-strand break repair capacity and its pathway gene variants predict the risk and prognosis of lung cancer. Lung Cancer. 2024; 192: 107831. https://doi.org/10.1016/j.lungcan.2024.107831.

9. Lee DH, Heo YR, Park WJ, Lee JH. A TERT-CLPTM1 locus polymorphism (rs401681) is associated with EGFR mutation in non-small cell lung cancer. Pathol Res Pract. 2017; 213(11): 1340–3. https://doi.org/10.1016/j.prp.2017.09.028.

10. Xu J, Shang Y, Cai F, et al. Correlation between lung cancer and the HHIP polymorphisms of chronic obstructive pulmonary disease (COPD) in the Chinese Han population. Genes Immun. 2019; 20(4): 273–80. https://doi.org/10.1038/s41435-018-0033-0.

11. Du H, Liu L, Liu H, et al. Genetic variants of DOCK2, EPHB1 and VAV2 in the natural killer cell-related pathway are associated with non-small cell lung cancer survival. Am J Cancer Res. 2021; 11(5): 2264–77.

12. Tiseo M, Giovannetti E, Tibaldi C, et al. Pharmacogenetic study of patients with advanced non-small cell lung cancer (NSCLC) treated with second-line pemetrexed or pemetrexedcarboplatin. Lung Cancer. 2012; 78(1): 92–9. https://doi.org/10.1016/j.lungcan.2012.07.009.

13. Lococo F, Ghaly G, Flamini S, et al. Artificial intelligence applications in personalizing lung cancer management: state of the art and future perspectives. J Thorac Dis. 2024; 16(10): 7096–110. https://doi.org/10.21037/jtd-24-244.

14. Odintsov I, Sholl LM. Prognostic and predictive biomarkers in non-small cell lung carcinoma. Pathology. 2024; 56(2): 192– 204. https://doi.org/10.1016/j.pathol.2023.11.006.

15. Shaverdashvili K, Burns TF. Advances in the treatment of KRASG12C mutant non-small cell lung cancer. Cancer. 2025; 131(Suppl 1): e35783. https://doi.org/10.1002/cncr.35783.

16. Hatano H, Yoshida T, Higashiyama R, et al. EGFR, TP53, and CUL3 triple mutation in non-small cell lung cancer and its potentially poor prognosis: a case report and database analysis. Thorac Cancer. 2025; 16(3): e15523. https://doi.org/10.1111/1759-7714.15523.

17. Liang X, Xu J, Jiang Y, et al. Concomitant genomic features stratify prognosis to patients with advanced EGFR mutant lung cancer. Mol Carcinog. 2024; 63(9): 1643–53. https://doi.org/10.1002/mc.23750.

18. VanderLaan PA, Rangachari D, Mockus SM, et al. Mutations in TP53, PIK3CA, PTEN and other genes in EGFR mutated lung cancers: correlation with clinical outcomes. Lung Cancer. 2017; 106: 17–21. https://doi.org/10.1016/j.lungcan.2017.01.011.

19. Marin E, Reyes R, Arcocha A, et al. Prospective evaluation of single nucleotide variants by two different technologies in paraffin samples of advanced non-small cell lung cancer patients. Diagnostics. 2020; 10(11): 902. https://doi.org/10.3390/diagnostics10110902.

20. Lian W, Ouyang Y. CT-guided aspiration lung biopsy for EGFR and ALK gene mutation analysis of lung cancer. Oncol Lett. 2017; 13(5): 3415–22. https://doi.org/10.3892/ol.2017.5921.

21. Raëth J, Malbert C, Pinsolle J, et al. Different pathways to lung cancer diagnosis in a real-life setting. Respir Med Res. 2024; 86: 101108. https://doi.org/10.1016/j.resmer.2024.101108.

22. Rizzo S, Petrella F, Buscarino V, et al. CT radiogenomic characterization of EGFR, K-RAS, and ALK mutations in nonsmall cell lung cancer. Eur Radiol. 2016; 26(1): 32–42. https://doi.org/10.1007/s00330-015-3814-0.

23. Jin D, Ni X, Tan Y, et al. Radiomics based on dual-layer spectral detector CT for predicting EGFR mutation status in non-small cell lung cancer. J Appl Clin Med Phys. 2025; 26(2): e14616. https://doi.org/10.1002/acm2.14616.

24. Cao P, Jia X, Wang X, et al. Deep learning radiomics for the prediction of epidermal growth factor receptor mutation status based on MRI in brain metastasis from lung adenocarcinoma patients. BMC Cancer. 2025; 25(1): 443. https://doi.org/10.1186/s12885-025-13823-8.

25. Xu K, Zhang C, Du T, et al. Progress of exosomes in the diagnosis and treatment of lung cancer. Biomed Pharmacother. 2021; 134: 111111. https://doi.org/10.1016/j.biopha.2020.111111.

26. Liu C, Cai Y, Mou S. Liquid biopsy in lung cancer: the role of circulating tumor cells in diagnosis, treatment, and prognosis. Biomed Pharmacother. 2024; 181: 117726. https://doi.org/10.1016/j.biopha.2024.117726.

27. Liu Y, Wang S, Xia H, et al. The potential applications of microparticles in the diagnosis, treatment, and prognosis of lung cancer. J Transl Med. 2022; 20(1): 404. https://doi.org/10.1186/s12967-022-03599-x.

28. Chen Y, Zitello E, Guo R, Deng Y. The function of LncRNAs and their role in the prediction, diagnosis, and prognosis of lung cancer. Clin Transl Med. 2021; 11(4): e367. https://doi.org/10.1002/ctm2.367.

29. Sheng M, Dong Z, Xie Y. Identification of tumor-educated platelet biomarkers of non-small-cell lung cancer. Onco Targets Ther. 2018; 11: 8143–51. https://doi.org/10.2147/OTT.S177384.

30. Rajaram R, Huang Q, Li RZ, et al. Recurrence-free survival in patients with surgically resected non-small cell lung cancer: a systematic literature review and meta-analysis. Chest. 2024; 165(5): 1260–70. https://doi.org/10.1016/j.chest.2023.11.042.

31. Belloni E, Veronesi G, Micucci C, et al. Genomic characterization of asymptomatic CT-detected lung cancers. Oncogene. 2011; 30(9): 1117–26. https://doi.org/10.1038/onc.2010.478.

32. Chen X, Kanhar GM, Hu S, et al. Occult metastases and survival of lung cancer by clinical diagnosis and CT screening: a simulation study. PLoS One. 2025; 20(1): e0313544. https://doi.org/10.1371/journal.pone.0313544.

33. Masse M, Chardin D, Tricarico P, et al. [18F]FDG-PET/CT atypical response patterns to immunotherapy in non-small cell lung cancer patients: long term prognosis assessment and clinical management proposal. Eur J Nucl Med Mol Imaging. 2024; 51(12): 3696–708. https://doi.org/10.1007/s00259-024-06794-8.

34. Ferro A, Bottosso M, Dieci MV, et al. Clinical applications of radiomics and deep learning in breast and lung cancer: a narrative literature review on current evidence and future perspectives. Crit Rev Oncol Hematol. 2024; 203: 104479. https://doi.org/10.1016/j.critrevonc.2024.104479.

35. Avanzo M, Stancanello J, Pirrone G, Sartor J. Radiomics and deep learning in lung cancer. Strahlenther Onkol. 2020; 196(10): 879–87. https://doi.org/10.1007/s00066-020-01625-9.

36. Gevaert O, Xu J, Hoang CD, et al. Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data – methods and preliminary results. Radiology. 2012; 264(2): 387–96. https://doi.org/10.1148/radiol.12111607.

37. Jin Y, Arimura H, Cui Y, et al. CT image-based biopsy to aid prediction of HOPX expression status and prognosis for nonsmall cell lung cancer patients. Cancers. 2023; 15(8): 2220. https://doi.org/10.3390/cancers15082220.

38. Abdurixiti M, Nijiati M, Shen R, et al. Current progress and quality of radiomic studies for predicting EGFR mutation in patients with non-small cell lung cancer using PET/CT images: a systematic review. Br J Radiol. 2021; 94(1122): 20201272. https://doi.org/10.1259/bjr.20201272.

39. He W, Huang W, Zhang L, et al. Radiogenomics: bridging the gap between imaging and genomics for precision oncology. MedComm. 2024; 5(9): e722. https://doi.org/10.1002/mco2.722.

40. Dovrou A, Bei E, Sfakianakis S, et al. Synergies of radiomics and transcriptomics in lung cancer diagnosis: a pilot study. Diagnostics. 2023; 13(4): 738. https://doi.org/10.3390/diagnostics13040738.

41. Lv X, Li Y, Wang B, et al. Multisequence MRI-based radiomics signature as potential biomarkers for differentiating KRAS mutations in non-small cell lung cancer with brain metastases. Eur J Radiol Open. 2024; 12: 100548. https://doi.org/10.1016/j.ejro.2024.100548.

42. Pan F, Feng L, Liu B, et al. Application of radiomics in diagnosis and treatment of lung cancer. Front Pharmacol. 2023; 14: 1295511. https://doi.org/10.3389/fphar.2023.1295511.

43. Chen B, Yang L, Zhang R, et al. Radiomics: an overview in lung cancer management – a narrative review. Ann Transl Med. 2020; 8(18): 1191. https://doi.org/10.21037/atm-20-4589.

44. Shiri I, Amini M, Nazari M, et al. Impact of feature harmonization on radiogenomics analysis: prediction of EGFR and KRAS mutations from non-small cell lung cancer PET/CT images. Comput Biol Med. 2022; 142: 105230. https://doi.org/10.1016/j.compbiomed.2022.105230.

45. Wang T, Yang X. Take CT, get PET free: AI-powered breakthrough in lung cancer diagnosis and prognosis. Cell Rep Med. 2024; 5(4): 101486. https://doi.org/10.1016/j.xcrm.2024.101486.

46. Chen W, Lin G, Feng Y, et al. Intratumoral and peritumoral CT radiomics in predicting anaplastic lymphoma kinase mutations and survival in patients with lung adenocarcinoma: a multicenter study. Cancer Imaging. 2025; 25(1): 35. https://doi.org/10.1186/s40644-025-00856-2.

47. Motta-Ribeiro G, Winkler T, Hashimoto S, Vidal Melo MF. Spatial heterogeneity of lung strain and aeration and regional inflammation during early lung injury assessed with PET/CT. Acad Radiol. 2018; 26(3): 313–25. https://doi.org/10.1016/j.acra.2018.02.028.

48. Yao X, Zhu Y, Huang Z, et al. Fusion of shallow and deep features from 18F-FDG PET/CT for predicting EGFR-sensitizing mutations in non-small cell lung cancer. Quant Imaging Med Surg. 2024; 14(8): 5460–72. https://doi.org/10.21037/qims-23-1028.

49. Chang C, Sun X, Wang G, et al. A machine learning model based on PET/CT radiomics and clinical characteristics predicts ALK rearrangement status in lung adenocarcinoma. Front Oncol. 2021; 11: 603882. https://doi.org/10.3389/fonc.2021.603882.

50. Minamimoto R, Jamali M, Gevaert O, et al. Prediction of EGFR and KRAS mutation in non-small cell lung cancer using quantitative 18F FDG-PET/CT metrics. Oncotarget. 2017; 8(32): 52792–801. https://doi.org/10.18632/oncotarget.17782.

51. Takamochi K, Mogushi K, Kawaji H, et al. Correlation of EGFR or KRAS mutation status with 18F-FDG uptake on PET-CT scan in lung adenocarcinoma. PLoS One. 2017; 12(4): e0175622. https://doi.org/10.1371/journal.pone.0175622.

52. Zhang YH, Liu XS, Gao Y, et al. SFXN1 as a potential diagnostic and prognostic biomarker of LUAD is associated with 18F-FDG metabolic parameters. Lung Cancer. 2024; 188: 107449. https://doi.org/10.1016/j.lungcan.2023.107449.

53. Fujimoto D, Hayashi H, Murotani K, et al. Prediction of prognosis in lung cancer using machine learning with interinstitutional generalizability: a multicenter cohort study (WJOG15121L: REAL-WIND). Lung Cancer. 2024; 194: 107896. https://doi.org/10.1016/j.lungcan.2024.107896.

54. Arif U, Zhang C, Hussain S, Abbasi AR. An efficient interpretable stacking ensemble model for lung cancer prognosis. Comput Biol Chem. 2024; 113: 108248. https://doi.org/10.1016/j.compbiolchem.2024.108248.

55. Zhou M, Leung A, Echegaray S, et al. Non-small cell lung cancer radiogenomics map identifies relationships between molecular and imaging phenotypes with prognostic implications. Radiology. 2018; 286(1): 307–15. https://doi.org/10.1148/radiol.2017161845.

56. Xiao Z, Cai H, Wang Y, et al. Deep learning for predicting epidermal growth factor receptor mutations of non-small cell lung cancer on PET/CT images. Quant Imaging Med Surg. 2023; 13(3): 1286–99. https://doi.org/10.21037/qims-22-760.

57. Song L, Zhu Z, Mao L, et al. Clinical, conventional CT and radiomic feature-based machine learning models for predicting ALK rearrangement status in lung adenocarcinoma patients. Front Oncol. 2020; 10: 369. https://doi.org/10.3389/fonc.2020.00369.

58. Wang F, Chen W, Chen F, et al. Risk stratification and overall survival prediction in extensive stage small cell lung cancer after chemotherapy with immunotherapy based on CT radiomics. Sci Rep. 2024; 14: 22659. https://doi.org/10.1038/s41598-024-73331-w.

59. Cords L, Engler S, Haberecker M, et al. Cancer-associated fibroblast phenotypes are associated with patient outcome in non-small cell lung cancer. Cancer Cell. 2024; 42(3): 369– 412e5. https://doi.org/10.1016/j.ccell.2023.12.021.

60. Piazzolla M, Parente P, Centra F, et al. Distribution of EGFR and KRAS mutations in patients with surgically resected nonsmall cell lung cancer from Southern Italy: real-life data from a single institution and literature review. Cancers. 2025; 17(5): 730. https://doi.org/10.3390/cancers17050730.

61. Johnson M, Garassino MC, Mok T, Mitsudomi T. Treatment strategies and outcomes for patients with EGFR-mutant non-small cell lung cancer resistant to EGFR tyrosine kinase inhibitors: focus on novel therapies. Lung Cancer. 2022; 170: 41–51. https://doi.org/10.1016/j.lungcan.2022.05.011.

62. Li S, Hu Y, Tian C, et al. Prediction of EGFR-TP53 genes comutations in patients with lung adenocarcinoma (LUAD) by 18F-FDG PET/CT radiomics. Clin Transl Oncol. 2025; 27(4): 1506–15. https://doi.org/10.1007/s12094-024-03685-0.

63. Gao JW, Rizzo S, Ma LH, et al. Pulmonary ground-glass opacity: computed tomography features, histopathology and molecular pathology. Transl Lung Cancer Res. 2017; 6(1): 68–75. https://doi.org/10.21037/tlcr.2017.01.02.

64. Sugano M, Shimizu K, Nakano T, et al. Correlation between computed tomography findings and epidermal growth factor receptor and KRAS gene mutations in patients with pulmonary adenocarcinoma. Oncol Rep. 2011; 26(5): 1205–11. https://doi.org/10.3892/or.2011.1412.

65. Zhou JY, Zheng J, Yu ZF, et al. Comparative analysis of clinicoradiologic characteristics of lung adenocarcinomas with ALK rearrangements or EGFR mutations. Eur Radiol. 2015; 25(5): 1257–66. https://doi.org/10.1007/s00330-014-3516-z.

66. Ma JW, Jiang X, Wang YM, et al. Dual-energy CT-based radiomics in predicting EGFR mutation status non-invasively in lung adenocarcinoma. Heliyon. 2024; 10(2): e24372. https://doi.org/10.1016/j.heliyon.2024.e24372.

67. Liu Y, Kim J, Balagurunathan Y, et al. Radiomic features are associated with EGFR mutation status in lung adenocarcinomas. Clin Lung Cancer. 2016; 17(5): 441–8.e6. https://doi.org/10.1016/j.cllc.2016.02.001.

68. Lu X, Li M, Zhang H, et al. A novel radiomic nomogram for predicting epidermal growth factor receptor mutation in peripheral lung adenocarcinoma. Phys Med Biol. 2020; 65(5): 055012. https://doi.org/10.1088/1361-6560/ab6f98.

69. Tu W, Sun G, Fan L, et al. Radiomics signature: a potential and incremental predictor for EGFR mutation status in NSCLC patients, comparison with CT morphology. Lung Cancer. 2019; 132: 28–35. https://doi.org/10.1016/j.lungcan.2019.03.025.

70. Prencipe B, Delprete C, Garolla E, et al. An explainable radiogenomic framework to predict mutational status of KRAS and EGFR in lung adenocarcinoma patients. Bioengineering. 2023; 10(7): 747. https://doi.org/10.3390/bioengineering10070747.

71. Ma N, Yang W, Wang Q, et al. Predictive value of 18F-FDG PET/ CT radiomics for EGFR mutation status in non-small cell lung cancer: a systematic review and meta-analysis. Front Oncol. 2024; 14: 1281572. https://doi.org/10.3389/fonc.2024.1281572.

72. Zuo Y, Liu L, Chang C, et al. Value of multi-center 18F-FDG PET/CT radiomics in predicting EGFR mutation status in lung adenocarcinoma. Med Phys. 2024; 51(7): 4872–87. https://doi.org/10.1002/mp.16947.

73. Fuster-Matanzo A, Picó Peris A, Bellvís Bataller F, et al. Prediction of oncogene mutation status in non-small cell lung cancer: a systematic review and meta-analysis with a special focus on artificial intelligence-based methods. Eur Radiol. 2025; Sep 8. https://doi.org/10.1007/s00330-025-11962-x.

74. Zhu L, He W, Xie C, et al. Efficacy and safety of immune checkpoint inhibitors for EGFR mutated non-small cell lung cancer: a network meta-analysis. Front Immunol. 2024; 15: 1512468. https://doi.org/10.3389/fimmu.2024.1512468.

75. Suchowski K, Pöschinger T, Rehemtulla A, et al. Noninvasive bioluminescence imaging of AKT kinase activity in subcutaneous and orthotopic NSCLC xenografts: correlation of AKT activity with tumor growth kinetics. Neoplasia. 2017; 19(4): 310–20. https://doi.org/10.1016/j.neo.2017.02.005.

76. Soria JC, Ohe Y, Vansteenkiste J, et al. Osimertinib in untreated EGFR-mutated advanced non-small-cell lung cancer. New Engl J Med. 2018; 378(2): 113–25. https://doi.org/10.1056/NEJMoa1713137.

77. Cheng Y, He Y, Li W, et al. Osimertinib versus comparator EGFR TKI as first-line treatment for EGFR-mutated advanced NSCLC: FLAURA China, a randomized study. Target Oncol. 2021; 16(2): 165–76. https://doi.org/10.1007/s11523-021-00794-6.

78. Felip E, Cho BC, Gutiérrez V, et al. Amivantamab plus lazertinib versus osimertinib in first-line EGFR-mutant advanced nonsmall-cell lung cancer with biomarkers of high-risk disease: a secondary analysis from MARIPOSA. Ann Oncol. 2024; 35(9): 805–16. https://doi.org/10.1016/j.annonc.2024.05.541.

79. Lim JU, Jung J, Kim YW, et al. Targeting the tumor microenvironment in EGFR-mutant lung cancer: opportunities and challenges. Biomedicines. 2025; 13(2): 470. https://doi.org/10.3390/biomedicines13020470.

80. Zeng A, Xiong Y, Zhang J, et al. Prognostic factors of resectable anaplastic lymphoma kinase (ALK)-rearranged non-small cell lung cancer (NSCLC) patients: a retrospective analysis based on a single center. Transl Lung Cancer Res. 2024; 13(1): 16–33. https://doi.org/10.21037/tlcr-23-606.

81. Bearz A, Bertoli E, Stanzione B, et al. EML4-ALK: update on ALK inhibitors. Int J Mol Sci. 2025; 26(1): 308. https://doi.org/10.3390/ijms26010308.

82. Munarriz BJ, Liu G, Khan S. A new ALK inhibitor is not a game changer, but an older one is! AME Clin Trials Rev. 2024; 2. https://doi.org/10.21037/actr-24-101.

83. Biswas B, Ghadyalpatil NS, Patil S, et al. Real world study of safety and efficacy of lorlatinib as second line and beyond in ALK-rearranged advanced non-small cell lung cancer patients in India – a multicentre chart review study (ROSELAND). Ecancermedicalscience. 2024; 18: 1667. https://doi.org/10.3332/ecancer.2024.1667.

84. Gadgeel S, Rahman A, Osaghae U, et al. P3. 12D. 07 divarasib versus adagrasib or sotorasib in pretreated KRAS G12C+ advanced or metastatic non-small cell lung cancer (NSCLC). J Thorac Oncol. 2024; 19(10): S349. https://doi.org/10.1016/j.jtho.2024.09.629.

85. Schöneck M, Rehbach N, Lotter-Becker L, et al. Machine learning-based radiomics analysis for identifying KRAS mutations in non-small-cell lung cancer from CT images: challenges, insights and implications. Life. 2025; 15(1): 83. https://doi.org/10.3390/life15010083.

86. Nair JKR, Saeed UA, McDougall CC, et al. Radiogenomic models using machine learning techniques to predict EGFR mutations in non-small cell lung cancer. Can Assoc Radiol J. 2021; 72(1): 109–19. https://doi.org/10.1177/0846537119899526.

87. Miyashita H, Kato S, Hong DS. KRAS G12C inhibitor combination therapies: current evidence and challenge. Front Oncol. 2024; 14: 1380584. https://doi.org/10.3389/fonc.2024.1380584.

88. Conroy R. Adagrasib shows meaningful activity in KRAS G12C-mutated solid tumors. Cancer Network, 2023. Available at: https://www.cancernetwork.com/view/adagrasib-shows-meaningful-activity-in-kras-g12c-mutated-solid-tumors (accessed 15.09.2025).

89. Stratmann JA, Althoff FC, Doebel P, et al. Sotorasib in KRAS G12C-mutated non-small cell lung cancer: a multicenter real-world experience from the compassionate use program in Germany. Eur J Cancer. 2024; 201: 113911. https://doi.org/10.1016/j.ejca.2024.113911.

90. Del Re M, Russo A, Rolfo C. Changing the way we treat KRAS G12C non-small cell lung cancers. AME Clin Trials Rev. 2024; 2. https://doi.org/10.21037/actr-23-12.

91. Chen LN, Keating C, Leb J, et al. Unusual presentation of ROS1 rearranged metastatic non-small cell lung cancer. Respir Med Case Rep. 2024; 51: 102091. https://doi.org/10.1016/j.rmcr.2024.102091.

92. Plodkowski AJ, Drilon A, Halpenny DF, et al. From genotype to phenotype: are there imaging characteristics associated with lung adenocarcinomas harboring RET and ROS1 rearrangements? Lung Cancer, 2015; 90(2): 321–5. https://doi.org/10.1016/j.lungcan.2015.09.018.

93. Spitaleri G, Trillo Aliaga P, Attili I, et al. Non-small-cell lung cancers (NSCLCs) harboring RET gene fusion, from their discovery to the advent of new selective potent RET inhibitors: “shadows and fogs”. Cancers. 2024; 16(16): 2877. https://doi.org/10.3390/cancers16162877.

94. Ninatti G, Kirienko M, Neri E, et al. Imaging-based prediction of molecular therapy targets in NSCLC by radiogenomics and AI approaches: a systematic review. Diagnostics. 2020; 10(6): 359. https://doi.org/10.3390/diagnostics10060359.

95. Chen YH, Lue KH, Chu SC, et al. The value of 18F-fluorodeoxyglucose positron emission tomography-based radiomics in non-small cell lung cancer. Tzu Chi Med J. 2024; 37(1): 17–27. https://doi.org/10.4103/tcmj.tcmj_124_24.

96. Xu X, Wu K, Zhao Y, Mei L. Stage I lung adenocarcinoma: the value of quantitative CT in differentiating pathological subtypes and predicting growth of subsolid nodules. Medicine. 2017; 96(16): e6595. https://doi.org/10.1097/MD.0000000000006595.

97. Liu ZT, Shen JT, Lei YJ, et al. Molecular subtyping based on immune cell marker genes predicts prognosis and therapeutic response in patients with lung adenocarcinoma. BMC Cancer. 2023; 23(1): 1141. https://doi.org/10.1186/s12885-023-11579-7.

98. Long Y, Zhao R, Du X. Diagnostic accuracy of MRI-based radiomic features for EGFR mutation status in non-small cell lung cancer patients with brain metastases: a meta-analysis. Front Oncol. 2025; 14: 1428929. https://doi.org/10.3389/fonc.2024.1428929.

99. Kludt C, Wang Y, Ahmad W, et al. Next-generation lung cancer pathology: development and validation of diagnostic and prognostic algorithms. Cell Rep Med. 2024; 5(9): 101697. https://doi.org/10.1016/j.xcrm.2024.101697.

100. Zhang Y, Wang G, Zhang Q, et al. The value of lung function assessment and testin expression detection in clinicopathological features and prognosis of NSCLC patients. J Cardiothorac Surg. 2024; 19(1): 223. https://doi.org/10.1186/s13019-024-02720-z.

101. Radiology Society of North America (RSNA). Radiologists central to burgeoning field of cancer radiogenomics. Available at: https://www.rsna.org/news/2024/august/radiology-and-cancer-radiogenomics (accessed 28.09.2025).

102. Anagnostopoulos AK, Gaitanis A, Gkiozos I, et al. Radiomics/ radiogenomics in lung cancer: basic principles and initial clinical results. Cancers. 2022; 14(7): 1657. https://doi.org/10.3390/cancers14071657.

103. Barry N, Kendrick J, Molin K, et al. Evaluating the impact of the Radiomics Quality Score: a systematic review and metaanalysis. Eur Radiol. 2025; 35(3): 1701–13. https://doi.org/10.1007/s00330-024-11341-y.

104. Mao F, Hu Z, Shi R, et al. Unravelling the prognostic and operative role of intratumoural microbiota in non-small cell lung cancer: insights from 16S rRNA and RNA sequencing. Clin Transl Med. 2025; 15(1): e70156. https://doi.org/10.1002/ctm2.70156.

105. Chi M, Liu J, Mei C, et al. TEAD4 functions as a prognostic biomarker and triggers EMT via PI3K/AKT pathway in bladder cancer. J Exp Clin Cancer Res. 2022; 41(1): 175. https://doi.org/10.1186/s13046-022-02377-3.

106. Pan Y, Shi L, Liu Y, et al. Multi-omics models for predicting prognosis in non-small cell lung cancer patients following chemotherapy and radiotherapy: a multi-center study. Radiother Oncol. 2025; 204: 110715. https://doi.org/10.1016/j.radonc.2025.110715.

107. Zhao B, Dercle L, Yang H, et al. Annotated test-retest dataset of lung cancer CT scan images reconstructed at multiple imaging parameters. Sci Data. 2024; 11(1): 1259. https://doi.org/10.1038/s41597-024-04085-3.