Artificial intelligence, machine learning, and radiomics in lung cancer classification

Year 2025, Volume: 11 Issue: 4, 821 - 827, 04.07.2025

Hatice Elmas , Aysun Hatice Uğuz , Abdullah Fahri Şahin , Fahriye Seçil Tecellioğlu , Lutz Welker

https://doi.org/10.18621/eurj.1660161

https://izlik.org/JA34TL39ET

Abstract

Lung cancer is a highly heterogeneous disease that presents significant challenges in accurate diagnosis and classification due to its diverse histological and molecular characteristics. Traditional diagnostic methods, while valuable, are often limited by invasiveness, subjectivity, and an inability to fully capture tumor complexity. Recent advancements in artificial intelligence (AI), machine learning, and radiomics have transformed the field, offering enhanced precision, efficiency, and objectivity in lung cancer classification. These technologies enable detailed analyses of imaging data, histopathological findings, and molecular profiles, facilitating improved subtype identification, outcome prediction, and personalized treatment strategies. Cytopathology remains a cornerstone of lung cancer diagnostics, particularly for small biopsies and cytological samples, which are often the only materials available in advanced stages. The integration of AI-driven methods into cytopathology and radiomics workflows has shown substantial potential to overcome the limitations of traditional approaches, reduce interobserver variability, and accelerate the diagnostic process. This review underscores the transformative role of AI and radiomics in lung cancer management, highlighting their synergy in advancing precision oncology. As ongoing research continues to refine these methodologies, the future of lung cancer care is poised for significant advancements, offering improved diagnostic accuracy, personalized therapies, and better patient outcomes.

Keywords

Lung cancer , heterogeneity , cytopathology , artificial intelligence

Ethical Statement

Ethical approval is not required for this study. There are no human or animal elements in the study. This review was carried out by a brief literature screening. Informed consent has not been collected specifically for the patient samples included in this study.

References

• 1. Lee MY, Giraddi RR, Tam WL. Cancer Stem Cells: Concepts, Challenges, and Opportunities for Cancer Therapy. Methods Mol Biol. 2019;2005:43-66. doi: 10.1007/978-1-4939-9524-0_4.
• 2. Yoo MH, Hatfield DL. The cancer stem cell theory: is it correct? Mol Cells. 2008;26(5):514-516.
• 3. Tomashefski JF, Cagle PT, Farver CF, Fraire. editors. Nonneoplastic Lung Disease. In: Dail and Hammar’s Pulmonary Pathology. 3rd ed., Springer New York:NY. 2008.
• 4. Zahir N, Sun R, Gallahan D, Gatenby RA, Curtis C. Characterizing the ecological and evolutionary dynamics of cancer. Nat Genet. 2020;52(8):759-767. doi: 10.1038/s41588-020-0668-4.
• 5. Marusyk A, Polyak K. Tumor heterogeneity: Causes and consequences. Biochim Biophys Acta. 2010;1805(1):105-117. doi: 10.1016/j.bbcan.2009.11.002.
• 6. Haag F, Hertel A, Tharmaseelan H, et al. Imaging-based characterization of tumoral heterogeneity for personalized cancer treatment. Rofo. 2024;196(3):262-272. doi: 10.1055/a-2175-4622.
• 7. Eun K, Ham SW, Kim H. Cancer stem cell heterogeneity: origin and new perspectives on CSC targeting. BMB Rep. 2017;50(3):117-125. doi: 10.5483/BMBRep.2017.50.3.222.
• 8. Chou TY, Dacic S, Wistuba I, et al; IASLC Pathology Committee. Differentiating Separate Primary Lung Adenocarcinomas From Intrapulmonary Metastases With Emphasis on Pathological and Molecular Considerations: Recommendations From the International Association for the Study of Lung Cancer Pathology Committee. J Thorac Oncol. 2025;20(3):311-330. doi: 10.1016/j.jtho.2024.11.016.
• 9. de Sousa VML, Carvalho L. Heterogeneity in Lung Cancer. Pathobiology. 2018;85(1-2):96-107. doi: 10.1159/000487440.
• 10. Tomasson MH. Cancer stem cells: A guide for skeptics. J Cell Biochem. 2009;106(5):745-749. doi: 10.1002/jcb.22050.
• 11. Biswas A, De S. Drivers of dynamic intratumor heterogeneity and phenotypic plasticity. Am J Physiol Physiol. 2021;320(5):C750-C760. doi: 10.1152/ajpcell.00575.2020.
• 12. Testa U, Castelli G, Pelosi E. Lung Cancers: Molecular Characterization, Clonal Heterogeneity and Evolution, and Cancer Stem Cells. Cancers (Basel). 2018;10(8):248. doi: 10.3390/cancers10080248.
• 13. WHO Reporting System for Lung Cytopathology. IAC-IARC-WHO Joint Editorial Board. IAC-IARC-WHO Cytopathology Reporting Systems. 1st ed., 2022.
• 14. Nicholson AG, Tsao MS, Beasley MB, et al. The 2021 WHO Classification of Lung Tumors: Impact of Advances Since 2015. J Thorac Oncol. 2022;17(3):362-387. doi: 10.1016/j.jtho.2021.11.003.
• 15. Elmas H, Önal B, Yilmaz S, Steurer S, Welker L. Optimizing Endoscopic Respiratory Diagnostics with Cytology: An Update on Touch Imprints with a Comparative Literature Review. Diagnostics. 2024;14(23):2750. doi: 10.3390/diagnostics14232750.
• 16. Dagogo-Jack I, Shaw AT. Tumour heterogeneity and resistance to cancer therapies. Nat Rev Clin Oncol. 2018;15(2):81-94. doi: 10.1038/nrclinonc.2017.166.
• 17. Wang S, Yang DM, Rong R, et al. Artificial Intelligence in Lung Cancer Pathology Image Analysis. Cancers (Basel). 2019;11(11):1673. doi :10.3390/cancers11111673.
• 18. Elmas H, Diel R, Önal B, Sauter G, Stellmacher F, Welker L. Recommendations for immunocytochemistry in lung cancer typing: An update on a resource‐efficient approach with large‐scale comparative Bayesian analysis. Cytopathology. 2022;33(1):65-76. doi: 10.1111/cyt.13051.
• 19. Ota T, Kirita K, Matsuzawa R, et al. Validity of using immunohistochemistry to predict treatment outcome in patients with non-small cell lung cancer not otherwise specified. J Cancer Res Clin Oncol. 2019;145(10):2495-2506. doi: 10.1007/s00432-019-03012-z.
• 20. Sokouti M, Sokouti B. Cancer genetics and deep learning applications for diagnosis, prognosis, and categorization. J Biol Methods. 2024;11(3):e99010017. doi: 10.14440/jbm.2024.0016.
• 21. Dunn B, Pierobon M, Wei Q. Automated Classification of Lung Cancer Subtypes Using Deep Learning and CT-Scan Based Radiomic Analysis. Bioengineering. 2023;10(6):690. doi: 10.3390/bioengineering10060690.
• 22. Davri A, Birbas E, Kanavos T, et al. Deep Learning for Lung Cancer Diagnosis, Prognosis and Prediction Using Histological and Cytological Images: A Systematic Review. Cancers (Basel). 2023;15(15):3981. doi: 10.3390/cancers15153981.
• 23. Sakamoto T, Furukawa T, Lami K, et al. A narrative review of digital pathology and artificial intelligence: focusing on lung cancer. Transl Lung Cancer Res. 2020;9(5):2255-2276. doi: 10.21037/tlcr-20-591.
• 24. Binczyk F, Prazuch W, Bozek P, Polanska J. Radiomics and artificial intelligence in lung cancer screening. Transl Lung Cancer Res. 2021;10(2):1186-1199. doi: 10.21037/tlcr-20-708.
• 25. Bębas E, Borowska M, Derlatka M, et al. Machine-learning-based classification of the histological subtype of non-small-cell lung cancer using MRI texture analysis. Biomed Signal Process Control. 2021;66:102446. doi: 10.1016/j.bspc.2021.102446.
• 26. Claudio Quiros A, Coudray N, Yeaton A, et al. Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unannotated pathology slides. Nat Commun. 2024;15(1):4596. doi: 10.1038/s41467-024-48666-7.
• 27. Brunetti A, Altini N, Buongiorno D, et al. A Machine Learning and Radiomics Approach in Lung Cancer for Predicting Histological Subtype. Appl Sci. 2022;12(12):5829. doi: 10.3390/app12125829.
• 28. Caiado F, Silva‐Santos B, Norell H. Intra‐tumour heterogeneity – going beyond genetics. FEBS J. 2016;283(12):2245-2258. doi: 10.1111/febs.13705.
• 29. Elmas H, Önal B, Steurer S, et al. Rapid Remote Online Evaluation in Endoscopic Diagnostics: An Analysis of Biopsy-Proven Respiratory Cytopathology. Diagnostics (Basel). 2023;13(21):3329. doi: 10.3390/diagnostics13213329.
• 30. Hanna MG, Parwani A, Sirintrapun SJ. Whole Slide Imaging: Technology and Applications. Adv Anat Pathol. 2020;27(4):251-259. doi: 10.1097/PAP.0000000000000273.
• 31. Hanna MG, Ardon O, Reuter VE, et al. Integrating digital pathology into clinical practice. Mod Pathol. 2022;35(2):152-164. doi: 10.1038/s41379-021-00929-0.
• 32. Sirintrapun SJ, Rudomina D, Mazzella A, et al. Robotic Telecytology for Remote Cytologic Evaluation without an On-site Cytotechnologist or Cytopathologist: A Tale of Implementation and Review of Constraints. J Pathol Inform. 2017;8:32. doi: 10.4103/jpi.jpi_26_17.
• 33. Hyun SH, Ahn MS, Koh YW, Lee SJ. A Machine-Learning Approach Using PET-Based Radiomics to Predict the Histological Subtypes of Lung Cancer. Clin Nucl Med. 2019;44(12):956-960. doi: 10.1097/RLU.0000000000002810.
• 34. Al-Thelaya H, Gilal NU, Alzubaidi M, et al. Applications of discriminative and deep learning feature extraction methods for whole slide image analysis: A survey. J Pathol Inform. 2023;14:100335. doi:10.1016/j.jpi.2023.100335.
• 35. Sakamoto T, Furukawa T, Lami K, et al. A narrative review of digital pathology and artificial intelligence: focusing on lung cancer. Transl Lung Cancer Res. 2020;9(5):2255-2276. doi:10.21037/tlcr-20-591.
• 36. Saw SN, Ng KH. Current challenges of implementing artificial intelligence in medical imaging. Phys Med. 2022;100:12-17. doi:10.1016/j.ejmp.2022.06.003.