Research advances in large language models for lung cancer diagnosis and treatment_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

REN Zhizhen ^1,2 , XI Yufan ² , ZHU Xu ² , LUO Yijie ² , HUANG Geting ³ , SONG Junqiao ³ , XU Xiuyuan ³ ,  CHEN Nan ¹ ,  PU Qiang ¹

1. Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, 610041, P. R. China;
2. West China School of Medicine, Sichuan University, Chengdu, 610041, P.R. China;
3. College of Computer Science, Sichuan University, Chengdu, 610065, P.R. China;

Corresponding author：

CHEN Nan, Email: puqiang100@163.com; PU Qiang, Email: dr.chennan@wchscu.cn

Keywords：

Large Language Models; Lung Cancer; Diagnosis and Treatment; Research Advances

DOI：

10.7507/1007-4848.202509019

Video：

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Abstract Full text Figures/Tables Video References Cited by

Lung cancer is a leading cause of cancer-related morbidity and mortality worldwide. Coupled with the substantial workload, the clinical management of lung cancer is challenged by the critical need to efficiently and accurately process increasingly complex medical information. In recent years, large language model technology has undergone explosive development, demonstrating unique advantages in handling complex medical data by leveraging its powerful natural language processing capabilities, and its application value in the field of lung cancer diagnosis and treatment is continuously increasing. This article reviews the research progress and applications of large language models in assisting with lung cancer diagnosis, tumor feature extraction, staging, analysis of disease progression and outcomes, treatment recommendations, clinical documentation generation, and patient medical education. We further analyze the current challenges and opportunities, and provide an outlook on the future development of specialized large language models for lung cancer.

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1. 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.
2. Guo L, Zhu C, Cai L, et al. Global burden of lung cancer in 2022 and projected burden in 2050. Chin Med J (Engl), 2024, 137(21): 2577-2582.
3. Gilson A, Safranek CW, Huang T, et al. How does ChatGPT perform on the United States Medical Licensing Examination (USMLE)? The implications of large language models for medical education and knowledge assessment. JMIR Med Educ, 2023, 9(1): e45312.
4. Van Veen D, Van Uden C, Blankemeier L, et al. Adapted large language models can outperform medical experts in clinical text summarization. Nat Med, 2024, 30(4): 1134-1142.
5. Li Z, Wu M, Zhang X, et al. Interrelationships of stress, burnout, anxiety, depression, quality of life and suicidality among Chinese residents under standardized residency training: a network analysis. Ann Med, 2024, 56(1): 2433030.
6. Low ZX, Yeo KA, Sharma VK, et al. Prevalence of burnout in medical and surgical residents: a meta-analysis. Int J Environ Res Public Health, 2019, 16(9): 1479.
7. Ma Y, Li Y, Lu W, et al. A cross-sectional study on knowledge, attitude and practice of osteoporosis prevention in patients with breast cancer undergoing endocrine therapy. Asia Pac J Oncol Nurs, 2025, 12: 100678.
8. Tajdar D, Lühmann D, Fertmann R, et al. Low health literacy is associated with higher risk of type 2 diabetes: a cross-sectional study in Germany. BMC Public Health, 2021, 21(1): 510.
9. Wang J, Chang Y, Li Z, et al. TechGPT-2.0: A large language model project to solve the task of knowledge graph construction. arXiv, 2024.
10. Hu EJ, Shen Y, Wallis P, et al. LoRA: Low-rank adaptation of large language models. arXiv, 2021.
11. 温萍华, 姜志杰, 蒋欢, 等. 睿宾Agent与主流大语言模型医学文献解读能力比较: 以食管癌为例. 中国胸心血管外科临床杂志, 2025, 32(10): 1404-1410.Wen PH, Jiang ZJ, Jiang H, et al. Ruibin Agent versus mainstream large language models: a comparative study on medical literature comprehension with esophageal cancer as a case study. Chin J Clin Thorac Cardiovasc Surg, 2025, 32(10): 1404-1410.
12. Singhal K, Tu T, Gottweis J, et al. Toward expert-level medical question answering with large language models. Nat Med, 2025, 31(3): 943-950.
13. Luo R, Sun L, Xia Y, et al. BioGPT: generative pre-trained transformer for biomedical text generation and mining. Brief Bioinform, 2022, 23(6): bbac409.
14. Yang X, Chen A, PourNejatian N, et al. A large language model for electronic health records. NPJ Digit Med, 2022, 5(1): 194.
15. Wang G, Yang G, Du Z, et al. ClinicalGPT: large language models finetuned with diverse medical data and comprehensive evaluation. arXiv, 2023.
16. Lan W, Wang W, Ji C, et al. ClinicalGPT-R1: pushing reasoning capability of generalist disease diagnosis with large language model. arXiv, 2025.
17. Ma T, Lin X, Li T, et al. Y-Mol: A multiscale biomedical knowledge-guided large language model for drug development. arXiv, 2024.
18. Unisound AI. https://shanhai.unisound.com/chat.
19. Chen J, Cai Z, Ji K, et al. HuatuoGPT-o1, towards medical complex reasoning with LLMs. arXiv, 2024.
20. Park HJ, Huh JY, Chae G, et al. Extraction of clinical data on major pulmonary diseases from unstructured radiologic reports using a large language model. PLoS One, 2024, 19(11): e0314136.
21. Li R, Mao S, Zhu C, et al. Enhancing pulmonary disease prediction using large language models with feature summarization and hybrid retrieval-augmented generation: multicenter methodological study based on radiology report. J Med Internet Res, 2025, 27: e72638.
22. American College of Radiology. Lung CT screening reporting & data system (Lung-RADS®) version 1.0. 2014. American College of Radiology website.
23. Singh R, Hamouda M, Chamberlin JH, et al. ChatGPT vs. Gemini: comparative accuracy and efficiency in Lung-RADS score assignment from radiology reports. Clin Imaging, 2025, 121: 110455.
24. Mao Y, Xu N, Wu Y, et al. Assessments of lung nodules by an artificial intelligence chatbot using longitudinal CT images. Cell Rep Med, 2025, 6(3): 101988.
25. Hu D, Liu B, Zhu X, et al. Zero-shot information extraction from radiological reports using ChatGPT. Int J Med Inform, 2024, 183: 105321.
26. Yamagishi Y, Nakamura Y, Hanaoka S, et al. Large language model approach for zero-shot information extraction and clustering of Japanese radiology reports: algorithm development and validation. JMIR Cancer, 2025, 11: e57275.
27. Bhattarai K, Oh IY, Sierra JM, et al. Leveraging GPT-4 for identifying cancer phenotypes in electronic health records: a performance comparison between GPT-4, GPT-3. 5-turbo, Flan-T5, Llama-3-8B, and spaCy's rule-based and machine learning-based methods. JAMIA Open, 2024, 7(3): ooae060.
28. Lee JE, Park KS, Kim YH, et al. Lung cancer staging using chest CT and FDG PET/CT free-text reports: comparison among three ChatGPT large language models and six human readers of varying experience. AJR Am J Roentgenol, 2024, 223(6): e2431696.
29. Matsuo H, Nishio M, Matsunaga T, et al. Exploring multilingual large language models for enhanced TNM classification of radiology report in lung cancer staging. Cancers (Basel), 2024, 16(21): 3621.
30. Tozuka R, Johno H, Amakawa A, et al. Application of NotebookLM, a large language model with retrieval-augmented generation, for lung cancer staging. Jpn J Radiol, 2025, 43(4): 706-712.
31. Huang J, Yang DM, Rong R, et al. A critical assessment of using ChatGPT for extracting structured data from clinical notes. NPJ Digit Med, 2024, 7(1): 106.
32. Cho H, Yoo S, Kim B, et al. Extracting lung cancer staging descriptors from pathology reports: a generative language model approach. J Biomed Inform, 2024, 157: 104720.
33. Kim S, Jang S, Kim B, et al. Automated pathologic TN classification prediction and rationale generation from lung cancer surgical pathology reports using a large language model fine-tuned with chain-of-thought: algorithm development and validation study. JMIR Med Inform, 2024, 12(1): e67056.
34. Elmarakeby HA, Trukhanov PS, Arroyo VM, et al. Empirical evaluation of language modeling to ascertain cancer outcomes from clinical text reports. BMC Bioinformatics, 2023, 24(1): 328.
35. Fink MA, Bischoff A, Fink CA, et al. Potential of ChatGPT and GPT-4 for data mining of free-text CT reports on lung cancer. Radiology, 2023, 308(3): e231362.
36. Zhu M, Lin H, Jiang J, et al. Large language model trained on clinical oncology data predicts cancer progression. NPJ Digit Med, 2025, 8(1): 397.
37. Yasaka K, Kanzawa J, Kanemaru N, et al. Fine-tuned large language model for extracting patients on pretreatment for lung cancer from a picture archiving and communication system based on radiological reports. J Imaging Inform Med, 2025, 38(1): 327-334.
38. Moore CL, Socrates V, Hesami M, et al. Using natural language processing to identify emergency department patients with incidental lung nodules requiring follow-up. Acad Emerg Med, 2025.
39. Zabaleta J, Aguinagalde B, Lopez I, et al. Utility of artificial intelligence for decision making in thoracic multidisciplinary tumor boards. J Clin Med, 2025, 14(2): 399.
40. Villar Álvarez F, Muguruza Trueba I, Belda Sanchis J, et al. Executive summary of the SEPAR recommendations for the diagnosis and treatment of non-small cell lung cancer. Arch Bronconeumol, 2016, 52(7): 378-388.
41. Gamble JL, Ferguson D, Yuen J, et al. Limitations of GPT-3. 5 and GPT-4 in applying Fleischner Society guidelines to incidental lung nodules. Can Assoc Radiol J, 2024, 75(2): 412-416.
42. Brown EDL, Shah HA, Donnelly BM, et al. Precision oncology in non-small cell lung cancer: a comparative study of contextualized ChatGPT models. Cureus, 2025, 17(3): e81097.
43. Benary M, Wang XD, Schmidt M, et al. Leveraging large language models for decision support in personalized oncology. JAMA Netw Open, 2023, 6(11): e2343689.
44. Hooshangnejad H, Huang G, Kelly K, et al. EXACT-Net: framework for EHR-guided lung tumor auto-segmentation for non-small cell lung cancer radiotherapy. Cancers (Basel), 2024, 16(23): 4097.
45. Wang Q, Wang Z, Li M, et al. A feasibility study of automating radiotherapy planning with large language model agents. Phys Med Biol, 2025, 70(7).
46. Dos Santos FC, Johnson LG, Madandola OO, et al. An example of leveraging AI for documentation: ChatGPT-generated nursing care plan for an older adult with lung cancer. J Am Med Inform Assoc, 2024, 31(9): 2089-2096.
47. Hartung MP, Bickle IC, Gaillard F, et al. How to create a great radiology report. Radiographics, 2020, 40(6): 1658-1670.
48. Liu Z, Zhong T, Li Y, et al. Evaluating large language models for radiology natural language processing. arXiv, 2023.
49. Hu D, Zhang S, Liu Q, et al. Large language models in summarizing radiology report impressions for lung cancer in Chinese: evaluation study. J Med Internet Res, 2025, 27: e65547.
50. Sun Z, Ong H, Kennedy P, et al. Evaluating GPT4 on impressions generation in radiology reports. Radiol, 2023, 307(5): e231259.
51. Li Y, Li F, Hong N, et al. A comparative study of recent large language models on generating hospital discharge summaries for lung cancer patients. J Biomed Inform, 2025, 168: 104867.
52. 石锐, 郑兵, 姚巡, 等. "华西黉医"大模型构建与应用. 中国胸心血管外科临床杂志, 2025, 32(5): 587-593.Shi R, Zheng B, Yao X, et al. Construction and application of the "Huaxi Hongyi" large medical model. Chin J Clin Thorac Cardiovasc Surg, 2025, 32(5): 587-593.
53. Wang CK, Ke CR, Huang MS, et al. Using large language models for efficient cancer registry coding in the real hospital setting: a feasibility study. Pac Symp Biocomput, 2025, 30: 121-137.
54. Rahsepar AA, Tavakoli N, Kim GHJ, et al. How AI responds to common lung cancer questions: ChatGPT vs Google Bard. Radiology, 2023, 307(5): e230922.
55. Gencer A. Readability analysis of ChatGPT's responses on lung cancer. Sci Rep, 2024, 14(1): 17234.
56. Haver HL, Lin CT, Sirajuddin A, et al. Use of ChatGPT, GPT-4, and Bard to improve readability of ChatGPT's answers to common questions about lung cancer and lung cancer screening. AJR Am J Roentgenol, 2023, 221(5): 701-704.
57. Rogasch JMM, Metzger G, Preisler M, et al. ChatGPT: can you prepare my patients for [18F]FDG PET/CT and explain my reports? J Nucl Med, 2023, 64(12): 1876-1879.
58. Jeblick K, Schachtner B, Dexl J, et al. ChatGPT makes medicine easy to swallow: an exploratory case study on simplified radiology reports. Eur Radiol, 2024, 34(5): 2817-2825.
59. Lyu Q, Tan J, Zapadka ME, et al. Translating radiology reports into plain language using ChatGPT and GPT-4 with prompt learning: results, limitations, and potential. Vis Comput Ind Biomed Art, 2023, 6: 9.
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