Expert consensus on the application of artificial intelligence in lung cancer screening, diagnosis, and treatment (2026 edition)_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

 ZHONG Wenzhao ¹ , WANG Haibo ² , HU Yi ³ , ZHANG Hao ⁴ , DAI Jigang ⁵ , FAN Junqiang ⁶ , QIAO Guibin ⁷ , YANG Fan ⁸ , HU Jian ⁹ , TAN Fengwei ¹⁰ , YANG Xuening ¹ , PU Qiang ¹¹ , CHEN Zihao ¹ , TIAN Hongxia ¹ , LIU Lunxu ¹¹ , LI Hecheng ¹² , YAN Xiaolong ¹³ , YU Zongyang ¹⁴ , QIU Zhenbin ¹ , SUN Yihua ¹⁵ , HU Jing ¹⁶ , SHI Yuhang ¹⁷ , GUO Zhifei ¹⁸ , ZHANG Peng ¹⁹ , CHEN Kezhong ⁸ , GAO Shugeng ¹⁰ , WU Yilong ¹ , on behalf of the Thoracic Surgery Branch of Chinese Medical Doctor Association , the Non-small Cell Lung Cancer Committee of Chinese Anti-Cancer Association , the Thoracic Surgery Committee of Chinese Research Hospital Association , the Pulmonary Oncology Branch of Guangdong Medical Association , and the Research Group of Noncommunicable Chronic Diseases-National Science and Technology Major Project (Development and Clinical Research of AI-based Precise Identification Technology for Pulmonary Nodules)

1. Department of Pulmonary Surgery, Guangdong Provincial People's Hospital, Guangdong Lung Cancer Institute, Guangdong Provincial Academy of Medical Sciences, Southern Medical University, Guangzhou, 510080, P. R. China;
2. Research Centre of Big Data and Artificial Intelligence in Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, P. R. China;
3. Department of Oncology, The Fifth Medical Center of PLA General Hospital, Beijing, 100039, P. R. China;
4. Department of Thoracic Surgery, Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221006, Jiangsu, P. R. China;
5. Department of Thoracic Surgery, The Second Affiliated Hospital of Army Medical University (Third Military Medical University), Chongqing, 630037, P. R. China;
6. Department of Thoracic Surgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, P. R. China;
7. Department of Thoracic Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, P. R. China;
8. Department of Thoracic Surgery, Peking University People's Hospital, Beijing, 100044, P. R. China;
9. Department of Thoracic Surgery, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310003, P. R. China;
10. Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, P. R. China;
11. Department of Thoracic Surgery, West China Hospital of Sichuan University, Chengdu, 610041, P. R. China;
12. Department of Thoracic Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, P. R. China;
13. Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, Xi'an, 710038, P. R. China;
14. Department of Respiratory Medicine, The 900th Hospital of Joint Logistics Support Force, PLA, Fuzhou, 350000, P. R. China;
15. Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, P. R. China;
16. Shukun Technology Company Limited, Beijing, 100039, P. R. China;
17. Shanghai United Imaging Hi-Tech Research Institute Co., Ltd. Shanghai, 200025, P. R.China;
18. Guangdong OptoMedic Technologies, Inc, Foshan, 528000, Guangdong, P. R. China;
19. Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Shanghai, 200433, P. R. China;

Corresponding?author：

ZHONG Wenzhao, Email: zhongwenzhao@gdph.org.cn

Keywords：

Artificial intelligence; lung cancer; screening; diagnosis; treatment; deep learning; precision medicine; radiomics; multimodal data

DOI：

10.7507/1007-4848.202603041

Video：

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Abstract Full text Figures/Tables Video References Cited by

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

1.	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.
2.	Aberle DR, Adams AM, Berg CD, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med, 2011, 365(5): 395-409.
3.	Huang D, Li Z, Jiang T, et al. Artificial intelligence in lung cancer: current applications, future perspectives, and challenges. Front Oncol, 2024, 14: 1486310.
4.	Coudray N, Ocampo PS, Sakellaropoulos T, et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat Med, 2018, 24(10): 1559-1567.
5.	Arun S, Grosheva M, Kosenko M, et al. Systematic scoping review of external validation studies of AI pathology models for lung cancer diagnosis. NPJ Precis Oncol, 2025, 9(1): 166.
6.	Ji G, Luo W, Zhu Y, et al. Early-stage lung cancer detection via thin-section low-dose CT reconstruction combined with AI in non-high risk populations: a large-scale real-world retrospective cohort study. Precis Clin Med, 2025, 8(2): pbaf007.
7.	Yang X, Chu XP, Huang S, et al. A novel image deep learning-based sub-centimeter pulmonary nodule management algorithm to expedite resection of the malignant and avoid over-diagnosis of the benign. Eur Radiol, 2024, 34(3): 2048-2061.
8.	Chen M, Copley SJ, Viola P, et al. Radiomics and artificial intelligence for precision medicine in lung cancer treatment. Semin Cancer Biol, 2023, 93: 97-113.
9.	Geppert J, Asgharzadeh A, Brown A, et al. Software using artificial intelligence for nodule and cancer detection in CT lung cancer screening: systematic review of test accuracy studies. Thorax, 2024, 79(11): 1040-1049.
10.	楊卓文, 鄭智中, 李斌, 等. 人工智能系統在預測肺結節良惡性和病理分型中的臨床應用. 中國胸心血管外科臨床雜志, 2025, 32(8): 1086-1095.Yang ZW, Zheng ZZ, Li B, et al. Clinical application of artificial intelligence system in predicting benign and malignant pulmonary nodules and pathological classification. Chin J Clin Thorac Cardiovasc Surg, 2025, 32(8): 1086-1095.
11.	Zhang C, Sun X, Dang K, et al. Toward an expert level of lung cancer detection and classification using a deep convolutional neural network. Oncologist, 2019, 24(9): 1159-1165.
12.	He J, Wang H, Feng Y, et al. Integrative fragmentomic and mutational signature profile of plasma cfDNA for early lung cancer detection. NPJ Precis Oncol, 2026 [Epub ahead of print].
13.	Wang H, Huang H, Li F, et al. Risk-stratified classification of pulmonary nodule malignancy via a machine learning model integrating imaging and cell-free DNA: a model development and validation study (DECIPHER-NODL). Lancet Reg Health West Pac, 2025, 56: 101730.
14.	He J, Wang B, Tao J, et al. Accurate classification of pulmonary nodules by a combined model of clinical, imaging, and cell-free DNA methylation biomarkers: a model development and external validation study. Lancet Digit Health, 2023, 5(10): e647-e656.
15.	Liang W, Chen Z, Li C, et al. Accurate diagnosis of pulmonary nodules using a noninvasive DNA methylation test. J Clin Invest, 2021, 131(10): e145973.
16.	Pan L, Luo J, Nie C, et al. A deep learning framework integrating tumor microenvironmental features accurately predicts multiple driver gene mutations in lung cancer pathology images. Cancer Res, 2026, 86(5): 1319-1337.
17.	Madani MH, Riess JW, Brown LM, et al. Imaging of lung cancer. Curr Probl Cancer, 2023, 47(2): 100966.
18.	薛保, 周俊杰, 邵偉. 基于可變形注意力和多尺度多實例學習的全切片病理圖像分類方法. 數據采集與處理, 2026, 41(1): 231-243.Xue B, Zhou JJ, Shao W. Whole slide image classification based on deformable attention and multi-scale multiple instance learning. J Data Acquis Process, 2026, 41(1): 231-243.
19.	Kim D, Lee J, Jung M, et al. Whole slide image-level classification of malignant effusion cytology using clustering-constrained attention multiple instance learning. Lung Cancer, 2025, 204: 108552.
20.	Yang H, Chen L, Cheng Z, et al. Deep learning-based six-type classifier for lung cancer and mimics from histopathological whole slide images: a retrospective study. BMC Med, 2021, 19(1): 80.
21.	Pan X, AbdulJabbar K, Coelho-Lima J, et al. The artificial intelligence-based model ANORAK improves histopathological grading of lung adenocarcinoma. Nat Cancer, 2024, 5(2): 347-363.
22.	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, 2026, 36(3): 2157-2185.
23.	Zhao Y, Xiong S, Ren Q, et al. Deep learning using histological images for gene mutation prediction in lung cancer: a multicentre retrospective study. Lancet Oncol, 2025, 26(1): 136-146.
24.	Chen ZH, Chu XP, Zhang JT, et al. The regularity of anatomical variations of dominant pulmonary segments in the right upper lobe. Thorac Cancer, 2023, 14(5): 462-469.
25.	Chen X, Dai C, Peng M, et al. Artificial intelligence driven 3D reconstruction for enhanced lung surgery planning. Nat Commun, 2025, 16(1): 4086.
26.	Etienne H, Hamdi S, Le Roux M, et al. Artificial intelligence in thoracic surgery: past, present, perspective and limits. Eur Respir Rev, 2020, 29(157): 190054.
27.	Niu G, Guan Y, Zhang Y, et al. A prospective multicenter trial of deep learning auto-segmentation for organs at risk in thoracic radiotherapy. Nat Commun, 2026 [Epub ahead of print].
28.	Yu L, Ni Q, Wang B, et al. Multicenter study on the versatility and adoption of AI-driven automated radiotherapy planning across cancer types. Nat Commun, 2025, 17(1): 867.
29.	Saad MB, Showkatian E, Verma V, et al. Causal AI-based clinical and radiomic analysis for optimizing patient selection in combined immunotherapy and SABR in early-stage NSCLC: a secondary analysis of the phaseⅡ I-SABR trial. J Immunother Cancer, 2025, 13(10): e013074.
30.	Kashyap M, Wang X, Panjwani N, et al. Automated deep learning-based detection and segmentation of lung tumors at CT. Radiology, 2025, 314(1): e233029.
31.	Xing W, Gao W, Lv X, et al. Artificial intelligence predicts lung cancer radiotherapy response: a meta-analysis. Artif Intell Med, 2023, 142: 102585.
32.	范雷, 丁利雅, 姜支農. 人工智能在肺癌的生物學標志物識別及免疫治療中的應用. 臨床與實驗病理學雜志, 2025, 41(6): 782-788.Fan L, Ding LY, Jiang ZN. Application of artificial intelligence in biomarker identification and immunotherapy of lung cancer. Chin J Clin Exp Pathol, 2025, 41(6): 782-788.
33.	Chen RJ, Lu MY, Wang J, et al. Pathomic fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis. IEEE Trans Med Imaging, 2022, 41(6): 1365-1379.
34.	He B, Dong D, She Y, et al. Predicting response to immunotherapy in advanced non-small-cell lung cancer using tumor mutational burden radiomic biomarker. J Immunother Cancer, 2020, 8(2): e000550.
35.	Stone E, Rankin N, Kerr S, et al. Does presentation at multidisciplinary team meetings improve lung cancer survival? Findings from a consecutive cohort study. Lung Cancer, 2018, 124: 199-204.
36.	Jensen K, Soguero-Ruiz C, Oyvind Mikalsen K, et al. Analysis of free text in electronic health records for identification of cancer patient trajectories. Sci Rep, 2017, 7: 46426.
37.	Wang Q, Mao Z, Wang B, et al. Knowledge graph embedding: a survey of approaches and applications. IEEE Trans Knowl Data Eng, 2017, 29(12): 2724-2743.
38.	Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med, 2019, 25(1): 44-56.
39.	Kather JN, Heij LR, Grabsch HI, et al. Pan-cancer image-based detection of clinically actionable genetic alterations. Nat Cancer, 2020, 1(8): 789-799.
40.	Chen S, Yang Y. OA04.02 Implementation of large language model and GraphRAG-assisted decision support for multidisciplinary team cancer care. J Thorac Oncol, 2025, 20(10): S16-S17.
41.	Qiu ZB, Li J, Dou S, et al. Quantifying early-stage lung adenocarcinoma progression with a radiomic trajectory. NPJ Digit Med, 2025, 8(1): 664.
42.	Goldstein BA, Navar AM, Pencina MJ, et al. Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review. J Am Med Inform Assoc, 2017, 24(1): 198-208.
43.	Harutyunyan H, Khachatrian H, Kale DC, et al. Multitask learning and benchmarking with clinical time series data. Sci Data, 2019, 6(1): 96.
44.	Ye G, Wei Z, Han C, et al. AI-derived longitudinal and multi-dimensional CT classifier for non-small cell lung cancer to optimize neoadjuvant chemoimmunotherapy decision: a multicentre retrospective study. EClinicalMedicine, 2025, 89: 103551.
45.	Sherman RE, Anderson SA, Dal Pan GJ, et al. Real-world evidence-what is it and what can it tell us? N Engl J Med, 2016, 375(23): 2293-2297.
46.	Booth CM, Karim S, Mackillop WJ. Real-world data: towards achieving the achievable in cancer care. Nat Rev Clin Oncol, 2019, 16(5): 312-325.

1. 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.
2. Aberle DR, Adams AM, Berg CD, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med, 2011, 365(5): 395-409.
3. Huang D, Li Z, Jiang T, et al. Artificial intelligence in lung cancer: current applications, future perspectives, and challenges. Front Oncol, 2024, 14: 1486310.
4. Coudray N, Ocampo PS, Sakellaropoulos T, et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat Med, 2018, 24(10): 1559-1567.
5. Arun S, Grosheva M, Kosenko M, et al. Systematic scoping review of external validation studies of AI pathology models for lung cancer diagnosis. NPJ Precis Oncol, 2025, 9(1): 166.
6. Ji G, Luo W, Zhu Y, et al. Early-stage lung cancer detection via thin-section low-dose CT reconstruction combined with AI in non-high risk populations: a large-scale real-world retrospective cohort study. Precis Clin Med, 2025, 8(2): pbaf007.
7. Yang X, Chu XP, Huang S, et al. A novel image deep learning-based sub-centimeter pulmonary nodule management algorithm to expedite resection of the malignant and avoid over-diagnosis of the benign. Eur Radiol, 2024, 34(3): 2048-2061.
8. Chen M, Copley SJ, Viola P, et al. Radiomics and artificial intelligence for precision medicine in lung cancer treatment. Semin Cancer Biol, 2023, 93: 97-113.
9. Geppert J, Asgharzadeh A, Brown A, et al. Software using artificial intelligence for nodule and cancer detection in CT lung cancer screening: systematic review of test accuracy studies. Thorax, 2024, 79(11): 1040-1049.
10. 楊卓文, 鄭智中, 李斌, 等. 人工智能系統在預測肺結節良惡性和病理分型中的臨床應用. 中國胸心血管外科臨床雜志, 2025, 32(8): 1086-1095.Yang ZW, Zheng ZZ, Li B, et al. Clinical application of artificial intelligence system in predicting benign and malignant pulmonary nodules and pathological classification. Chin J Clin Thorac Cardiovasc Surg, 2025, 32(8): 1086-1095.
11. Zhang C, Sun X, Dang K, et al. Toward an expert level of lung cancer detection and classification using a deep convolutional neural network. Oncologist, 2019, 24(9): 1159-1165.
12. He J, Wang H, Feng Y, et al. Integrative fragmentomic and mutational signature profile of plasma cfDNA for early lung cancer detection. NPJ Precis Oncol, 2026 [Epub ahead of print].
13. Wang H, Huang H, Li F, et al. Risk-stratified classification of pulmonary nodule malignancy via a machine learning model integrating imaging and cell-free DNA: a model development and validation study (DECIPHER-NODL). Lancet Reg Health West Pac, 2025, 56: 101730.
14. He J, Wang B, Tao J, et al. Accurate classification of pulmonary nodules by a combined model of clinical, imaging, and cell-free DNA methylation biomarkers: a model development and external validation study. Lancet Digit Health, 2023, 5(10): e647-e656.
15. Liang W, Chen Z, Li C, et al. Accurate diagnosis of pulmonary nodules using a noninvasive DNA methylation test. J Clin Invest, 2021, 131(10): e145973.
16. Pan L, Luo J, Nie C, et al. A deep learning framework integrating tumor microenvironmental features accurately predicts multiple driver gene mutations in lung cancer pathology images. Cancer Res, 2026, 86(5): 1319-1337.
17. Madani MH, Riess JW, Brown LM, et al. Imaging of lung cancer. Curr Probl Cancer, 2023, 47(2): 100966.
18. 薛保, 周俊杰, 邵偉. 基于可變形注意力和多尺度多實例學習的全切片病理圖像分類方法. 數據采集與處理, 2026, 41(1): 231-243.Xue B, Zhou JJ, Shao W. Whole slide image classification based on deformable attention and multi-scale multiple instance learning. J Data Acquis Process, 2026, 41(1): 231-243.
19. Kim D, Lee J, Jung M, et al. Whole slide image-level classification of malignant effusion cytology using clustering-constrained attention multiple instance learning. Lung Cancer, 2025, 204: 108552.
20. Yang H, Chen L, Cheng Z, et al. Deep learning-based six-type classifier for lung cancer and mimics from histopathological whole slide images: a retrospective study. BMC Med, 2021, 19(1): 80.
21. Pan X, AbdulJabbar K, Coelho-Lima J, et al. The artificial intelligence-based model ANORAK improves histopathological grading of lung adenocarcinoma. Nat Cancer, 2024, 5(2): 347-363.
22. 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, 2026, 36(3): 2157-2185.
23. Zhao Y, Xiong S, Ren Q, et al. Deep learning using histological images for gene mutation prediction in lung cancer: a multicentre retrospective study. Lancet Oncol, 2025, 26(1): 136-146.
24. Chen ZH, Chu XP, Zhang JT, et al. The regularity of anatomical variations of dominant pulmonary segments in the right upper lobe. Thorac Cancer, 2023, 14(5): 462-469.
25. Chen X, Dai C, Peng M, et al. Artificial intelligence driven 3D reconstruction for enhanced lung surgery planning. Nat Commun, 2025, 16(1): 4086.
26. Etienne H, Hamdi S, Le Roux M, et al. Artificial intelligence in thoracic surgery: past, present, perspective and limits. Eur Respir Rev, 2020, 29(157): 190054.
27. Niu G, Guan Y, Zhang Y, et al. A prospective multicenter trial of deep learning auto-segmentation for organs at risk in thoracic radiotherapy. Nat Commun, 2026 [Epub ahead of print].
28. Yu L, Ni Q, Wang B, et al. Multicenter study on the versatility and adoption of AI-driven automated radiotherapy planning across cancer types. Nat Commun, 2025, 17(1): 867.
29. Saad MB, Showkatian E, Verma V, et al. Causal AI-based clinical and radiomic analysis for optimizing patient selection in combined immunotherapy and SABR in early-stage NSCLC: a secondary analysis of the phaseⅡ I-SABR trial. J Immunother Cancer, 2025, 13(10): e013074.
30. Kashyap M, Wang X, Panjwani N, et al. Automated deep learning-based detection and segmentation of lung tumors at CT. Radiology, 2025, 314(1): e233029.
31. Xing W, Gao W, Lv X, et al. Artificial intelligence predicts lung cancer radiotherapy response: a meta-analysis. Artif Intell Med, 2023, 142: 102585.
32. 范雷, 丁利雅, 姜支農. 人工智能在肺癌的生物學標志物識別及免疫治療中的應用. 臨床與實驗病理學雜志, 2025, 41(6): 782-788.Fan L, Ding LY, Jiang ZN. Application of artificial intelligence in biomarker identification and immunotherapy of lung cancer. Chin J Clin Exp Pathol, 2025, 41(6): 782-788.
33. Chen RJ, Lu MY, Wang J, et al. Pathomic fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis. IEEE Trans Med Imaging, 2022, 41(6): 1365-1379.
34. He B, Dong D, She Y, et al. Predicting response to immunotherapy in advanced non-small-cell lung cancer using tumor mutational burden radiomic biomarker. J Immunother Cancer, 2020, 8(2): e000550.
35. Stone E, Rankin N, Kerr S, et al. Does presentation at multidisciplinary team meetings improve lung cancer survival? Findings from a consecutive cohort study. Lung Cancer, 2018, 124: 199-204.
36. Jensen K, Soguero-Ruiz C, Oyvind Mikalsen K, et al. Analysis of free text in electronic health records for identification of cancer patient trajectories. Sci Rep, 2017, 7: 46426.
37. Wang Q, Mao Z, Wang B, et al. Knowledge graph embedding: a survey of approaches and applications. IEEE Trans Knowl Data Eng, 2017, 29(12): 2724-2743.
38. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med, 2019, 25(1): 44-56.
39. Kather JN, Heij LR, Grabsch HI, et al. Pan-cancer image-based detection of clinically actionable genetic alterations. Nat Cancer, 2020, 1(8): 789-799.
40. Chen S, Yang Y. OA04.02 Implementation of large language model and GraphRAG-assisted decision support for multidisciplinary team cancer care. J Thorac Oncol, 2025, 20(10): S16-S17.
41. Qiu ZB, Li J, Dou S, et al. Quantifying early-stage lung adenocarcinoma progression with a radiomic trajectory. NPJ Digit Med, 2025, 8(1): 664.
42. Goldstein BA, Navar AM, Pencina MJ, et al. Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review. J Am Med Inform Assoc, 2017, 24(1): 198-208.
43. Harutyunyan H, Khachatrian H, Kale DC, et al. Multitask learning and benchmarking with clinical time series data. Sci Data, 2019, 6(1): 96.
44. Ye G, Wei Z, Han C, et al. AI-derived longitudinal and multi-dimensional CT classifier for non-small cell lung cancer to optimize neoadjuvant chemoimmunotherapy decision: a multicentre retrospective study. EClinicalMedicine, 2025, 89: 103551.
45. Sherman RE, Anderson SA, Dal Pan GJ, et al. Real-world evidence-what is it and what can it tell us? N Engl J Med, 2016, 375(23): 2293-2297.
46. Booth CM, Karim S, Mackillop WJ. Real-world data: towards achieving the achievable in cancer care. Nat Rev Clin Oncol, 2019, 16(5): 312-325.

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Latest ArticlesExpert consensus on the application of artificial intelligence in lung cancer screening, diagnosis, and treatment (2026 edition)

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