Artificial intelligence-driven comprehensive management for lung cancer_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

LI Xiang ¹ , YAN Shi ¹ , WANG Quanne ¹ ,  WU Nan ²

1. Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Thoracic Surgery Ⅱ, Peking University Cancer Hospital & Institute, Beijing, 100142, P. R. China;
2. State Key Laboratory of Molecular Oncology, Beijing Key Laboratory of Carcinogenesis and Translational Research, Department of Thoracic Surgery Ⅱ, Peking University Cancer Hospital & Institute. (Beijing 100142);

Corresponding author：

WU Nan, Email: nanwu@bjmu.edu.cn

Keywords：

Artificial intelligence; lung neoplasms; comprehensive health care; early detection of lung cancer; follow-up management

DOI：

10.7507/1007-4848.202507002

Video：

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Abstract Full text Figures/Tables Video References Cited by

This review thoroughly investigates the application and advancements of artificial intelligence (AI) technology in the comprehensive management of lung cancer. AI is utilized at various stages of lung cancer diagnosis and treatment through techniques such as computer vision, deep learning, and natural language processing. In the early diagnosis stage, AI assists in identifying high-risk populations and, in conjunction with pathological techniques, accomplishes functions like histological classification of lung cancer tissues, prediction of molecular markers, and quantitative analysis of immunohistochemistry. During the treatment stage, AI integrates multimodal data to aid in formulating individualized treatment plans and enhances efficiency via clinical decision support systems (CDSS). In the follow-up stage, continuous patient monitoring and optimization of follow-up strategies are realized through imaging, remote monitoring, and intelligent follow-up systems. The prospects for AI medical technology are promising. However, it still confronts challenges such as weak generalizability, poor interpretability of AI decisions, and ethical and legal issues.

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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.
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6.	Liu S, McCoy AB, Aldrich MC, et al. Leveraging natural language processing to identify eligible lung cancer screening patients with the electronic health record. Int J Med Inform, 2023, 177: 105136.
7.	Yoo H, Lee SH, Arru CD, et al. AI-based improvement in lung cancer detection on chest radiographs: results of a multi-reader study in NLST dataset. Eur Radiol, 2021, 31(12): 9664-9674.
8.	Nam JG, Park S, Hwang EJ, et al. Development and validation of deep learning-based automatic detection algorithm for malignant pulmonary nodules on chest radiographs. Radiology, 2019, 290(1): 218-228.
9.	苏寅晨, 张晓琴. 人工智能辅助诊断系统在肺结节检测及良恶性判断中的应用价值. CT理论与应用研究, 2024, 33(3): 325-331.Su YC, Zhang XQ. Artificial intelligence-assisted diagnosis in detecting lung nodules and differentiating benign from malignant nodules. CT Theory Appl, 2024, 33(3): 325-331.
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11.	Li X, Zhang S, Luo X, et al. Accuracy and efficiency of an artificial intelligence-based pulmonary broncho-vascular three-dimensional reconstruction system supporting thoracic surgery: retrospective and prospective validation study. EBioMedicine, 2023, 87: 103857.
12.	Duranti L, Tavecchio L, Rolli L, et al. New perspectives on lung cancer screening and artificial intelligence. Life (Basel), 2025, 15(3): 756.
13.	Liu M, Wu J, Wang N, et al. The value of artificial intelligence in the diagnosis of lung cancer: a systematic review and meta-analysis. PLoS One, 2023, 18(3): e0273445.
14.	Lancaster HL, Jiang B, Davies MPA, et al. Histological proven AI performance in the UKLS CT lung cancer screening study: potential for workload reduction. Eur J Cancer, 2025, 220: 115324.
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16.	Yanagawa M. Artificial intelligence improves radiologist performance for predicting malignancy at chest CT. Radiological Society of North America Scientific Assembly and Annual Meeting, 2022: 692-693.
17.	Alahmari SS, Cherezov D, Goldgof D, et al. Delta radiomics improves pulmonary nodule malignancy prediction in lung cancer screening. IEEE Access, 2018, 6: 77796-77806.
18.	Li HJ, Qiu ZB, Wang MM, et al. Radiomics-based support vector machine distinguishes molecular events driving the progression of lung adenocarcinoma. J Thorac Oncol, 2025, 20(1): 52-64.
19.	Yoo H, Kim KH, Singh R, et al. Validation of a deep learning algorithm for the detection of malignant pulmonary nodules in chest radiographs. JAMA Netw Open, 2020, 3(9): e2017135.
20.	Wallis D, Soussan M, Lacroix M, et al. An ^[18F]FDG-PET/CT deep learning method for fully automated detection of pathological mediastinal lymph nodes in lung cancer patients. Eur J Nucl Med Mol Imaging, 2022, 49(3): 881-888.
21.	Wang J, Sui X, Zhao R, et al. Value of deep learning reconstruction of chest low-dose CT for image quality improvement and lung parenchyma assessment on lung window. Eur Radiol, 2024, 34(2): 1053-1064.
22.	Goto M, Nagayama Y, Sakabe D, et al. Lung-optimized deep-learning-based reconstruction for ultralow-dose CT. Acad Radiol, 2023, 30(3): 431-440.
23.	胸外科分会, 吴阶平医学基金会. 人工智能在肺结节诊治中的应用专家共识(2022年版). 中国肺癌杂志, 2022, 25(4): 219-225.T horacic Surger y Committee, Department of Simulated Medicine, Wu Jieping Medical Foundation. Chinese Experts Consensus on Artificial Intelligence Assisted Management for Pulmonary Nodule (2022 Version). Chin Lung Cancer J, 2022, 25(4): 219-225.
24.	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.
25.	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.
26.	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.
27.	Shao J, Feng J, Li J, et al. Novel tools for early diagnosis and precision treatment based on artificial intelligence. Chin Med J Pulm Crit Care Med, 2023, 1(3): 148-160.
28.	Yang Y, Yang J, Liang Y, et al. Identification and validation of efficacy of immunological therapy for lung cancer from histopathological images based on deep learning. Front Genet, 2021, 12: 718662.
29.	Kather JN, Pearson AT, Halama N, et al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat Med, 2019, 25(7): 1054-1056.
30.	Niu Y, Wang L, Zhang X, et al. Predicting tumor mutational burden from lung adenocarcinoma histopathological images using deep learning. Front Oncol, 2022, 12: 927426.
31.	Hondeblink LM, Hüyük M, Postmus PE, et al. Development and validation of a supervised deep learning algorithm for automated whole-slide programmed death-ligand 1 tumour proportion score assessment in non-small cell lung cancer. Histopathology, 2022, 80(4): 635-647.
32.	Zhu Y, Liu YL, Feng Y, et al. A CT-derived deep neural network predicts for programmed death ligand-1 expression status in advanced lung adenocarcinomas. Ann Transl Med, 2020, 8(15): 930.
33.	Sha L, Osinski BL, Ho IY, et al. Multi-field-of-view deep learning model predicts nonsmall cell lung cancer programmed death-ligand 1 status from whole-slide hematoxylin and eosin images. J Pathol Inform, 2019, 10: 24.
34.	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.
35.	Wang S, Yang DM, Rong R, et al. Artificial intelligence in lung cancer pathology image analysis. Cancers (Basel), 2019, 11(11): 1673.
36.	Adegbesan A, Akingbola A, Aremu O, et al. From scalpels to algorithms: the risk of dependence on artificial intelligence in surgery. J Med Surg Public Health, 2024, 3: 100140.
37.	Sinha T, Khan A, Awan M, et al. Artificial intelligence and machine learning in predicting the response to immunotherapy in non-small cell lung carcinoma: a systematic review. Cureus, 2024, 16(5): e61220.
38.	Sherman MA, Yaari AU, Priebe O, et al. Genome-wide mapping of somatic mutation rates uncovers drivers of cancer. Nat Biotechnol, 2022, 40(11): 1634-1643.
39.	Wang S, Yu H, Gan Y, et al. Mining whole-lung information by artificial intelligence for predicting EGFR genotype and targeted therapy response in lung cancer: a multicohort study. Lancet Digit Health, 2022, 4(5): e309-e319.
40.	Tamborero D, Dienstmann R, Rachid MH, et al. Support systems to guide clinical decision-making in precision oncology: the Cancer Core Europe Molecular Tumor Board Portal. Nat Med, 2020, 26(7): 992-994.
41.	Kurnit KC, Dumbra EII, Litzenburger B, et al. Precision oncology decision support: current approaches and strategies for the future. Clin Cancer Res, 2018, 24(12): 2719-2731.
42.	Pritchard D, Goodman C, Nadauld LD. Clinical utility of genomic testing in cancer care. JCO Precis Oncol, 2022, 6: e2100349.
43.	Liu C, Liu X, Wu F, et al. Using artificial intelligence (Watson for oncology) for treatment recommendations amongst Chinese patients with lung cancer: feasibility study. J Med Internet Res, 2018, 20(9): e11087.
44.	胡坚, 刘伦旭, 张毅, 等. 人工智能一体化三维重建应用于胸外科的中国专家共识. 中国胸心血管外科临床杂志, 2023, 30(5): 641-646.Hu J, Liu LX, Zhang Y, et al. Chinese expert consensus on the application of integrated 3D reconstruction with artificial intelligence in thoracic surgery. Chin Clin J Thorac Cardiovasc Surg, 30(5): 641-646.
45.	Sadeghi AH, Mank Q, Tuzcu AS, et al. Artificial intelligence-assisted augmented reality robotic lung surgery: navigating the future of thoracic surgery. JTCVS Tech, 2024, 26: 121-125.
46.	Peek JJ, Zhang X, Hildebrandt K, et al. A novel 3D image registration technique for augmented reality vision in minimally invasive thoracoscopic pulmonary segmentectomy. Int J Comput Assist Radiol Surg, 2025, 20(4): 787-795.
47.	Huang G, Liu L, Wang L, et al. Prediction of postoperative cardiopulmonary complications after lung resection in a Chinese population: a machine learning-based study. Front Oncol, 2022, 12: 1003722.
48.	Leivaditis V, Maniatopoulos AA, Lausberg H, et al. Artificial intelligence in thoracic surgery: a review bridging innovation and clinical practice for the next generation of surgical care. J Clin Med, 2025, 14(8): 2047.
49.	Nakanishi R, Morooka KI, Omori K, et al. Artificial intelligence-based prediction of recurrence after curative resection for colorectal cancer from digital pathological images. Ann Surg Oncol, 2023, 30(6): 3506-3514.
50.	朱佳瑞, 滕琳, 宋立明, 等. 人工智能在放射影像中的应用. 科学通报, 2024: 1-17.Zhu J, Teng L, Song L, et al. The applications of artificial intelligence in radiographic imaging (in Chinese). Chin Sci Bull, 2024: 1-17.
51.	Li Q, Kim J, Balagurunathan Y, et al. CT imaging features associated with recurrence in non-small cell lung cancer patients after stereotactic body radiotherapy. Radiat Oncol, 2017, 12(1): 158.
52.	Bhattacharya K, Bhattacharya N, Kumar S, et al. Artificial intelligence in predicting postoperative surgical complications. Indian J Surg, 2024: 1-5.
53.	Knight SR, Ng N, Tsanas A, et al. Mobile devices and wearable technology for measuring patient outcomes after surgery: a systematic review. NPJ Digit Med, 2021, 4(1): 157.
54.	Lee J, Kong S, Shin S, et al. Wearable device–based intervention for promoting patient physical activity after lung cancer surgery: a nonrandomized clinical trial. JAMA Netw Open, 2024, 7(9): e2434180.
55.	Lv C, Lu F, Zhou X, et al. Efficacy of a smartphone application assisting home-based rehabilitation and symptom management for patients with lung cancer undergoing video-assisted thoracoscopic lobectomy: a prospective, single-blinded, randomised control trial (POPPER study). Int J Surg, 2025, 111(1): 597-608.
56.	Li YH, Li YL, Wei MY, et al. Innovation and challenges of artificial intelligence technology in personalized healthcare. Sci Rep, 2024, 14(1): 18994.
57.	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.
58.	China national lung cancer screening guideline with low-dose computed tomography (2023 version). Chin Lung Cancer J, 2023, 26(1): 1-9.

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. Li N, Tan F, Chen W, et al. One-off low-dose CT for lung cancer screening in China: a multicentre, population-based, prospective cohort study. Lancet Respir Med, 2022, 10(4): 378-391.
4. Gould MK, Huang BZ, Tammemagi MC, et al. Machine learning for early lung cancer identification using routine clinical and laboratory data. Am J Respir Crit Care Med, 2021, 204(4): 445-453.
5. Yang S, Huang Y, Lou X, et al. Toward a computable phenotype for determining eligibility of lung cancer screening using electronic health records. JCO Clin Cancer Inform, 2025, 9: e2400139.
6. Liu S, McCoy AB, Aldrich MC, et al. Leveraging natural language processing to identify eligible lung cancer screening patients with the electronic health record. Int J Med Inform, 2023, 177: 105136.
7. Yoo H, Lee SH, Arru CD, et al. AI-based improvement in lung cancer detection on chest radiographs: results of a multi-reader study in NLST dataset. Eur Radiol, 2021, 31(12): 9664-9674.
8. Nam JG, Park S, Hwang EJ, et al. Development and validation of deep learning-based automatic detection algorithm for malignant pulmonary nodules on chest radiographs. Radiology, 2019, 290(1): 218-228.
9. 苏寅晨, 张晓琴. 人工智能辅助诊断系统在肺结节检测及良恶性判断中的应用价值. CT理论与应用研究, 2024, 33(3): 325-331.Su YC, Zhang XQ. Artificial intelligence-assisted diagnosis in detecting lung nodules and differentiating benign from malignant nodules. CT Theory Appl, 2024, 33(3): 325-331.
10. Hao L. Chinese experts consensus on artificial intelligence assisted management for pulmonary nodule (2022 version). Chin J Lung Cancer, 2022, 25(4): 219-225.
11. Li X, Zhang S, Luo X, et al. Accuracy and efficiency of an artificial intelligence-based pulmonary broncho-vascular three-dimensional reconstruction system supporting thoracic surgery: retrospective and prospective validation study. EBioMedicine, 2023, 87: 103857.
12. Duranti L, Tavecchio L, Rolli L, et al. New perspectives on lung cancer screening and artificial intelligence. Life (Basel), 2025, 15(3): 756.
13. Liu M, Wu J, Wang N, et al. The value of artificial intelligence in the diagnosis of lung cancer: a systematic review and meta-analysis. PLoS One, 2023, 18(3): e0273445.
14. Lancaster HL, Jiang B, Davies MPA, et al. Histological proven AI performance in the UKLS CT lung cancer screening study: potential for workload reduction. Eur J Cancer, 2025, 220: 115324.
15. Ardila D, Kiraly AP, Bharadwaj S, et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med, 2019, 25(6): 954-961.
16. Yanagawa M. Artificial intelligence improves radiologist performance for predicting malignancy at chest CT. Radiological Society of North America Scientific Assembly and Annual Meeting, 2022: 692-693.
17. Alahmari SS, Cherezov D, Goldgof D, et al. Delta radiomics improves pulmonary nodule malignancy prediction in lung cancer screening. IEEE Access, 2018, 6: 77796-77806.
18. Li HJ, Qiu ZB, Wang MM, et al. Radiomics-based support vector machine distinguishes molecular events driving the progression of lung adenocarcinoma. J Thorac Oncol, 2025, 20(1): 52-64.
19. Yoo H, Kim KH, Singh R, et al. Validation of a deep learning algorithm for the detection of malignant pulmonary nodules in chest radiographs. JAMA Netw Open, 2020, 3(9): e2017135.
20. Wallis D, Soussan M, Lacroix M, et al. An ^[18F]FDG-PET/CT deep learning method for fully automated detection of pathological mediastinal lymph nodes in lung cancer patients. Eur J Nucl Med Mol Imaging, 2022, 49(3): 881-888.
21. Wang J, Sui X, Zhao R, et al. Value of deep learning reconstruction of chest low-dose CT for image quality improvement and lung parenchyma assessment on lung window. Eur Radiol, 2024, 34(2): 1053-1064.
22. Goto M, Nagayama Y, Sakabe D, et al. Lung-optimized deep-learning-based reconstruction for ultralow-dose CT. Acad Radiol, 2023, 30(3): 431-440.
23. 胸外科分会, 吴阶平医学基金会. 人工智能在肺结节诊治中的应用专家共识(2022年版). 中国肺癌杂志, 2022, 25(4): 219-225.T horacic Surger y Committee, Department of Simulated Medicine, Wu Jieping Medical Foundation. Chinese Experts Consensus on Artificial Intelligence Assisted Management for Pulmonary Nodule (2022 Version). Chin Lung Cancer J, 2022, 25(4): 219-225.
24. 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.
25. 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.
26. 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.
27. Shao J, Feng J, Li J, et al. Novel tools for early diagnosis and precision treatment based on artificial intelligence. Chin Med J Pulm Crit Care Med, 2023, 1(3): 148-160.
28. Yang Y, Yang J, Liang Y, et al. Identification and validation of efficacy of immunological therapy for lung cancer from histopathological images based on deep learning. Front Genet, 2021, 12: 718662.
29. Kather JN, Pearson AT, Halama N, et al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat Med, 2019, 25(7): 1054-1056.
30. Niu Y, Wang L, Zhang X, et al. Predicting tumor mutational burden from lung adenocarcinoma histopathological images using deep learning. Front Oncol, 2022, 12: 927426.
31. Hondeblink LM, Hüyük M, Postmus PE, et al. Development and validation of a supervised deep learning algorithm for automated whole-slide programmed death-ligand 1 tumour proportion score assessment in non-small cell lung cancer. Histopathology, 2022, 80(4): 635-647.
32. Zhu Y, Liu YL, Feng Y, et al. A CT-derived deep neural network predicts for programmed death ligand-1 expression status in advanced lung adenocarcinomas. Ann Transl Med, 2020, 8(15): 930.
33. Sha L, Osinski BL, Ho IY, et al. Multi-field-of-view deep learning model predicts nonsmall cell lung cancer programmed death-ligand 1 status from whole-slide hematoxylin and eosin images. J Pathol Inform, 2019, 10: 24.
34. 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.
35. Wang S, Yang DM, Rong R, et al. Artificial intelligence in lung cancer pathology image analysis. Cancers (Basel), 2019, 11(11): 1673.
36. Adegbesan A, Akingbola A, Aremu O, et al. From scalpels to algorithms: the risk of dependence on artificial intelligence in surgery. J Med Surg Public Health, 2024, 3: 100140.
37. Sinha T, Khan A, Awan M, et al. Artificial intelligence and machine learning in predicting the response to immunotherapy in non-small cell lung carcinoma: a systematic review. Cureus, 2024, 16(5): e61220.
38. Sherman MA, Yaari AU, Priebe O, et al. Genome-wide mapping of somatic mutation rates uncovers drivers of cancer. Nat Biotechnol, 2022, 40(11): 1634-1643.
39. Wang S, Yu H, Gan Y, et al. Mining whole-lung information by artificial intelligence for predicting EGFR genotype and targeted therapy response in lung cancer: a multicohort study. Lancet Digit Health, 2022, 4(5): e309-e319.
40. Tamborero D, Dienstmann R, Rachid MH, et al. Support systems to guide clinical decision-making in precision oncology: the Cancer Core Europe Molecular Tumor Board Portal. Nat Med, 2020, 26(7): 992-994.
41. Kurnit KC, Dumbra EII, Litzenburger B, et al. Precision oncology decision support: current approaches and strategies for the future. Clin Cancer Res, 2018, 24(12): 2719-2731.
42. Pritchard D, Goodman C, Nadauld LD. Clinical utility of genomic testing in cancer care. JCO Precis Oncol, 2022, 6: e2100349.
43. Liu C, Liu X, Wu F, et al. Using artificial intelligence (Watson for oncology) for treatment recommendations amongst Chinese patients with lung cancer: feasibility study. J Med Internet Res, 2018, 20(9): e11087.
44. 胡坚, 刘伦旭, 张毅, 等. 人工智能一体化三维重建应用于胸外科的中国专家共识. 中国胸心血管外科临床杂志, 2023, 30(5): 641-646.Hu J, Liu LX, Zhang Y, et al. Chinese expert consensus on the application of integrated 3D reconstruction with artificial intelligence in thoracic surgery. Chin Clin J Thorac Cardiovasc Surg, 30(5): 641-646.
45. Sadeghi AH, Mank Q, Tuzcu AS, et al. Artificial intelligence-assisted augmented reality robotic lung surgery: navigating the future of thoracic surgery. JTCVS Tech, 2024, 26: 121-125.
46. Peek JJ, Zhang X, Hildebrandt K, et al. A novel 3D image registration technique for augmented reality vision in minimally invasive thoracoscopic pulmonary segmentectomy. Int J Comput Assist Radiol Surg, 2025, 20(4): 787-795.
47. Huang G, Liu L, Wang L, et al. Prediction of postoperative cardiopulmonary complications after lung resection in a Chinese population: a machine learning-based study. Front Oncol, 2022, 12: 1003722.
48. Leivaditis V, Maniatopoulos AA, Lausberg H, et al. Artificial intelligence in thoracic surgery: a review bridging innovation and clinical practice for the next generation of surgical care. J Clin Med, 2025, 14(8): 2047.
49. Nakanishi R, Morooka KI, Omori K, et al. Artificial intelligence-based prediction of recurrence after curative resection for colorectal cancer from digital pathological images. Ann Surg Oncol, 2023, 30(6): 3506-3514.
50. 朱佳瑞, 滕琳, 宋立明, 等. 人工智能在放射影像中的应用. 科学通报, 2024: 1-17.Zhu J, Teng L, Song L, et al. The applications of artificial intelligence in radiographic imaging (in Chinese). Chin Sci Bull, 2024: 1-17.
51. Li Q, Kim J, Balagurunathan Y, et al. CT imaging features associated with recurrence in non-small cell lung cancer patients after stereotactic body radiotherapy. Radiat Oncol, 2017, 12(1): 158.
52. Bhattacharya K, Bhattacharya N, Kumar S, et al. Artificial intelligence in predicting postoperative surgical complications. Indian J Surg, 2024: 1-5.
53. Knight SR, Ng N, Tsanas A, et al. Mobile devices and wearable technology for measuring patient outcomes after surgery: a systematic review. NPJ Digit Med, 2021, 4(1): 157.
54. Lee J, Kong S, Shin S, et al. Wearable device–based intervention for promoting patient physical activity after lung cancer surgery: a nonrandomized clinical trial. JAMA Netw Open, 2024, 7(9): e2434180.
55. Lv C, Lu F, Zhou X, et al. Efficacy of a smartphone application assisting home-based rehabilitation and symptom management for patients with lung cancer undergoing video-assisted thoracoscopic lobectomy: a prospective, single-blinded, randomised control trial (POPPER study). Int J Surg, 2025, 111(1): 597-608.
56. Li YH, Li YL, Wei MY, et al. Innovation and challenges of artificial intelligence technology in personalized healthcare. Sci Rep, 2024, 14(1): 18994.
57. 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.
58. China national lung cancer screening guideline with low-dose computed tomography (2023 version). Chin Lung Cancer J, 2023, 26(1): 1-9.

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