Artificial intelligence and three-dimensional reconstruction empower lung cancer with low-dose computed tomography: Technical integration, clinical value, and future prospects_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

TANG Jingyi ,  ZHANG Xiaoli , WANG Meng , LIU Jue

Shanghai Chest hospital, Shanghai, 200030, P. R. China;

Corresponding?author：

ZHANG Xiaoli, Email: zhangxiaoli@shchest.org

Keywords：

Low-dose computed tomography; lung cancer screening; artificial intelligence; three-dimensional reconstruction; review

DOI：

10.7507/1007-4848.202510009

Video：

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[Abstract ]Lung cancer ranks first in morbidity and mortality among malignant tumors in China. Low-dose computed tomography (LDCT), as the primary screening method, faces increasingly prominent challenges of false positives and overdiagnosis associated with its high sensitivity. In recent years, the integration of artificial intelligence (AI) and three-dimensional reconstruction technology has provided a novel approach to addressing these challenges. This review highlights how AI and three-dimensional reconstruction contribute to improving image quality, enabling precise identification of pulmonary nodules and intelligent risk stratification, optimizing dynamic follow-up strategies, and assisting in preoperative planning, thereby driving the transformation of the LDCT screening paradigm. Meanwhile, current technical limitations and future directions are also discussed.

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.	馬曉波, 馬小桐, 馬建清, 等. 1990—2021年中國腫瘤疾病負擔分析. 醫學新知, 2025, 35(8): 885-891.Ma XB, Ma XT, Ma JQ, et al. Analysis of cancer disease burden in China from 1990 to 2021. J New Med, 2025, 35(8): 885-891.
3.	Luo G, Zhang Y, Etxeberria J, et al. Projections of lung cancer incidence by 2035 in 40 countries worldwide: population-based study. JMIR Public Health Surveill, 2023, 9: e43651.
4.	Park YS, Kim SH, Park SK, et al. Costs for 5-year lung cancer survivors in a tertiary care hospital in South Korea. Lung Cancer, 2010, 68(2): 299-304.
5.	陳瓊, 張明霞, 郭蘭偉, 等. 河南省2015—2019年惡性腫瘤5年相對生存率分析. 中華腫瘤雜志, 2024, 46(10): 954-960.Chen Q, Zhang MX, Guo LW, et al. The 5-year relative survival rate among cancer patients in Henan province of China, 2015-2019. Chin J Oncol, 2024, 46(10): 954-960.
6.	Xing PY, Zhu YX, Wang L, et al. What are the clinical symptoms and physical signs for non-small cell lung cancer before diagnosis is made? A nation-wide multicenter 10-year retrospective study in China. Cancer Med, 2019, 8(8): 4055-4069.
7.	韓寶惠, 王潔. 中華醫學會肺癌臨床診療指南(2025版). 中華醫學雜志, 2025, 15(34): 2918-2959.Han BH, Wang J. Chinese Medical Association guideline for clinical diagnosis and treatment of lung cancer (2025 edition). Natl Med J China, 2025, 15(34): 2918-2959.
8.	管雅喆, 任萌, 郭冬利, 等. 肺癌篩查研究進展. 中國肺癌雜志, 2020, 23(11): 954-960.Guan YZ, Ren M, Guo DL, et al. Research progress on lung cancer screening. Chin J Lung Cancer, 2020, 23(11): 954-960.
9.	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.
10.	De Koning HJ, Van Der Aalst CM, De Jong PA, et al. Reduced lung-cancer mortality with volume CT screening in a randomized trial. N Engl J Med, 2020, 382(6): 503-513.
11.	Zhou Q, Fan Y, Wu N, et al. Demonstration program of population-based lung cancer screening in China: rationale and study design. Thorac Cancer, 2014, 5(3): 197-203.
12.	陳萬青, 李霓, 石菊芳, 等. 中國城市癌癥早診早治項目進展. 中國腫瘤, 2019, 28(1): 23-25.Chen WQ, Li N, Shi JF, et al. Progress of cancer screening program in urban China. China Cancer, 2019, 28(1): 23-25.
13.	Christensen J, Prosper AE, Wu CC, et al. ACR Lung-RADS v2022: assessment categories and management recommendations. Chest, 2024, 165(3): 738-753.
14.	付玲, 魏拓, 隆海紅, 等. 社區低劑量螺旋CT肺癌篩查患者心理狀況與影響因素分析. 石河子大學學報(自然科學版), 2019, 37(6): 764-769.Fu L, Wei T, Long HH, et al. Analysis and countermeasures of psychological status of patients with low-dose spiral CT lung cancer screening in community. J Shihezi University (Nat Sci), 2019, 37(6): 764-769.
15.	Xie D, Zhang L, He N, et al. Overdiagnosis of lung cancer due to the introduction of low-dose computed tomography in average-risk populations in the People's Republic of China. J Thorac Oncol, 2025, 20(7): 884-896.
16.	Wang M, Lin S, He N, et al. The introduction of low-dose CT imaging and lung cancer overdiagnosis in Chinese women. Chest, 2023, 163(1): 239-250.
17.	Gebremeskel TG, Romeo F. Economic viability of artificial intelligence assistance in lung cancer screening using computed tomography. Computational and Systems Oncology, 2024, 4(2).
18.	Dickson JL, Horst C, Nair A, et al. Hesitancy around low-dose CT screening for lung cancer. Ann Oncol, 2022, 33(1): 34-41.
19.	Koetzier LR, Mastrodicasa D, Szczykutowicz TP, et al. Deep learning image reconstruction for CT: technical principles and clinical prospects. Radiology, 2023, 306(3): e221257.
20.	Sahu A, Mathur S, Takaoka H, et al. Transforming CT imaging with deep learning: noise reduction, artifact management, and clinical applications: a comprehensive review. European Journal of Radiology Artificial Intelligence, 2025, 4: 100042.
21.	Jung Y, Hur J, Han K, et al. Radiation dose reduction using deep learning-based image reconstruction for a low-dose chest computed tomography protocol: a phantom study. Quant Imaging Med Surg, 2023, 13(3): 1937-1947.
22.	Wang H, Li LL, Shang J, et al. Application of deep learning image reconstruction in low-dose chest CT scan. Br J Radiol, 2022, 95(1133): 20210380.
23.	Wang J, Zhu Z, Pan Z, et al. Deep learning reconstruction improves computer-aided pulmonary nodule detection and measurement accuracy for ultra-low-dose chest CT. BMC Medical Imaging, 2025, 25(1): 200.
24.	李麗, 邱天爽. 基于三維血管重建的高分辨率CT圖像孤立性肺結節提取. 中國生物醫學工程學報, 2010, 29(4): 486-491.Li L, Qiu TS. The abstraction of isolated lung nodules from high resolution CT images based on vessel 3D reconstruction. Chin J Biomed Eng, 2010, 29(4): 486-491.
25.	Chen C, Fu Z, Ye S, et al. Study on high-precision three-dimensional reconstruction of pulmonary lesions and surrounding blood vessels based on CT images. Opt Express, 2024, 32(2): 1371-1390.
26.	劉子成, 楊赫, 王鴻亞, 等. 基于三維重建的影像學分析在部分實性肺結節中的研究進展. 中國肺癌雜志, 2022, 25(2): 124-129.Liu ZC, Yang H, Wang HY, et al. Research progress in 3D-reconstruction based imaging analysis in partial solid pulmonary nodule. Chin J Lung Cancer, 2022, 25(2): 124-129.
27.	Gao H, Li J, Wu Y, et al. Imaging-aided diagnosis and treatment based on artificial intelligence for pulmonary nodules: a review. Phys Med, 2025, 136: 105050.
28.	Zhang J, Liu Y, Yu X, et al. MSC U-Net: a multi-scale encoder and spatial channel attention U-Net for precise organ-at-risk segmentation in CT images. Biomed Signal Process Control, 2025, 110: 108162.
29.	張懷瑢, 孫瀟, 田興倉, 等. 基于人工智能密度直方圖預測磨玻璃結節病理分型. 中國醫學影像學雜志, 2021, 29(12): 1212-1215.Zhang HR, Sun X, Tian XC, et al. Predict the pathological classification of ground-glass nodules based on artificial intelligence density histograms. Chin J Med Imaging, 2021, 29(12): 1212-1215.
30.	張正華, 蔡雅倩, 韓丹, 等. 人工智能與不同級別醫師對肺結節檢出效能的對照研究. 中國醫學影像學雜志, 2020, 28(9): 662-665.Zhang ZH, Cai YQ, Han D, et al. Artificial intelligence and various levels of physicians in detection of pulmonary nodules: a comparative study. Chin J Med Imaging, 2020, 28(9): 662-665.
31.	吳階平醫學基金會模擬醫學部胸外科專委會. 人工智能在肺結節診治中的應用專家共識(2022年版). 中國肺癌雜志, 2022, 25(4): 219-225.Thoracic Surgery Committee, Department of Simulated Medicine, Wu Jieping Medical Foundation. Chinese experts consensus on artificial intelligence assisted management for pulmonary nodule (2022 version). Chin J Lung Cancer, 2022, 25(4): 219-225.
32.	Ji G, Liu F, Chen Z, et al. Application value of CT three-dimensional reconstruction technology in the identification of benign and malignant lung nodules and the characteristics of nodule distribution. BMC Med Imaging, 2025, 25(1): 7.
33.	Wang YW, Wang JW, Yang SX, et al. Proposing a deep learning-based method for improving the diagnostic certainty of pulmonary nodules in CT scan of chest. Eur Radiol, 2021, 31(11): 8160-8167.
34.	Venkadesh KV, Setio AAA, Schreuder A, et al. Deep learning for malignancy risk estimation of pulmonary nodules detected at low-dose screening CT. Radiology, 2021, 300(2): 438-447.
35.	Antonissen N, Venkadesh KV, Dinnessen R, et al. External test of a deep learning algorithm for pulmonary nodule malignancy risk stratification using European screening data. Radiology, 2025, 316(3): e250874.
36.	Zhang R, Wei Y, Wang D, et al. Deep learning for malignancy risk estimation of incidental sub-centimeter pulmonary nodules on CT images. Eur Radiol, 2024, 34(7): 4218-4229.
37.	Fink N, Sperl JI, Rueckel J, et al. Artificial intelligence-based automated matching of pulmonary nodules on follow-up chest CT. Eur Radiol Exp, 2025, 9(1): 48.
38.	梁媛, 都麗娜, 伍建林. 基于AI定量檢測pGGN體積與質量并預測其生長規律. 臨床放射學雜志, 2022, 41(10): 1898-1902.Liang Y, Du LN, Wu JL. Quantitative detection of the volume and mass of pGGN and predicted its growth law based on AI. J Clin Radiol, 2022, 41(10): 1898-1902.
39.	Wang J, Zhang X, Tang W, et al. A multi-view CNN model to predict resolving of new lung nodules on follow-up low-dose chest CT. Insights Imaging, 2025, 16(1): 138.
40.	趙佳璧, 孫希文. 一種基于影像組學和神經網絡的肺結節倍增時間預測方法: 202211042784.Zhao JB, Sun XW. A method for predicting the doubling time of pulmonary nodules based on radiomics and neural networks: 202211042784.
41.	Yan Q, Wei Y, Cheng Z, et al. A deep learning model for preoperative prediction of lymph node metastasis in cT1-stage lung adenocarcinoma: a multicenter external validation study. Acad Radiol, 2025, 32(10): 6272-6283.
42.	Huang X, Huang X, Wang K, et al. 2.5D deep learning radiomics and clinical data for predicting occult lymph node metastasis in lung adenocarcinoma. BMC Med Imaging, 2025, 25(1): 225.
43.	張鵬, 羅靜, 叢壯壯, 等. 基于人工智能的肺結節CT定量參數對肺腺癌譜病變浸潤程度的預測價值. 中國胸心血管外科臨床雜志, 2025.Zhang P, Luo J, Cong ZZ, et al. The value of quantitative CT parameters based on artificial intelligence in predicting the invasion degree of lung adenocarcinoma spectrum lesions. Chin J Clin Thorac Cardiovasc Surg, 2025.
44.	張楠, 陳樹興, 許德新, 等. “靜脈流域分析法”切除位于亞段交界位置的早期周圍型肺癌10例報告. 中國微創外科雜志, 2023, 29(7): 543-546.Zhang N, Chen SX, Xu DX, et al. Anatomical partial lobectomy based on “venous watershed analysis” for early-stage peripheral lung cancer located between the adjacent subsegments: a clinical analysis of 10 cases. Chin J Minimally Invasive Surg, 2023, 29(7): 543-546.
45.	王騰騰, 劉寶東, 張毅, 等. 術前輔助定位聯合CT三維重建在以肺結節為中心的單孔胸腔鏡聯合亞段/肺段切除術中的應用. 中國胸心血管外科臨床雜志, 2023, 30(3): 364-368.Wang TT, Liu BD, Zhang Y, et al. Application of preoperative localization coupled with CT three-dimensional reconstruction in pulmonary nodule-centered uniportal thoracoscopic combined subsegmental/segmental resection. Chin J Clin Thorac Cardiovasc Surg, 2023, 30(3): 364-368.
46.	桑成鵬, 朱逸, 王亞勤, 等. AI與Mimics軟件行三維重建在胸腔鏡下解剖性肺段切除術中應用的回顧性隊列研究. 中國胸心血管外科臨床雜志, 2025, 32(3): 313-321.Sang CP, Zhu Y, Wang YQ, et al. Application of AI versus Mimics software for three-dimensional reconstruction in thoracoscopic anatomic segmentectomy: a retrospective cohort study. Chin J Clin Thorac Cardiovasc Surg, 2025, 32(3): 313-321.
47.	莫靚, 韋兵, 聶軍, 等. 三維重建及3D打印技術在精準胸腔鏡肺段切除術中的應用. 影像科學與光化學, 2024, 42(3): 239-246.Mo L, Wei B, Nie J, et al. Application of 3D reconstruction and 3D printing technology in precision thoracoscopic pulmonary segment resection. Imaging Sci Photochem, 2024, 42(3): 239-246.
48.	李青, 李潤睿, 強彥, 等. 人工智能在醫學CT圖像重建中的研究進展. 太原理工大學學報, 2023, 54(1): 1-16.Li Q, Li RR, Qiang Y, et al. Research and progress of artificial intelligence in medical CT image reconstruction. J Taiyuan University Techno, 2023, 54(1): 1-16.
49.	陳子瑜, 程國斌. 醫療人工智能中的算法黑箱及其核心倫理問題. 醫學與哲學, 2024, 45(12): 6-10.Chen ZY, Chen GB. Algorithm black box in medical artificial intelligence and its core ethical issues. Med & Philos, 2024, 45(12): 6-10.
50.	Li YH, Li YL, Wei MY, et al. Innovation and challenges of artificial intelligence technology in personalized healthcare. Sci Rep, 2024, 14(1): 18994.
51.	劉再毅, 梁長虹. 醫學影像人工智能發展現狀、困境剖析與前景展望. 中華放射學雜志, 2024, 58(11): 1365-1370.Liu ZY, Liang CH. A critical analysis of the development, challenges, and future prospects of artificial intelligence in medical imaging. Chin J Radiol, 2024, 58(11): 1365-1370.
52.	Chen W, Whitney HM, Kahaki S, et al. Multimodal data curation via interoperability: use cases with the Medical Imaging and Data Resource Center. Scientific Data, 2025, 12(1): 1340.
53.	Wang C, Shao J, He Y, et al. Data-driven risk stratification and precision management of pulmonary nodules detected on chest computed tomography. Nat Med, 2024, 30(11): 3184-3195.
54.	Wei GQ, Zhou ZK, Shi L, et al. Tunable-generalization diffusion powered by self-supervised contextual sub-data for low-dose CT reconstruction. 2025: arXiv: 2509.23885.
55.	Wang Y, Ma Z. Ethical and legal challenges of medical AI on informed consent: China as an example. Dev World Bioeth, 2025, 25(1): 46-54.

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. 馬曉波, 馬小桐, 馬建清, 等. 1990—2021年中國腫瘤疾病負擔分析. 醫學新知, 2025, 35(8): 885-891.Ma XB, Ma XT, Ma JQ, et al. Analysis of cancer disease burden in China from 1990 to 2021. J New Med, 2025, 35(8): 885-891.
3. Luo G, Zhang Y, Etxeberria J, et al. Projections of lung cancer incidence by 2035 in 40 countries worldwide: population-based study. JMIR Public Health Surveill, 2023, 9: e43651.
4. Park YS, Kim SH, Park SK, et al. Costs for 5-year lung cancer survivors in a tertiary care hospital in South Korea. Lung Cancer, 2010, 68(2): 299-304.
5. 陳瓊, 張明霞, 郭蘭偉, 等. 河南省2015—2019年惡性腫瘤5年相對生存率分析. 中華腫瘤雜志, 2024, 46(10): 954-960.Chen Q, Zhang MX, Guo LW, et al. The 5-year relative survival rate among cancer patients in Henan province of China, 2015-2019. Chin J Oncol, 2024, 46(10): 954-960.
6. Xing PY, Zhu YX, Wang L, et al. What are the clinical symptoms and physical signs for non-small cell lung cancer before diagnosis is made? A nation-wide multicenter 10-year retrospective study in China. Cancer Med, 2019, 8(8): 4055-4069.
7. 韓寶惠, 王潔. 中華醫學會肺癌臨床診療指南(2025版). 中華醫學雜志, 2025, 15(34): 2918-2959.Han BH, Wang J. Chinese Medical Association guideline for clinical diagnosis and treatment of lung cancer (2025 edition). Natl Med J China, 2025, 15(34): 2918-2959.
8. 管雅喆, 任萌, 郭冬利, 等. 肺癌篩查研究進展. 中國肺癌雜志, 2020, 23(11): 954-960.Guan YZ, Ren M, Guo DL, et al. Research progress on lung cancer screening. Chin J Lung Cancer, 2020, 23(11): 954-960.
9. 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.
10. De Koning HJ, Van Der Aalst CM, De Jong PA, et al. Reduced lung-cancer mortality with volume CT screening in a randomized trial. N Engl J Med, 2020, 382(6): 503-513.
11. Zhou Q, Fan Y, Wu N, et al. Demonstration program of population-based lung cancer screening in China: rationale and study design. Thorac Cancer, 2014, 5(3): 197-203.
12. 陳萬青, 李霓, 石菊芳, 等. 中國城市癌癥早診早治項目進展. 中國腫瘤, 2019, 28(1): 23-25.Chen WQ, Li N, Shi JF, et al. Progress of cancer screening program in urban China. China Cancer, 2019, 28(1): 23-25.
13. Christensen J, Prosper AE, Wu CC, et al. ACR Lung-RADS v2022: assessment categories and management recommendations. Chest, 2024, 165(3): 738-753.
14. 付玲, 魏拓, 隆海紅, 等. 社區低劑量螺旋CT肺癌篩查患者心理狀況與影響因素分析. 石河子大學學報(自然科學版), 2019, 37(6): 764-769.Fu L, Wei T, Long HH, et al. Analysis and countermeasures of psychological status of patients with low-dose spiral CT lung cancer screening in community. J Shihezi University (Nat Sci), 2019, 37(6): 764-769.
15. Xie D, Zhang L, He N, et al. Overdiagnosis of lung cancer due to the introduction of low-dose computed tomography in average-risk populations in the People's Republic of China. J Thorac Oncol, 2025, 20(7): 884-896.
16. Wang M, Lin S, He N, et al. The introduction of low-dose CT imaging and lung cancer overdiagnosis in Chinese women. Chest, 2023, 163(1): 239-250.
17. Gebremeskel TG, Romeo F. Economic viability of artificial intelligence assistance in lung cancer screening using computed tomography. Computational and Systems Oncology, 2024, 4(2).
18. Dickson JL, Horst C, Nair A, et al. Hesitancy around low-dose CT screening for lung cancer. Ann Oncol, 2022, 33(1): 34-41.
19. Koetzier LR, Mastrodicasa D, Szczykutowicz TP, et al. Deep learning image reconstruction for CT: technical principles and clinical prospects. Radiology, 2023, 306(3): e221257.
20. Sahu A, Mathur S, Takaoka H, et al. Transforming CT imaging with deep learning: noise reduction, artifact management, and clinical applications: a comprehensive review. European Journal of Radiology Artificial Intelligence, 2025, 4: 100042.
21. Jung Y, Hur J, Han K, et al. Radiation dose reduction using deep learning-based image reconstruction for a low-dose chest computed tomography protocol: a phantom study. Quant Imaging Med Surg, 2023, 13(3): 1937-1947.
22. Wang H, Li LL, Shang J, et al. Application of deep learning image reconstruction in low-dose chest CT scan. Br J Radiol, 2022, 95(1133): 20210380.
23. Wang J, Zhu Z, Pan Z, et al. Deep learning reconstruction improves computer-aided pulmonary nodule detection and measurement accuracy for ultra-low-dose chest CT. BMC Medical Imaging, 2025, 25(1): 200.
24. 李麗, 邱天爽. 基于三維血管重建的高分辨率CT圖像孤立性肺結節提取. 中國生物醫學工程學報, 2010, 29(4): 486-491.Li L, Qiu TS. The abstraction of isolated lung nodules from high resolution CT images based on vessel 3D reconstruction. Chin J Biomed Eng, 2010, 29(4): 486-491.
25. Chen C, Fu Z, Ye S, et al. Study on high-precision three-dimensional reconstruction of pulmonary lesions and surrounding blood vessels based on CT images. Opt Express, 2024, 32(2): 1371-1390.
26. 劉子成, 楊赫, 王鴻亞, 等. 基于三維重建的影像學分析在部分實性肺結節中的研究進展. 中國肺癌雜志, 2022, 25(2): 124-129.Liu ZC, Yang H, Wang HY, et al. Research progress in 3D-reconstruction based imaging analysis in partial solid pulmonary nodule. Chin J Lung Cancer, 2022, 25(2): 124-129.
27. Gao H, Li J, Wu Y, et al. Imaging-aided diagnosis and treatment based on artificial intelligence for pulmonary nodules: a review. Phys Med, 2025, 136: 105050.
28. Zhang J, Liu Y, Yu X, et al. MSC U-Net: a multi-scale encoder and spatial channel attention U-Net for precise organ-at-risk segmentation in CT images. Biomed Signal Process Control, 2025, 110: 108162.
29. 張懷瑢, 孫瀟, 田興倉, 等. 基于人工智能密度直方圖預測磨玻璃結節病理分型. 中國醫學影像學雜志, 2021, 29(12): 1212-1215.Zhang HR, Sun X, Tian XC, et al. Predict the pathological classification of ground-glass nodules based on artificial intelligence density histograms. Chin J Med Imaging, 2021, 29(12): 1212-1215.
30. 張正華, 蔡雅倩, 韓丹, 等. 人工智能與不同級別醫師對肺結節檢出效能的對照研究. 中國醫學影像學雜志, 2020, 28(9): 662-665.Zhang ZH, Cai YQ, Han D, et al. Artificial intelligence and various levels of physicians in detection of pulmonary nodules: a comparative study. Chin J Med Imaging, 2020, 28(9): 662-665.
31. 吳階平醫學基金會模擬醫學部胸外科專委會. 人工智能在肺結節診治中的應用專家共識(2022年版). 中國肺癌雜志, 2022, 25(4): 219-225.Thoracic Surgery Committee, Department of Simulated Medicine, Wu Jieping Medical Foundation. Chinese experts consensus on artificial intelligence assisted management for pulmonary nodule (2022 version). Chin J Lung Cancer, 2022, 25(4): 219-225.
32. Ji G, Liu F, Chen Z, et al. Application value of CT three-dimensional reconstruction technology in the identification of benign and malignant lung nodules and the characteristics of nodule distribution. BMC Med Imaging, 2025, 25(1): 7.
33. Wang YW, Wang JW, Yang SX, et al. Proposing a deep learning-based method for improving the diagnostic certainty of pulmonary nodules in CT scan of chest. Eur Radiol, 2021, 31(11): 8160-8167.
34. Venkadesh KV, Setio AAA, Schreuder A, et al. Deep learning for malignancy risk estimation of pulmonary nodules detected at low-dose screening CT. Radiology, 2021, 300(2): 438-447.
35. Antonissen N, Venkadesh KV, Dinnessen R, et al. External test of a deep learning algorithm for pulmonary nodule malignancy risk stratification using European screening data. Radiology, 2025, 316(3): e250874.
36. Zhang R, Wei Y, Wang D, et al. Deep learning for malignancy risk estimation of incidental sub-centimeter pulmonary nodules on CT images. Eur Radiol, 2024, 34(7): 4218-4229.
37. Fink N, Sperl JI, Rueckel J, et al. Artificial intelligence-based automated matching of pulmonary nodules on follow-up chest CT. Eur Radiol Exp, 2025, 9(1): 48.
38. 梁媛, 都麗娜, 伍建林. 基于AI定量檢測pGGN體積與質量并預測其生長規律. 臨床放射學雜志, 2022, 41(10): 1898-1902.Liang Y, Du LN, Wu JL. Quantitative detection of the volume and mass of pGGN and predicted its growth law based on AI. J Clin Radiol, 2022, 41(10): 1898-1902.
39. Wang J, Zhang X, Tang W, et al. A multi-view CNN model to predict resolving of new lung nodules on follow-up low-dose chest CT. Insights Imaging, 2025, 16(1): 138.
40. 趙佳璧, 孫希文. 一種基于影像組學和神經網絡的肺結節倍增時間預測方法: 202211042784.Zhao JB, Sun XW. A method for predicting the doubling time of pulmonary nodules based on radiomics and neural networks: 202211042784.
41. Yan Q, Wei Y, Cheng Z, et al. A deep learning model for preoperative prediction of lymph node metastasis in cT1-stage lung adenocarcinoma: a multicenter external validation study. Acad Radiol, 2025, 32(10): 6272-6283.
42. Huang X, Huang X, Wang K, et al. 2.5D deep learning radiomics and clinical data for predicting occult lymph node metastasis in lung adenocarcinoma. BMC Med Imaging, 2025, 25(1): 225.
43. 張鵬, 羅靜, 叢壯壯, 等. 基于人工智能的肺結節CT定量參數對肺腺癌譜病變浸潤程度的預測價值. 中國胸心血管外科臨床雜志, 2025.Zhang P, Luo J, Cong ZZ, et al. The value of quantitative CT parameters based on artificial intelligence in predicting the invasion degree of lung adenocarcinoma spectrum lesions. Chin J Clin Thorac Cardiovasc Surg, 2025.
44. 張楠, 陳樹興, 許德新, 等. “靜脈流域分析法”切除位于亞段交界位置的早期周圍型肺癌10例報告. 中國微創外科雜志, 2023, 29(7): 543-546.Zhang N, Chen SX, Xu DX, et al. Anatomical partial lobectomy based on “venous watershed analysis” for early-stage peripheral lung cancer located between the adjacent subsegments: a clinical analysis of 10 cases. Chin J Minimally Invasive Surg, 2023, 29(7): 543-546.
45. 王騰騰, 劉寶東, 張毅, 等. 術前輔助定位聯合CT三維重建在以肺結節為中心的單孔胸腔鏡聯合亞段/肺段切除術中的應用. 中國胸心血管外科臨床雜志, 2023, 30(3): 364-368.Wang TT, Liu BD, Zhang Y, et al. Application of preoperative localization coupled with CT three-dimensional reconstruction in pulmonary nodule-centered uniportal thoracoscopic combined subsegmental/segmental resection. Chin J Clin Thorac Cardiovasc Surg, 2023, 30(3): 364-368.
46. 桑成鵬, 朱逸, 王亞勤, 等. AI與Mimics軟件行三維重建在胸腔鏡下解剖性肺段切除術中應用的回顧性隊列研究. 中國胸心血管外科臨床雜志, 2025, 32(3): 313-321.Sang CP, Zhu Y, Wang YQ, et al. Application of AI versus Mimics software for three-dimensional reconstruction in thoracoscopic anatomic segmentectomy: a retrospective cohort study. Chin J Clin Thorac Cardiovasc Surg, 2025, 32(3): 313-321.
47. 莫靚, 韋兵, 聶軍, 等. 三維重建及3D打印技術在精準胸腔鏡肺段切除術中的應用. 影像科學與光化學, 2024, 42(3): 239-246.Mo L, Wei B, Nie J, et al. Application of 3D reconstruction and 3D printing technology in precision thoracoscopic pulmonary segment resection. Imaging Sci Photochem, 2024, 42(3): 239-246.
48. 李青, 李潤睿, 強彥, 等. 人工智能在醫學CT圖像重建中的研究進展. 太原理工大學學報, 2023, 54(1): 1-16.Li Q, Li RR, Qiang Y, et al. Research and progress of artificial intelligence in medical CT image reconstruction. J Taiyuan University Techno, 2023, 54(1): 1-16.
49. 陳子瑜, 程國斌. 醫療人工智能中的算法黑箱及其核心倫理問題. 醫學與哲學, 2024, 45(12): 6-10.Chen ZY, Chen GB. Algorithm black box in medical artificial intelligence and its core ethical issues. Med & Philos, 2024, 45(12): 6-10.
50. Li YH, Li YL, Wei MY, et al. Innovation and challenges of artificial intelligence technology in personalized healthcare. Sci Rep, 2024, 14(1): 18994.
51. 劉再毅, 梁長虹. 醫學影像人工智能發展現狀、困境剖析與前景展望. 中華放射學雜志, 2024, 58(11): 1365-1370.Liu ZY, Liang CH. A critical analysis of the development, challenges, and future prospects of artificial intelligence in medical imaging. Chin J Radiol, 2024, 58(11): 1365-1370.
52. Chen W, Whitney HM, Kahaki S, et al. Multimodal data curation via interoperability: use cases with the Medical Imaging and Data Resource Center. Scientific Data, 2025, 12(1): 1340.
53. Wang C, Shao J, He Y, et al. Data-driven risk stratification and precision management of pulmonary nodules detected on chest computed tomography. Nat Med, 2024, 30(11): 3184-3195.
54. Wei GQ, Zhou ZK, Shi L, et al. Tunable-generalization diffusion powered by self-supervised contextual sub-data for low-dose CT reconstruction. 2025: arXiv: 2509.23885.
55. Wang Y, Ma Z. Ethical and legal challenges of medical AI on informed consent: China as an example. Dev World Bioeth, 2025, 25(1): 46-54.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Latest ArticlesArtificial intelligence and three-dimensional reconstruction empower lung cancer with low-dose computed tomography: Technical integration, clinical value, and future prospects

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