Advances in the multidimensional applications of artificial intelligence in pulmonary nodule management: From early detection to surgical decision support_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

ZHOU Zheng ¹ , WU Junhan ^1,2 , LU Guojie ^1,2 ,  QIAO Guibin ^1,2

1. Southern Medical University, Guangzhou, 510515, P. R. China;
2. Department of Thoracic Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou 510282, P. R. China;

Corresponding author：

QIAO Guibin, Email: guibinqiao@126.com

Keywords：

Artificial intelligence; pulmonary nodules; multi-omics; three-dimensional reconstruction; perioperative management; review

DOI：

10.7507/1007-4848.202507090

Video：

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Abstract Full text Figures/Tables Video References Cited by

With the widespread adoption of lung cancer screening and growing public awareness, the detection rate of pulmonary nodules has increased substantially, posing new challenges for clinical management. Artificial intelligence (AI) has emerged as a powerful tool across the entire management spectrum of pulmonary nodules. Beyond improving detection sensitivity and consistency in chest radiographs and low-dose CT, AI has demonstrated promising applications in malignancy risk assessment, molecular subtype prediction, preoperative 3D planning, intraoperative navigation, and postoperative monitoring. This review summarizes recent advances in the application of AI to pulmonary nodule screening, longitudinal evaluation, pathology prediction, multi-omics integration, and perioperative management. It also discusses the technical characteristics, clinical performance, current limitations, and future prospects of various AI models. The continuous development of AI is reshaping the clinical pathway of pulmonary nodules toward more efficient and individualized care.

1.	吴俊翰, 庄伟涛, 许海杰, 等. 肺结节患者焦虑、抑郁情绪及其影响因素的横断面研究. 中国胸心血管外科临床杂志, 2023, 30(3): 357-363.Wu JH, Zhuang WT, Xu HJ, et al. Anxiety and depression in patients with pulmonary nodules and its related influencing factors: a cross-sectional study. Chin J Clin Thorac Cardiovasc Surg, 2023, 30(3): 357-363.
2.	Cellina M, Cacioppa LM, Cè M, et al. Artificial intelligence in lung cancer screening: the future is now. Cancers (Basel), 2023, 15(17): 4557.
3.	吴阶平医学基金会模拟医学部胸外科专委会. 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.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.
4.	Nam JG, Hwang EJ, Kim J, et al. AI improves nodule detection on chest radiographs in a health screening population: a randomized controlled trial. Radiology, 2023, 307(2): e221894.
5.	Kim JY, Ryu WS, Kim D, et al. Better performance of deep learning pulmonary nodule detection using chest radiography with pixel level labels in reference to computed tomography: data quality matters. Sci Rep, 2024, 14(1): 15967.
6.	Tang YX, Tang YB, Peng Y, et al. Automated abnormality classification of chest radiographs using deep convolutional neural networks. NPJ Digit Med, 2020, 3: 70.
7.	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.
8.	Hwang SH, Shin HJ, Kim EK, et al. Clinical outcomes and actual consequence of lung nodules incidentally detected on chest radiographs by artificial intelligence. Sci Rep, 2023, 13(1): 19732.
9.	Ueda D, Matsumoto T, Yamamoto A, et al. A deep learning-based model to estimate pulmonary function from chest x-rays: multi-institutional model development and validation study in Japan. Lancet Digit Health, 2024, 6(8): e580-e588.
10.	Singla S, Eslami M, Pollack B, et al. Explaining the black-box smoothly-a counterfactual approach. Med Image Anal, 2023, 84: 102721.
11.	Singh R, Kalra MK, Homayounieh F, et al. Artificial intelligence-based vessel suppression for detection of sub-solid nodules in lung cancer screening computed tomography. Quant Imaging Med Surg, 2021, 11(4): 1134-1143.
12.	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 Med Imaging, 2025, 25(1): 200.
13.	Jiang B, Li N, Shi X, et al. Deep learning reconstruction shows better lung nodule detection for ultra-low-dose chest CT. Radiology, 2022, 303(1): 202-212.
14.	Yu P, Zhang H, Wang D, et al. Spatial resolution enhancement using deep learning improves chest disease diagnosis based on thick slice CT. NPJ Digit Med, 2024, 7(1): 335.
15.	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.
16.	Fang J, Wang J, Li A, et al. Siamese encoder-based spatial-temporal mixer for growth trend prediction of lung nodules on CT scans. Int Conf Med Image Comput Comput Assist Interv, 2022: 484-494.
17.	Li Y, Yang J, Xu Y, et al. Learning tumor growth via follow-up volume prediction for lung nodules. Int Conf Med Image Comput Comput Assist Interv, 2020: 508-517.
18.	Takeshita Y, Onozawa S, Katase S, et al. Evaluation of an artificial intelligence U-net algorithm for pulmonary nodule tracking on chest computed tomography images. J Int Med Res, 2024, 52(2): 3000605241230033.
19.	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.
20.	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.
21.	Pan Z, Hu G, Zhu Z, et al. Predicting invasiveness of lung adenocarcinoma at chest CT with deep learning ternary classification models. Radiology, 2024, 311(1): e232057.
22.	Zhong Y, She Y, Deng J, et al. Deep learning for prediction of N2 metastasis and survival for clinical stage I non-small cell lung cancer. Radiology, 2022, 302(1): 200-211.
23.	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.
24.	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.
25.	Lastwika KJ, Wu W, Zhang Y, et al. Multi-omic biomarkers improve indeterminate pulmonary nodule malignancy risk assessment. Cancers (Basel), 2023, 15(13): 3345.
26.	Peng M. Classification of pulmonary nodules in the era of precision medicine. Lancet Digit Health, 2023, 5(10): e633-e634.
27.	He W, Huang W, Zhang L, et al. Radiogenomics: bridging the gap between imaging and genomics for precision oncology. MedComm (2020), 2024, 5(9): e722.
28.	Zhu E, Muneer A, Zhang J, et al. Progress and challenges of artificial intelligence in lung cancer clinical translation. NPJ Precis Oncol, 2025, 9(1): 210.
29.	Baldwin DR, Gustafson J, Pickup L, et al. External validation of a convolutional neural network artificial intelligence tool to predict malignancy in pulmonary nodules. Thorax, 2020, 75(4): 306-312.
30.	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.
31.	Wang C, Shao J, Xu X, et al. DeepLN: a multi-task AI tool to predict the imaging characteristics, malignancy and pathological subtypes in CT-detected pulmonary nodules. Front Oncol, 2022, 12: 683792.
32.	王蕾, 向之明. 人工智能联合三维重建在胸腔镜肺结节切除术中的应用进展. 中国胸心血管外科临床杂志, 2025, 32(2): 252-257.Wang L, Xiang ZM. Advances in the application of AI-assisted three-dimensional reconstruction in thoracoscopic pulmonary nodule resection surgery. Chin J Clin Thorac Cardiovasc Surg, 2025, 32(2): 252-257.
33.	Das N, Happaerts S, Gyselinck I, et al. Collaboration between explainable artificial intelligence and pulmonologists improves the accuracy of pulmonary function test interpretation. Eur Respir J, 2023, 61(5): 2201948.
34.	Chan EG, Landreneau JR, Schuchert MJ, et al. Preoperative (3-dimensional) computed tomography lung reconstruction before anatomic segmentectomy or lobectomy for stage I non-small cell lung cancer. J Thorac Cardiovasc Surg, 2015, 150(3): 523-528.
35.	钟文昭, 杨帆, 胡坚, 等. 肺部流域地形图2. 0原理、技术规范及临床应用胸外科专家共识(2024版). 中国胸心血管外科临床杂志, 2025, 32(2): 141-152.Zhong WZ, Yang F, Hu J, et al. Principles, technical specifications, and clinical application of lung watershed topography map 2. 0: a thoracic surgery expert consensus (2024 version). Chin J Clin Thorac Cardiovasc Surg, 2025, 32(2): 141-152.
36.	Kadomatsu Y, Nakao M, Ueno H, et al. A novel system applying artificial intelligence in the identification of air leak sites. JTCVS Tech, 2022, 15: 181-191.
37.	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.
38.	Kuang Q, Feng B, Xu K, et al. Multimodal deep learning radiomics model for predicting postoperative progression in solid stage I non-small cell lung cancer. Cancer Imaging, 2024, 24(1): 140.
39.	Kim H, Goo JM, Lee KH, et al. Preoperative CT-based deep learning model for predicting disease-free survival in patients with lung adenocarcinomas. Radiology, 2020, 296(1): 216-224.
40.	Zhou CM, Xue Q, Li H, et al. A predictive model for post-thoracoscopic surgery pulmonary complications based on the PBNN algorithm. Sci Rep, 2024, 14(1): 7035.

1. 吴俊翰, 庄伟涛, 许海杰, 等. 肺结节患者焦虑、抑郁情绪及其影响因素的横断面研究. 中国胸心血管外科临床杂志, 2023, 30(3): 357-363.Wu JH, Zhuang WT, Xu HJ, et al. Anxiety and depression in patients with pulmonary nodules and its related influencing factors: a cross-sectional study. Chin J Clin Thorac Cardiovasc Surg, 2023, 30(3): 357-363.
2. Cellina M, Cacioppa LM, Cè M, et al. Artificial intelligence in lung cancer screening: the future is now. Cancers (Basel), 2023, 15(17): 4557.
3. 吴阶平医学基金会模拟医学部胸外科专委会. 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.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.
4. Nam JG, Hwang EJ, Kim J, et al. AI improves nodule detection on chest radiographs in a health screening population: a randomized controlled trial. Radiology, 2023, 307(2): e221894.
5. Kim JY, Ryu WS, Kim D, et al. Better performance of deep learning pulmonary nodule detection using chest radiography with pixel level labels in reference to computed tomography: data quality matters. Sci Rep, 2024, 14(1): 15967.
6. Tang YX, Tang YB, Peng Y, et al. Automated abnormality classification of chest radiographs using deep convolutional neural networks. NPJ Digit Med, 2020, 3: 70.
7. 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.
8. Hwang SH, Shin HJ, Kim EK, et al. Clinical outcomes and actual consequence of lung nodules incidentally detected on chest radiographs by artificial intelligence. Sci Rep, 2023, 13(1): 19732.
9. Ueda D, Matsumoto T, Yamamoto A, et al. A deep learning-based model to estimate pulmonary function from chest x-rays: multi-institutional model development and validation study in Japan. Lancet Digit Health, 2024, 6(8): e580-e588.
10. Singla S, Eslami M, Pollack B, et al. Explaining the black-box smoothly-a counterfactual approach. Med Image Anal, 2023, 84: 102721.
11. Singh R, Kalra MK, Homayounieh F, et al. Artificial intelligence-based vessel suppression for detection of sub-solid nodules in lung cancer screening computed tomography. Quant Imaging Med Surg, 2021, 11(4): 1134-1143.
12. 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 Med Imaging, 2025, 25(1): 200.
13. Jiang B, Li N, Shi X, et al. Deep learning reconstruction shows better lung nodule detection for ultra-low-dose chest CT. Radiology, 2022, 303(1): 202-212.
14. Yu P, Zhang H, Wang D, et al. Spatial resolution enhancement using deep learning improves chest disease diagnosis based on thick slice CT. NPJ Digit Med, 2024, 7(1): 335.
15. 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.
16. Fang J, Wang J, Li A, et al. Siamese encoder-based spatial-temporal mixer for growth trend prediction of lung nodules on CT scans. Int Conf Med Image Comput Comput Assist Interv, 2022: 484-494.
17. Li Y, Yang J, Xu Y, et al. Learning tumor growth via follow-up volume prediction for lung nodules. Int Conf Med Image Comput Comput Assist Interv, 2020: 508-517.
18. Takeshita Y, Onozawa S, Katase S, et al. Evaluation of an artificial intelligence U-net algorithm for pulmonary nodule tracking on chest computed tomography images. J Int Med Res, 2024, 52(2): 3000605241230033.
19. 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.
20. 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.
21. Pan Z, Hu G, Zhu Z, et al. Predicting invasiveness of lung adenocarcinoma at chest CT with deep learning ternary classification models. Radiology, 2024, 311(1): e232057.
22. Zhong Y, She Y, Deng J, et al. Deep learning for prediction of N2 metastasis and survival for clinical stage I non-small cell lung cancer. Radiology, 2022, 302(1): 200-211.
23. 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.
24. 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.
25. Lastwika KJ, Wu W, Zhang Y, et al. Multi-omic biomarkers improve indeterminate pulmonary nodule malignancy risk assessment. Cancers (Basel), 2023, 15(13): 3345.
26. Peng M. Classification of pulmonary nodules in the era of precision medicine. Lancet Digit Health, 2023, 5(10): e633-e634.
27. He W, Huang W, Zhang L, et al. Radiogenomics: bridging the gap between imaging and genomics for precision oncology. MedComm (2020), 2024, 5(9): e722.
28. Zhu E, Muneer A, Zhang J, et al. Progress and challenges of artificial intelligence in lung cancer clinical translation. NPJ Precis Oncol, 2025, 9(1): 210.
29. Baldwin DR, Gustafson J, Pickup L, et al. External validation of a convolutional neural network artificial intelligence tool to predict malignancy in pulmonary nodules. Thorax, 2020, 75(4): 306-312.
30. 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.
31. Wang C, Shao J, Xu X, et al. DeepLN: a multi-task AI tool to predict the imaging characteristics, malignancy and pathological subtypes in CT-detected pulmonary nodules. Front Oncol, 2022, 12: 683792.
32. 王蕾, 向之明. 人工智能联合三维重建在胸腔镜肺结节切除术中的应用进展. 中国胸心血管外科临床杂志, 2025, 32(2): 252-257.Wang L, Xiang ZM. Advances in the application of AI-assisted three-dimensional reconstruction in thoracoscopic pulmonary nodule resection surgery. Chin J Clin Thorac Cardiovasc Surg, 2025, 32(2): 252-257.
33. Das N, Happaerts S, Gyselinck I, et al. Collaboration between explainable artificial intelligence and pulmonologists improves the accuracy of pulmonary function test interpretation. Eur Respir J, 2023, 61(5): 2201948.
34. Chan EG, Landreneau JR, Schuchert MJ, et al. Preoperative (3-dimensional) computed tomography lung reconstruction before anatomic segmentectomy or lobectomy for stage I non-small cell lung cancer. J Thorac Cardiovasc Surg, 2015, 150(3): 523-528.
35. 钟文昭, 杨帆, 胡坚, 等. 肺部流域地形图2. 0原理、技术规范及临床应用胸外科专家共识(2024版). 中国胸心血管外科临床杂志, 2025, 32(2): 141-152.Zhong WZ, Yang F, Hu J, et al. Principles, technical specifications, and clinical application of lung watershed topography map 2. 0: a thoracic surgery expert consensus (2024 version). Chin J Clin Thorac Cardiovasc Surg, 2025, 32(2): 141-152.
36. Kadomatsu Y, Nakao M, Ueno H, et al. A novel system applying artificial intelligence in the identification of air leak sites. JTCVS Tech, 2022, 15: 181-191.
37. 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.
38. Kuang Q, Feng B, Xu K, et al. Multimodal deep learning radiomics model for predicting postoperative progression in solid stage I non-small cell lung cancer. Cancer Imaging, 2024, 24(1): 140.
39. Kim H, Goo JM, Lee KH, et al. Preoperative CT-based deep learning model for predicting disease-free survival in patients with lung adenocarcinomas. Radiology, 2020, 296(1): 216-224.
40. Zhou CM, Xue Q, Li H, et al. A predictive model for post-thoracoscopic surgery pulmonary complications based on the PBNN algorithm. Sci Rep, 2024, 14(1): 7035.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Latest ArticlesAdvances in the multidimensional applications of artificial intelligence in pulmonary nodule management: From early detection to surgical decision support

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