Research progress on autoantibody liquid biopsy and AI-based radiomics in the diagnosis and treatment of non-small cell lung cancer_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

WANG Donghui ^1,2 , TANG Xu ^1,2 , ZHANG Zhiyuan ¹ , MA Nan ³ ,  DONG Xinchun ^1,2 ,  GOU Yunjiu ^1,2

1. Graduate School, Gansu University of Chinese Medicine, Lanzhou, 730000, P. R. China;
2. Department of Thoracic Surgery, Gansu Provincial Hospital, Lanzhou, 730000, P. R. China;
3. First Ward of Department of Cardiology, Gansu Provincial Hospital, Lanzhou, 730000, P. R. China;

Corresponding author：

DONG Xinchun, Email: 3050319523@qq.com; GOU Yunjiu, Email: gouyunjiu@163.com

Keywords：

Lung cancer; non-small cell lung cancer; autoantibodies liquid biopsy; artificial intelligence; radiomics; review

DOI：

10.7507/1007-4848.202508010

Video：

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Abstract Full text Figures/Tables Video References Cited by

Lung cancer has the highest incidence and mortality rates among malignant tumors both in China and worldwide, with approximately 85% of cases being non-small cell lung cancer (NSCLC). In the diagnosis and treatment of lung cancer, conventional imaging and tissue biopsy are often limited by insufficient sensitivity or invasive risks, making it difficult to meet the demands of future precision medicine. In recent years, artificial intelligence (AI)-based radiomics and autoantibody-based liquid biopsy have developed rapidly and have become major research focuses. AI radiomics significantly improves the accuracy of traditional imaging diagnosis by autonomously learning from large-scale imaging databases. Autoantibody liquid biopsy, on the other hand, utilizes tumor-associated autoantigens and antibodies as biomarkers, offering the advantages of being non-invasive, precise, efficient, and capable of reflecting spatiotemporal tumor heterogeneity, thereby demonstrating great potential in NSCLC diagnosis and treatment. This review summarizes recent research advances in autoantibody liquid biopsy and AI radiomics for the management of lung cancer.

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.	唐旭, 王瑾, 周文生, 等. 基于循环肿瘤DNA液体活检监测分子残留病灶在非小细胞肺癌诊疗中的应用进展. 中国胸心血管外科临床杂志, 2025.Epub ahead of print]. Tang X, Wang J, Zhou WS, et al. Application progress of circulating tumor DNA liquid biopsy in monitoring minimal residual disease in non-small cell lung cancer. Chin J Clin Thorac Cardiovasc Surg, 2025, [Epub ahead of print].
3.	Minami Y. The notable topics of the 5th edition of WHO classification for the thoracic tumours (2021). Gan To Kagaku Ryoho, 2022, 49(8): 847-852.
4.	Goldstraw P, Chansky K, Crowley J, et al. The IASLC lung cancer staging project: proposals for revision of the TNM stage groupings in the forthcoming (eighth) edition of the TNM classification for lung cancer. J Thorac Oncol, 2016, 11(1): 39-51.
5.	Liu Y, Gao Y, Wu Y, et al. Autoantibodies as potential liquid biopsy biomarker in detection of pancreatic cancer: a diagnostic test accuracy review and meta-analysis. Scand J Immunol, 2025, 101(4): e70012.
6.	Ouyang R, Wu S, Zhang B, et al. Clinical value of tumor-associated antigens and autoantibody panel combination detection in the early diagnostic of lung cancer. Cancer Biomark, 2021, 32(3): 401-409.
7.	Graeve VIJ, Laures S, Spirig A, et al. Implementation of an AI algorithm in clinical practice to reduce missed incidental pulmonary embolisms on chest CT and its impact on short-term survival. Invest Radiol, 2025, 60(4): 260-266.
8.	桑亚丽, 宋丽沙, 彭明月, 等. 肺癌患者血清中外泌体潜在自身抗体标志物的研究进展. 内蒙古医学杂志, 2025, 57(5): 569-573.Sang YL, Song LS, Peng MY, et al. Research progress on potential autoantibody markers in serum exosomes of lung cancer patients. Inner Mongol Med J, 2025, 57(5): 569-573.
9.	孙硕, 王锋, 何立, 等. 液体活检生物标志物及其联合影像学在肺癌早期诊断中应用的研究进展. 中国胸心血管外科临床杂志, 2023, 30(2): 313-319.Sun S, Wang F, He L, et al. Research progress on liquid biopsy biomarkers and their combination with imaging in the early diagnosis of lung cancer. Chin J Clin Thorac Cardiovasc Surg, 2023, 30(2): 313-319.
10.	王冰, 邱红. 肿瘤标志物在肺癌诊断中的研究进展. 国际检验医学杂志, 2013, 34(24): 3384-3386.Wang B, Qiu H. Research progress on tumor markers in the diagnosis of lung cancer. Int J Lab Med, 2013, 34(24): 3384-3386.
11.	王柳倩, 马为. 肿瘤标志物在肺癌的早期诊断及预后评估中的研究进展. 现代肿瘤医学, 2014, 22(12): 3004-3007.Wang LQ, Ma W. Research progress on tumor markers in early diagnosis and prognosis evaluation of lung cancer. J Mod Oncol, 2014, 22(12): 3004-3007.
12.	吴东霞, 沈溪明. 肿瘤标志物在肺癌早期诊断与治疗中的价值. 吉林医学, 2016, 37(8): 1976-1977.Wu DX, Shen XM. The value of tumor markers in the early diagnosis and treatment of lung cancer. Jilin Med J, 2016, 37(8): 1976-1977.
13.	Xu Y, Zhang W, Xia T, et al. Diagnostic value of tumor-associated autoantibodies panel in combination with traditional tumor markers for lung cancer. Front Oncol, 2023, 13: 1022331.
14.	周潇, 邹强, 张岩. 7项肿瘤相关抗原自身抗体在不同分期非小细胞肺癌诊断中临床意义. 临床军医杂志, 2022, 50(9): 922-925.Zhou X, Zou Q, Zhang Y. Clinical significance of seven tumor-associated antigen autoantibodies in the diagnosis of non-small cell lung cancer at different stages. J Clin Military Surgeon, 2022, 50(9): 922-925.
15.	Tong L, Sun J, Zhang X, et al. Development of an autoantibody panel for early detection of lung cancer in the Chinese population. Front Med (Lausanne), 2023, 10: 1209747.
16.	张少华, 杜文水. 人血清涎液化糖链抗原磁微粒化学发光免疫分析方法的建立及对肺癌的诊断价值. 医疗装备, 2024, 37(1): 95-101.Zhang SH, Du WS. Establishment of a magnetic particle chemiluminescence immunoassay for human serum sialylated glycoprotein antigen and its diagnostic value for lung cancer. Med Equipment, 2024, 37(1): 95-101.
17.	He T, Wu Z, Xia P, et al. The combination of a seven-autoantibody panel with computed tomography scanning can enhance the diagnostic efficiency of non-small cell lung cancer. Front Oncol, 2022, 12: 1047019.
18.	Qin J, Zeng N, Yang T, et al. Diagnostic value of autoantibodies in lung cancer: a systematic review and meta-analysis. Cell Physiol Biochem, 2018, 51(6): 2631-2646.
19.	Xie F, Xu L, Mu Y, et al. Diagnostic value of seven autoantibodies combined with CEA and CA199 in non-small cell lung cancer. Clin Lab, 2023, 69(5): 789-795.
20.	Mu YY, Xie FY, Wang FB, et al. Performance evaluation of an enzyme-linked immunosorbent assay for seven autoantibodies in lung cancer. Clin Lab, 2019, 65(4): 475-482.
21.	Chen Q, Zhu S, Jiao N, et al. Improvement in the performance of an autoantibody panel in combination with heat shock protein 90a for the detection of early-stage lung cancer. Exp Ther Med, 2023, 25(2): 82.
22.	Veronesi G, Bianchi F, Infante M, et al. The challenge of small lung nodules identified in CT screening: can biomarkers assist diagnosis? Biomark Med, 2016, 10(2): 137-143.
23.	Chapman CJ, Healey GF, Murray A, et al. EarlyCDT-Lung test: improved clinical utility through additional autoantibody assays. Tumour Biol, 2012, 33(5): 1319-1326.
24.	Jett JR, Peek LJ, Fredericks L, et al. Audit of the autoantibody test, EarlyCDT-Lung, in 1600 patients: an evaluation of its performance in routine clinical practice. Lung Cancer, 2014, 83(1): 51-55.
25.	Massion PP, Healey GF, Peek LJ, et al. Autoantibody signature enhances the positive predictive power of computed tomography and nodule-based risk models for detection of lung cancer. J Thorac Oncol, 2017, 12(3): 578-584.
26.	Ren S, Zhang S, Jiang T, et al. Early detection of lung cancer by using an autoantibody panel in Chinese population. Oncoimmunology, 2018, 7(2): e1384108.
27.	Zhang R, Ma L, Li W, et al. Diagnostic value of multiple tumor-associated autoantibodies in lung cancer. Onco Targets Ther, 2019, 12: 457-469.
28.	Huang H, Luo W, Ni Y, et al. The diagnostic efficiency of seven autoantibodies in lung cancer. Eur J Cancer Prev, 2020, 29(4): 315-320.
29.	罗国庆, 卢潇, 李定慧, 等. 2024年第5版《NCCN肿瘤临床实践指南: 非小细胞肺癌》更新解读. 中国胸心血管外科临床杂志, 2024, 31(7): 955-961.Luo GQ, Lu X, Li DH, et al. Interpretation of the updated NCCN clinical practice guidelines in oncology: non-small cell lung cancer (version 5. 2024). Chin J Clin Thorac Cardiovasc Surg, 2024, 31(7): 955-961.
30.	王家乐, 邱天羽, 崔亚男, 等. 2024 CSCO非小细胞肺癌指南(晚期部分)解读. 同济大学学报(医学版), 2024, 45(4): 465-470.Wang JL, Qiu TY, Cui YN, et al. Interpretation of the 2024 CSCO non-small cell lung cancer guidelines (advanced stage). J Tongji Univ (Med Sci), 2024, 45(4): 465-470.
31.	Jiang P, Wang K, Wei Y, et al. Serum autoantibody-based biomarkers for prognosis in early-stage lung cancer patients with surgical resection. Biomarkers, 2025, 30(2): 131-139.
32.	张守宇, 陈勃江, 李为民. 人工智能在肺癌早期诊断与精准治疗中的应用与挑战. 肿瘤防治研究, 2024, 51(12): 1000-1006.Zhang SY, Chen BJ, Li WM. Application and challenges of artificial intelligence in the early diagnosis and precision treatment of lung cancer. Cancer Res Prev Treat, 2024, 51(12): 1000-1006.
33.	Shimizu H, Nakayama KI. Artificial intelligence in oncology. Cancer Sci, 2020, 111(5): 1452-1460.
34.	Li C, Yan Y, Lin W, et al. Enhancing cancer subtype classification through convolutional neural networks: a deepinsight analysis of TCGA gene expression data. Health Inf Sci Syst, 2025, 13(1): 33.
35.	Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer, 2012, 48(4): 441-446.
36.	Zwanenburg A, Vallieres M, Abdalah MA, et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology, 2020, 295(2): 328-338.
37.	Tuminello S, Flores R, Untalan M, et al. Predicted effect of incidental pulmonary nodule findings on NSCLC mortality. J Thorac Oncol, 2025, 20(3): 273-284.
38.	Liu JA, Yang IY, Tsai EB. Artificial intelligence (AI) for lung nodules, from the AJR special series on AI applications. AJR Am J Roentgenol, 2022, 219(5): 703-712.
39.	Boubnovski Martell M, Linton-Reid K, Chen M, et al. Radiomics for lung cancer diagnosis, management, and future prospects. Clin Radiol, 2025, 86: 106926.
40.	Hendrix W, Hendrix N, Scholten ET, et al. Deep learning for the detection of benign and malignant pulmonary nodules in non-screening chest CT scans. Commun Med (Lond), 2023, 3(1): 156.
41.	Vyas A, Kumar K, Sharma A, et al. Advancing the frontier of artificial intelligence on emerging technologies to redefine cancer diagnosis and care. Comput Biol Med, 2025, 191: 110178.
42.	苗光, 李朝锋. 二维和三维卷积神经网络相结合的CT图像肺结节检测方法. 激光与光电子学进展, 2018, 55(5): 135-143.Miao G, Li CF. Lung nodule detection method in CT images combining two-dimensional and three-dimensional convolutional neural networks. Laser Optoelectron Prog, 2018, 55(5): 135-143.
43.	Li R, Xiao C, Huang Y, et al. Deep learning applications in computed tomography images for pulmonary nodule detection and diagnosis: a review. Diagnostics (Basel), 2022, 12(2): 489.
44.	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.
45.	Chassagnon G, de Margerie-Mellon C, Vakalopoulou M, et al. Artificial intelligence in lung cancer: current applications and perspectives. Jpn J Radiol, 2023, 41(3): 235-244.
46.	van Riel SJ, Ciompi F, Winkler Wille MM, et al. Malignancy risk estimation of pulmonary nodules in screening CTs: comparison between a computer model and human observers. PLoS One, 2017, 12(11): e0185032.
47.	Mikhael PG, Wohlwend J, Yala A, et al. Sybil: a validated deep learning model to predict future lung cancer risk from a single low-dose chest computed tomography. J Clin Oncol, 2023, 41(12): 2191-2200.
48.	Schneider BJ, Ismaila N, Aerts J, et al. Lung cancer surveillance after definitive curative-intent therapy: ASCO guideline. J Clin Oncol, 2020, 38(7): 753-766.
49.	Park J, Rho MJ, Moon MH. Enhanced deep learning model for precise nodule localization and recurrence risk prediction following curative-intent surgery for lung cancer. PLoS One, 2024, 19(7): e0300442.
50.	Chen J, Wee L, Dekker A, et al. Using 3D deep features from CT scans for cancer prognosis based on a video classification model: a multi-dataset feasibility study. Med Phys, 2023, 50(7): 4220-4233.
51.	Gong J, Liu J, Li H, et al. Deep learning-based stage-wise risk stratification for early lung adenocarcinoma in CT images: a multi-center study. Cancers (Basel), 2021, 13(13): 3326.
52.	Wang W, Zhuang R, Ma H, et al. The diagnostic value of a seven-autoantibody panel and a nomogram with a scoring table for predicting the risk of non-small-cell lung cancer. Cancer Sci, 2020, 111(5): 1699-1710.
53.	桂国华, 畅龙, 胡炎兴, 等. 血清CEA、Dickkopf-1检测联合低剂量螺旋CT扫描在肺癌早期诊断中的价值分析. 中国CT和MRI杂志, 2022, 20(1): 67-70.Gui GH, Chang L, Hu YX, et al. Value analysis of serum CEA and Dickkopf-1 detection combined with low-dose spiral CT scan in the early diagnosis of lung cancer. Chin J CT MRI, 2022, 20(1): 67-70.
54.	刘霄, 江智蛟, 马铮, 等. 七项TAAbs联合影像临床特征在肺部结节恶性风险评估中的研究. 临床肺科杂志, 2021, 26(9): 1415-1419.Liu X, Jiang ZJ, Ma Z, et al. Study on seven tumor-associated autoantibodies combined with imaging and clinical features in the evaluation of malignant risk of pulmonary nodules. J Clin Pulm Med, 2021, 26(9): 1415-1419.
55.	Chen J, Ming M, Huang S, et al. AI-enhanced diagnostic model for pulmonary nodule classification. Front Oncol, 2024, 14: 1417753.
56.	Xu L, Chang N, Yang T, et al. Development of diagnosis model for early lung nodules based on a seven autoantibodies panel and imaging features. Front Oncol, 2022, 12: 883543.

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. 唐旭, 王瑾, 周文生, 等. 基于循环肿瘤DNA液体活检监测分子残留病灶在非小细胞肺癌诊疗中的应用进展. 中国胸心血管外科临床杂志, 2025.Epub ahead of print]. Tang X, Wang J, Zhou WS, et al. Application progress of circulating tumor DNA liquid biopsy in monitoring minimal residual disease in non-small cell lung cancer. Chin J Clin Thorac Cardiovasc Surg, 2025, [Epub ahead of print].
3. Minami Y. The notable topics of the 5th edition of WHO classification for the thoracic tumours (2021). Gan To Kagaku Ryoho, 2022, 49(8): 847-852.
4. Goldstraw P, Chansky K, Crowley J, et al. The IASLC lung cancer staging project: proposals for revision of the TNM stage groupings in the forthcoming (eighth) edition of the TNM classification for lung cancer. J Thorac Oncol, 2016, 11(1): 39-51.
5. Liu Y, Gao Y, Wu Y, et al. Autoantibodies as potential liquid biopsy biomarker in detection of pancreatic cancer: a diagnostic test accuracy review and meta-analysis. Scand J Immunol, 2025, 101(4): e70012.
6. Ouyang R, Wu S, Zhang B, et al. Clinical value of tumor-associated antigens and autoantibody panel combination detection in the early diagnostic of lung cancer. Cancer Biomark, 2021, 32(3): 401-409.
7. Graeve VIJ, Laures S, Spirig A, et al. Implementation of an AI algorithm in clinical practice to reduce missed incidental pulmonary embolisms on chest CT and its impact on short-term survival. Invest Radiol, 2025, 60(4): 260-266.
8. 桑亚丽, 宋丽沙, 彭明月, 等. 肺癌患者血清中外泌体潜在自身抗体标志物的研究进展. 内蒙古医学杂志, 2025, 57(5): 569-573.Sang YL, Song LS, Peng MY, et al. Research progress on potential autoantibody markers in serum exosomes of lung cancer patients. Inner Mongol Med J, 2025, 57(5): 569-573.
9. 孙硕, 王锋, 何立, 等. 液体活检生物标志物及其联合影像学在肺癌早期诊断中应用的研究进展. 中国胸心血管外科临床杂志, 2023, 30(2): 313-319.Sun S, Wang F, He L, et al. Research progress on liquid biopsy biomarkers and their combination with imaging in the early diagnosis of lung cancer. Chin J Clin Thorac Cardiovasc Surg, 2023, 30(2): 313-319.
10. 王冰, 邱红. 肿瘤标志物在肺癌诊断中的研究进展. 国际检验医学杂志, 2013, 34(24): 3384-3386.Wang B, Qiu H. Research progress on tumor markers in the diagnosis of lung cancer. Int J Lab Med, 2013, 34(24): 3384-3386.
11. 王柳倩, 马为. 肿瘤标志物在肺癌的早期诊断及预后评估中的研究进展. 现代肿瘤医学, 2014, 22(12): 3004-3007.Wang LQ, Ma W. Research progress on tumor markers in early diagnosis and prognosis evaluation of lung cancer. J Mod Oncol, 2014, 22(12): 3004-3007.
12. 吴东霞, 沈溪明. 肿瘤标志物在肺癌早期诊断与治疗中的价值. 吉林医学, 2016, 37(8): 1976-1977.Wu DX, Shen XM. The value of tumor markers in the early diagnosis and treatment of lung cancer. Jilin Med J, 2016, 37(8): 1976-1977.
13. Xu Y, Zhang W, Xia T, et al. Diagnostic value of tumor-associated autoantibodies panel in combination with traditional tumor markers for lung cancer. Front Oncol, 2023, 13: 1022331.
14. 周潇, 邹强, 张岩. 7项肿瘤相关抗原自身抗体在不同分期非小细胞肺癌诊断中临床意义. 临床军医杂志, 2022, 50(9): 922-925.Zhou X, Zou Q, Zhang Y. Clinical significance of seven tumor-associated antigen autoantibodies in the diagnosis of non-small cell lung cancer at different stages. J Clin Military Surgeon, 2022, 50(9): 922-925.
15. Tong L, Sun J, Zhang X, et al. Development of an autoantibody panel for early detection of lung cancer in the Chinese population. Front Med (Lausanne), 2023, 10: 1209747.
16. 张少华, 杜文水. 人血清涎液化糖链抗原磁微粒化学发光免疫分析方法的建立及对肺癌的诊断价值. 医疗装备, 2024, 37(1): 95-101.Zhang SH, Du WS. Establishment of a magnetic particle chemiluminescence immunoassay for human serum sialylated glycoprotein antigen and its diagnostic value for lung cancer. Med Equipment, 2024, 37(1): 95-101.
17. He T, Wu Z, Xia P, et al. The combination of a seven-autoantibody panel with computed tomography scanning can enhance the diagnostic efficiency of non-small cell lung cancer. Front Oncol, 2022, 12: 1047019.
18. Qin J, Zeng N, Yang T, et al. Diagnostic value of autoantibodies in lung cancer: a systematic review and meta-analysis. Cell Physiol Biochem, 2018, 51(6): 2631-2646.
19. Xie F, Xu L, Mu Y, et al. Diagnostic value of seven autoantibodies combined with CEA and CA199 in non-small cell lung cancer. Clin Lab, 2023, 69(5): 789-795.
20. Mu YY, Xie FY, Wang FB, et al. Performance evaluation of an enzyme-linked immunosorbent assay for seven autoantibodies in lung cancer. Clin Lab, 2019, 65(4): 475-482.
21. Chen Q, Zhu S, Jiao N, et al. Improvement in the performance of an autoantibody panel in combination with heat shock protein 90a for the detection of early-stage lung cancer. Exp Ther Med, 2023, 25(2): 82.
22. Veronesi G, Bianchi F, Infante M, et al. The challenge of small lung nodules identified in CT screening: can biomarkers assist diagnosis? Biomark Med, 2016, 10(2): 137-143.
23. Chapman CJ, Healey GF, Murray A, et al. EarlyCDT-Lung test: improved clinical utility through additional autoantibody assays. Tumour Biol, 2012, 33(5): 1319-1326.
24. Jett JR, Peek LJ, Fredericks L, et al. Audit of the autoantibody test, EarlyCDT-Lung, in 1600 patients: an evaluation of its performance in routine clinical practice. Lung Cancer, 2014, 83(1): 51-55.
25. Massion PP, Healey GF, Peek LJ, et al. Autoantibody signature enhances the positive predictive power of computed tomography and nodule-based risk models for detection of lung cancer. J Thorac Oncol, 2017, 12(3): 578-584.
26. Ren S, Zhang S, Jiang T, et al. Early detection of lung cancer by using an autoantibody panel in Chinese population. Oncoimmunology, 2018, 7(2): e1384108.
27. Zhang R, Ma L, Li W, et al. Diagnostic value of multiple tumor-associated autoantibodies in lung cancer. Onco Targets Ther, 2019, 12: 457-469.
28. Huang H, Luo W, Ni Y, et al. The diagnostic efficiency of seven autoantibodies in lung cancer. Eur J Cancer Prev, 2020, 29(4): 315-320.
29. 罗国庆, 卢潇, 李定慧, 等. 2024年第5版《NCCN肿瘤临床实践指南: 非小细胞肺癌》更新解读. 中国胸心血管外科临床杂志, 2024, 31(7): 955-961.Luo GQ, Lu X, Li DH, et al. Interpretation of the updated NCCN clinical practice guidelines in oncology: non-small cell lung cancer (version 5. 2024). Chin J Clin Thorac Cardiovasc Surg, 2024, 31(7): 955-961.
30. 王家乐, 邱天羽, 崔亚男, 等. 2024 CSCO非小细胞肺癌指南(晚期部分)解读. 同济大学学报(医学版), 2024, 45(4): 465-470.Wang JL, Qiu TY, Cui YN, et al. Interpretation of the 2024 CSCO non-small cell lung cancer guidelines (advanced stage). J Tongji Univ (Med Sci), 2024, 45(4): 465-470.
31. Jiang P, Wang K, Wei Y, et al. Serum autoantibody-based biomarkers for prognosis in early-stage lung cancer patients with surgical resection. Biomarkers, 2025, 30(2): 131-139.
32. 张守宇, 陈勃江, 李为民. 人工智能在肺癌早期诊断与精准治疗中的应用与挑战. 肿瘤防治研究, 2024, 51(12): 1000-1006.Zhang SY, Chen BJ, Li WM. Application and challenges of artificial intelligence in the early diagnosis and precision treatment of lung cancer. Cancer Res Prev Treat, 2024, 51(12): 1000-1006.
33. Shimizu H, Nakayama KI. Artificial intelligence in oncology. Cancer Sci, 2020, 111(5): 1452-1460.
34. Li C, Yan Y, Lin W, et al. Enhancing cancer subtype classification through convolutional neural networks: a deepinsight analysis of TCGA gene expression data. Health Inf Sci Syst, 2025, 13(1): 33.
35. Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer, 2012, 48(4): 441-446.
36. Zwanenburg A, Vallieres M, Abdalah MA, et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology, 2020, 295(2): 328-338.
37. Tuminello S, Flores R, Untalan M, et al. Predicted effect of incidental pulmonary nodule findings on NSCLC mortality. J Thorac Oncol, 2025, 20(3): 273-284.
38. Liu JA, Yang IY, Tsai EB. Artificial intelligence (AI) for lung nodules, from the AJR special series on AI applications. AJR Am J Roentgenol, 2022, 219(5): 703-712.
39. Boubnovski Martell M, Linton-Reid K, Chen M, et al. Radiomics for lung cancer diagnosis, management, and future prospects. Clin Radiol, 2025, 86: 106926.
40. Hendrix W, Hendrix N, Scholten ET, et al. Deep learning for the detection of benign and malignant pulmonary nodules in non-screening chest CT scans. Commun Med (Lond), 2023, 3(1): 156.
41. Vyas A, Kumar K, Sharma A, et al. Advancing the frontier of artificial intelligence on emerging technologies to redefine cancer diagnosis and care. Comput Biol Med, 2025, 191: 110178.
42. 苗光, 李朝锋. 二维和三维卷积神经网络相结合的CT图像肺结节检测方法. 激光与光电子学进展, 2018, 55(5): 135-143.Miao G, Li CF. Lung nodule detection method in CT images combining two-dimensional and three-dimensional convolutional neural networks. Laser Optoelectron Prog, 2018, 55(5): 135-143.
43. Li R, Xiao C, Huang Y, et al. Deep learning applications in computed tomography images for pulmonary nodule detection and diagnosis: a review. Diagnostics (Basel), 2022, 12(2): 489.
44. 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.
45. Chassagnon G, de Margerie-Mellon C, Vakalopoulou M, et al. Artificial intelligence in lung cancer: current applications and perspectives. Jpn J Radiol, 2023, 41(3): 235-244.
46. van Riel SJ, Ciompi F, Winkler Wille MM, et al. Malignancy risk estimation of pulmonary nodules in screening CTs: comparison between a computer model and human observers. PLoS One, 2017, 12(11): e0185032.
47. Mikhael PG, Wohlwend J, Yala A, et al. Sybil: a validated deep learning model to predict future lung cancer risk from a single low-dose chest computed tomography. J Clin Oncol, 2023, 41(12): 2191-2200.
48. Schneider BJ, Ismaila N, Aerts J, et al. Lung cancer surveillance after definitive curative-intent therapy: ASCO guideline. J Clin Oncol, 2020, 38(7): 753-766.
49. Park J, Rho MJ, Moon MH. Enhanced deep learning model for precise nodule localization and recurrence risk prediction following curative-intent surgery for lung cancer. PLoS One, 2024, 19(7): e0300442.
50. Chen J, Wee L, Dekker A, et al. Using 3D deep features from CT scans for cancer prognosis based on a video classification model: a multi-dataset feasibility study. Med Phys, 2023, 50(7): 4220-4233.
51. Gong J, Liu J, Li H, et al. Deep learning-based stage-wise risk stratification for early lung adenocarcinoma in CT images: a multi-center study. Cancers (Basel), 2021, 13(13): 3326.
52. Wang W, Zhuang R, Ma H, et al. The diagnostic value of a seven-autoantibody panel and a nomogram with a scoring table for predicting the risk of non-small-cell lung cancer. Cancer Sci, 2020, 111(5): 1699-1710.
53. 桂国华, 畅龙, 胡炎兴, 等. 血清CEA、Dickkopf-1检测联合低剂量螺旋CT扫描在肺癌早期诊断中的价值分析. 中国CT和MRI杂志, 2022, 20(1): 67-70.Gui GH, Chang L, Hu YX, et al. Value analysis of serum CEA and Dickkopf-1 detection combined with low-dose spiral CT scan in the early diagnosis of lung cancer. Chin J CT MRI, 2022, 20(1): 67-70.
54. 刘霄, 江智蛟, 马铮, 等. 七项TAAbs联合影像临床特征在肺部结节恶性风险评估中的研究. 临床肺科杂志, 2021, 26(9): 1415-1419.Liu X, Jiang ZJ, Ma Z, et al. Study on seven tumor-associated autoantibodies combined with imaging and clinical features in the evaluation of malignant risk of pulmonary nodules. J Clin Pulm Med, 2021, 26(9): 1415-1419.
55. Chen J, Ming M, Huang S, et al. AI-enhanced diagnostic model for pulmonary nodule classification. Front Oncol, 2024, 14: 1417753.
56. Xu L, Chang N, Yang T, et al. Development of diagnosis model for early lung nodules based on a seven autoantibodies panel and imaging features. Front Oncol, 2022, 12: 883543.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Latest ArticlesResearch progress on autoantibody liquid biopsy and AI-based radiomics in the diagnosis and treatment of non-small cell lung cancer

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