Research progress on the characteristics and rapid diagnostic tools of early lung adenocarcinoma subtypes_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

WANG Kui ^1,2 , ZHANG Hongyi ² , PANG Yao ² ,  ZHU Zijiang ^1,2

1. The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou, 730000, P. R. China;
2. The Second Department of Thoracic Surgery, Gansu Provincial Hospital, Lanzhou, 730000, P. R. China;

Corresponding author：

ZHU Zijiang, Email: 18419510885@163.com

Keywords：

Lung adenocarcinoma subtypes; micropapillary; solid; rapid diagnosis; semi-dry dot-blot; artificial intelligence; mass spectrometry; review

DOI：

10.7507/1007-4848.202111041

Video：

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Abstract Full text Figures/Tables Video References Cited by

Lung adenocarcinoma has become the most common type of lung cancer. According to the 2015 World Health Organization histological classification of lung cancer, invasive lung adenocarcinoma can be divided into 5 subtypes: lepidic, acinar, papillary, solid, and micropapillary. Relevant studies have shown that the local lobectomy or sublobectomy is sufficient for early lepidic predominant adenocarcinoma, while lobectomy should be recommended for tumors containing micropapillary and solid ingredients (≥5%). Currently, the percentage of micropapillary and solid components diagnosed by frozen pathological examination is 65.7%, and the accuracy of diagnosis is limited. Therefore, to improve the accuracy of diagnosis, it is necessary to seek new methods and techniques. This paper summarized the characteristics and rapid diagnosis tools of early lung adenocarcinoma subtypes.

Citation： WANG Kui, ZHANG Hongyi, PANG Yao, ZHU Zijiang. Research progress on the characteristics and rapid diagnostic tools of early lung adenocarcinoma subtypes. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2023, 30(10): 1507-1512. doi: 10.7507/1007-4848.202111041 Copy

1.	曹毛毛, 陈万青. GLOBOCAN 2020全球癌症统计数据解读. 中国医学前沿杂志 (电子版), 2021, 13(3): 63-69.
2.	刘宗超, 李哲轩, 张阳, 等. 2020全球癌症统计报告解读. 肿瘤综合治疗电子杂志, 2021, 7(2): 1-13.
3.	Wu G, Woodruff HC, Sanduleanu S, et al. Preoperative CT-based radiomics combined with intraoperative frozen section is predictive of invasive adenocarcinoma in pulmonary nodules: A multicenter study. Eur Radiol, 2020, 30(5): 2680-2691.
4.	Mengoli MC, Longo FR, Fraggetta F, et al. The 2015 World Health Organization classification of lung tumors: New entities since the 2004 classification. Pathologica, 2018, 110(1): 39-67.
5.	Inamura K. Clinicopathological characteristics and mutations driving development of early lung adenocarcinoma: Tumor initiation and progression. Int J Mol Sci, 2018, 19(4): 1259.
6.	杨欣, 林冬梅. 2015版WHO肺癌组织学分类变化及其临床意义. 中国肺癌杂志, 2016, 19(6): 332-336.
7.	Zombori T, Nyári T, Tiszlavicz L, et al. The more the micropapillary pattern in stage Ⅰ lung adenocarcinoma, the worse the prognosis-a retrospective study on digitalized slides. Virchows Arch, 2018, 472(6): 949-958.
8.	Shi Y, Wu S, Ma S, et al. Comparison between wedge resection and lobectomy/segmentectomy for early-stage non-small cell lung cancer: A Bayesian meta-analysis and systematic review. Ann Surg Oncol, 2022, 29(3): 1868-1879.
9.	Gossot D, Mariolo AV, Lefevre M, et al. Strategies of lymph node dissection during sublobar resection for early-stage lung cancer. Front Surg, 2021, 8: 725005.
10.	Sun W, Su H, Liu J, et al. Impact of histological components on selecting limited lymphadenectomy for lung adenocarcinoma≤2 cm. Lung Cancer, 2020, 150: 36-43.
11.	Nitadori J, Bograd AJ, Kadota K, et al. Impact of micropapillary histologic subtype in selecting limited resection vs. lobectomy for lung adenocarcinoma of 2 cm or smaller. J Natl Cancer Inst, 2013, 105(16): 1212-1220.
12.	Su H, Xie H, Dai C, et al. Procedure-specific prognostic impact of micropapillary subtype may guide resection strategy in small-sized lung adenocarcinomas: A multicenter study. Ther Adv Med Oncol, 2020, 12: 1758835920937893.
13.	Hung JJ, Jeng WJ, Chou TY, et al. Prognostic value of the new International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society lung adenocarcinoma classification on death and recurrence in completely resected stageⅠlung adenocarcinoma. Ann Surg, 2013, 258(6): 1079-1086.
14.	刘琳, 焦宗林, 曾媛, 等. 浸润性肺腺癌不同病理亚型的研究进展. 癌症进展, 2020, 18(5): 436-438, 466.
15.	Zhao ZR, To KF, Mok TS, et al. Is there significance in identification of non-predominant micropapillary or solid components in early-stage lung adenocarcinoma? Interact Cardiovasc Thorac Surg, 2017, 24(1): 121-125.
16.	何小群, 罗天友, 李琦, 等. 浸润性肺腺癌不同病理亚型的临床病理及CT特征分析. 第三军医大学学报, 2020, 42(19): 1950-1956.
17.	Qian J, Zhao S, Zou Y, et al. Genomic underpinnings of tumor behavior in in situ and early lung adenocarcinoma. Am J Respir Crit Care Med, 2020, 201(6): 697-706.
18.	Kawasaki K, Nojima S, Hijiki S, et al. FAM111B enhances proliferation of KRAS-driven lung adenocarcinoma by degrading p16. Cancer Sci, 2020, 111(7): 2635-2646.
19.	Molina-Romero C, Rangel-Escareño C, Ortega-Gómez A, et al. Differential gene expression profiles according to the Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society histopathological classification in lung adenocarcinoma subtypes. Hum Pathol, 2017, 66: 188-199.
20.	Gomez-Puerto MC, Iyengar PV, García de Vinuesa A, et al. Bone morphogenetic protein receptor signal transduction in human disease. J Pathol, 2019, 247(1): 9-20.
21.	Deng T, Lin D, Zhang M, et al. Differential expression of bone morphogenetic protein 5 in human lung squamous cell carcinoma and adenocarcinoma. Acta Biochim Biophys Sin (Shanghai), 2015, 47(7): 557-563.
22.	Zhou J, Liu B, Li Z, et al. Proteomic analyses identify differentially expressed proteins and pathways between low-risk and high-risk subtypes of early-stage lung adenocarcinoma and their prognostic impacts. Mol Cell Proteomics, 2021, 20: 100015.
23.	Cui N, Hu M, Khalil RA. Biochemical and biological attributes of matrix metalloproteinases. Prog Mol Biol Transl Sci, 2017, 147: 1-73.
24.	Yu Y, Ding Z, Jian H, et al. Prognostic value of MMP9 activity level in resected stageⅠB lung adenocarcinoma. Cancer Med, 2016, 5(9): 2323-2331.
25.	Montagne F, Guisier F, Venissac N, et al. The role of surgery in lung cancer treatment: Present indications and future perspectives-state of the art. Cancers (Basel), 2021, 13(15): 3711.
26.	Okada M. Radical sublobar resection for small-diameter lung cancers. Thorac Surg Clin, 2013, 23(3): 301-311.
27.	Ettinger DS, Wood DE, Aisner DL, et al. Non-small cell lung cancer, version 5. 2017, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw, 2017, 15(4): 504-535.
28.	Zeng W, Zhang W, Zhang J, et al. Systematic review and meta-analysis of video-assisted thoracoscopic surgery segmentectomy versus lobectomy for stageⅠ non-small cell lung cancer. World J Surg Oncol, 2020, 18(1): 44.
29.	Berg E, Madelaine L, Baste JM, et al. Interest of anatomical segmentectomy over lobectomy for lung cancer: A nationwide study. J Thorac Dis, 2021, 13(6): 3587-3596.
30.	Kent MS, Mandrekar SJ, Landreneau R, et al. Impact of sublobar resection on pulmonary function: Long-term results from American College of Surgeons Oncology Group Z4032 (Alliance). Ann Thorac Surg, 2016, 102(1): 230-238.
31.	Yoshida Y, Nitadori JI, Shinozaki-Ushiku A, et al. Micropapillary histological subtype in lung adenocarcinoma of 2 cm or less: Impact on recurrence and clinical predictors. Gen Thorac Cardiovasc Surg, 2017, 65(5): 273-279.
32.	Yeh YC, Nitadori J, Kadota K, et al. Using frozen section to identify histological patterns in stageⅠ lung adenocarcinoma of ≤3 cm: Accuracy and interobserver agreement. Histopathology, 2015, 66(7): 922-938.
33.	Trejo Bittar HE, Incharoen P, Althouse AD, et al. Accuracy of the IASLC/ATS/ERS histological subtyping of stageⅠlung adenocarcinoma on intraoperative frozen sections. Mod Pathol, 2015, 28(8): 1058-1063.
34.	Romanov V, Brooks BD. Antibody printing technologies. Methods Mol Biol, 2021, 2237: 151-177.
35.	刘嘉, 李冬, 王徐, 等. 蛋白质组学的研究进展. 现代医药卫生, 2019, 35(9): 1380-1384.
36.	Shan Q, Lou X, Xiao T, et al. A cancer/testis antigen microarray to screen autoantibody biomarkers of non-small cell lung cancer. Cancer Lett, 2013, 328(1): 160-167.
37.	邓安梅, 仲人前, 陈孙孝, 等. 用蛋白质芯片联合检测肺癌患者血清中的肿瘤标志物. 中国免疫学杂志, 2002, 18(10): 701-702.
38.	Han MK, Oh YH, Kang J, et al. Protein profiling in human sera for identification of potential lung cancer biomarkers using antibody microarray. Proteomics, 2009, 9(24): 5544-5552.
39.	Nagano K. Development and evaluation of antibody proteomics technology for rapid and comprehensive identification of potential biomarkers and therapeutic targets. Biol Pharm Bull, 2018, 41(5): 663-669.
40.	Hirakawa H, Shibata K, Ohzono E. Use of a semi-dry dot-blot for rapid detection of lymph node metastasis. Clin Chim Acta, 2010, 411(15-16): 1149-1150.
41.	Otsubo R, Oikawa M, Hirakawa H, et al. Novel diagnostic procedure for determining metastasis to sentinel lymph nodes in breast cancer using a semi-dry dot-blot method. Int J Cancer, 2014, 134(4): 905-912.
42.	Tomoshige K, Tsuchiya T, Otsubo R, et al. Intraoperative diagnosis of lymph node metastasis in non-small-cell lung cancer by a semi-dry dot-blot method. Eur J Cardiothorac Surg, 2016, 49(2): 617-622.
43.	Otsubo R, Hirakawa H, Oikawa M, et al. Validation of a novel diagnostic kit using the semidry dot-blot method to detect metastatic lymph nodes in breast cancer: Distinguishing macrometastases from nonmacrometastases. Clin Breast Cancer, 2018, 18(3): e345-e351.
44.	Perez CJ, Bagga AK, Prova SS, et al. Review and perspectives on the applications of mass spectrometry imaging under ambient conditions. Rapid Commun Mass Spectrom, 2019, 33 Suppl 3: 27-53.
45.	Zhang L, Zhu B, Zeng Y, et al. Clinical lipidomics in understanding of lung cancer: Opportunity and challenge. Cancer Lett, 2020, 470: 75-83.
46.	Lu H, Zhang H, Wei Y, et al. Ambient mass spectrometry for the molecular diagnosis of lung cancer. Analyst, 2020, 145(2): 313-320.
47.	Lee GK, Lee HS, Park YS, et al. Lipid MALDI profile classifies non-small cell lung cancers according to the histologic type. Lung Cancer, 2012, 76(2): 197-203.
48.	徐建军, 陈立如, 魏益平, 等. 常压直接质谱技术在快速鉴别肺癌与癌旁组织中的应用. 广东医学, 2014, 35(8): 1179-1182.
49.	Zhang J, Huang Y, Tang X, et al. Roles of lipid profiles in human non-small cell lung cancer. Technol Cancer Res Treat, 2021, 20: 15330338211041472.
50.	董来东, 黄果. 基于CT影像的人工智能辅助诊断系统对4771例肺癌诊断价值的系统评价与Meta分析. 中国胸心血管外科临床杂志, 2021, 28(10): 1183-1191.
51.	陶雪敏, 方瑞, 吴重重, 等. 深度学习模型对纯磨玻璃结节肺腺癌病理亚型的预测分析. 中国医学科学院学报, 2020, 42(4): 477-484.
52.	Wang Y, Zhou L, Wang M, et al. Combination of generative adversarial network and convolutional neural network for automatic subcentimeter pulmonary adenocarcinoma classification. Quant Imaging Med Surg, 2020, 10(6): 1249-1264.
53.	He B, Song Y, Wang L, et al. A machine learning-based prediction of the micropapillary/solid growth pattern in invasive lung adenocarcinoma with radiomics. Transl Lung Cancer Res, 2021, 10(2): 955-964.
54.	余婷, 王鹏, 钱菁, 等. 数字图像分析在病理诊断中的应用综述. 解放军医学院学报, 2020, 41(5): 539-542.
55.	Yu KH, Zhang C, Berry GJ, et al. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nat Commun, 2016, 7: 12474.
56.	Teramoto A, Tsukamoto T, Kiriyama Y, et al. Automated classification of lung cancer types from cytological images using deep convolutional neural networks. Biomed Res Int, 2017, 2017: 4067832.
57.	Yu KH, Wang F, Berry GJ, et al. Classifying non-small cell lung cancer types and transcriptomic subtypes using convolutional neural networks. J Am Med Inform Assoc, 2020, 27(5): 757-769.
58.	Wei JW, Tafe LJ, Linnik YA, et al. Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks. Sci Rep, 2019, 9(1): 3358.

1. 曹毛毛, 陈万青. GLOBOCAN 2020全球癌症统计数据解读. 中国医学前沿杂志 (电子版), 2021, 13(3): 63-69.
2. 刘宗超, 李哲轩, 张阳, 等. 2020全球癌症统计报告解读. 肿瘤综合治疗电子杂志, 2021, 7(2): 1-13.
3. Wu G, Woodruff HC, Sanduleanu S, et al. Preoperative CT-based radiomics combined with intraoperative frozen section is predictive of invasive adenocarcinoma in pulmonary nodules: A multicenter study. Eur Radiol, 2020, 30(5): 2680-2691.
4. Mengoli MC, Longo FR, Fraggetta F, et al. The 2015 World Health Organization classification of lung tumors: New entities since the 2004 classification. Pathologica, 2018, 110(1): 39-67.
5. Inamura K. Clinicopathological characteristics and mutations driving development of early lung adenocarcinoma: Tumor initiation and progression. Int J Mol Sci, 2018, 19(4): 1259.
6. 杨欣, 林冬梅. 2015版WHO肺癌组织学分类变化及其临床意义. 中国肺癌杂志, 2016, 19(6): 332-336.
7. Zombori T, Nyári T, Tiszlavicz L, et al. The more the micropapillary pattern in stage Ⅰ lung adenocarcinoma, the worse the prognosis-a retrospective study on digitalized slides. Virchows Arch, 2018, 472(6): 949-958.
8. Shi Y, Wu S, Ma S, et al. Comparison between wedge resection and lobectomy/segmentectomy for early-stage non-small cell lung cancer: A Bayesian meta-analysis and systematic review. Ann Surg Oncol, 2022, 29(3): 1868-1879.
9. Gossot D, Mariolo AV, Lefevre M, et al. Strategies of lymph node dissection during sublobar resection for early-stage lung cancer. Front Surg, 2021, 8: 725005.
10. Sun W, Su H, Liu J, et al. Impact of histological components on selecting limited lymphadenectomy for lung adenocarcinoma≤2 cm. Lung Cancer, 2020, 150: 36-43.
11. Nitadori J, Bograd AJ, Kadota K, et al. Impact of micropapillary histologic subtype in selecting limited resection vs. lobectomy for lung adenocarcinoma of 2 cm or smaller. J Natl Cancer Inst, 2013, 105(16): 1212-1220.
12. Su H, Xie H, Dai C, et al. Procedure-specific prognostic impact of micropapillary subtype may guide resection strategy in small-sized lung adenocarcinomas: A multicenter study. Ther Adv Med Oncol, 2020, 12: 1758835920937893.
13. Hung JJ, Jeng WJ, Chou TY, et al. Prognostic value of the new International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society lung adenocarcinoma classification on death and recurrence in completely resected stageⅠlung adenocarcinoma. Ann Surg, 2013, 258(6): 1079-1086.
14. 刘琳, 焦宗林, 曾媛, 等. 浸润性肺腺癌不同病理亚型的研究进展. 癌症进展, 2020, 18(5): 436-438, 466.
15. Zhao ZR, To KF, Mok TS, et al. Is there significance in identification of non-predominant micropapillary or solid components in early-stage lung adenocarcinoma? Interact Cardiovasc Thorac Surg, 2017, 24(1): 121-125.
16. 何小群, 罗天友, 李琦, 等. 浸润性肺腺癌不同病理亚型的临床病理及CT特征分析. 第三军医大学学报, 2020, 42(19): 1950-1956.
17. Qian J, Zhao S, Zou Y, et al. Genomic underpinnings of tumor behavior in in situ and early lung adenocarcinoma. Am J Respir Crit Care Med, 2020, 201(6): 697-706.
18. Kawasaki K, Nojima S, Hijiki S, et al. FAM111B enhances proliferation of KRAS-driven lung adenocarcinoma by degrading p16. Cancer Sci, 2020, 111(7): 2635-2646.
19. Molina-Romero C, Rangel-Escareño C, Ortega-Gómez A, et al. Differential gene expression profiles according to the Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society histopathological classification in lung adenocarcinoma subtypes. Hum Pathol, 2017, 66: 188-199.
20. Gomez-Puerto MC, Iyengar PV, García de Vinuesa A, et al. Bone morphogenetic protein receptor signal transduction in human disease. J Pathol, 2019, 247(1): 9-20.
21. Deng T, Lin D, Zhang M, et al. Differential expression of bone morphogenetic protein 5 in human lung squamous cell carcinoma and adenocarcinoma. Acta Biochim Biophys Sin (Shanghai), 2015, 47(7): 557-563.
22. Zhou J, Liu B, Li Z, et al. Proteomic analyses identify differentially expressed proteins and pathways between low-risk and high-risk subtypes of early-stage lung adenocarcinoma and their prognostic impacts. Mol Cell Proteomics, 2021, 20: 100015.
23. Cui N, Hu M, Khalil RA. Biochemical and biological attributes of matrix metalloproteinases. Prog Mol Biol Transl Sci, 2017, 147: 1-73.
24. Yu Y, Ding Z, Jian H, et al. Prognostic value of MMP9 activity level in resected stageⅠB lung adenocarcinoma. Cancer Med, 2016, 5(9): 2323-2331.
25. Montagne F, Guisier F, Venissac N, et al. The role of surgery in lung cancer treatment: Present indications and future perspectives-state of the art. Cancers (Basel), 2021, 13(15): 3711.
26. Okada M. Radical sublobar resection for small-diameter lung cancers. Thorac Surg Clin, 2013, 23(3): 301-311.
27. Ettinger DS, Wood DE, Aisner DL, et al. Non-small cell lung cancer, version 5. 2017, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw, 2017, 15(4): 504-535.
28. Zeng W, Zhang W, Zhang J, et al. Systematic review and meta-analysis of video-assisted thoracoscopic surgery segmentectomy versus lobectomy for stageⅠ non-small cell lung cancer. World J Surg Oncol, 2020, 18(1): 44.
29. Berg E, Madelaine L, Baste JM, et al. Interest of anatomical segmentectomy over lobectomy for lung cancer: A nationwide study. J Thorac Dis, 2021, 13(6): 3587-3596.
30. Kent MS, Mandrekar SJ, Landreneau R, et al. Impact of sublobar resection on pulmonary function: Long-term results from American College of Surgeons Oncology Group Z4032 (Alliance). Ann Thorac Surg, 2016, 102(1): 230-238.
31. Yoshida Y, Nitadori JI, Shinozaki-Ushiku A, et al. Micropapillary histological subtype in lung adenocarcinoma of 2 cm or less: Impact on recurrence and clinical predictors. Gen Thorac Cardiovasc Surg, 2017, 65(5): 273-279.
32. Yeh YC, Nitadori J, Kadota K, et al. Using frozen section to identify histological patterns in stageⅠ lung adenocarcinoma of ≤3 cm: Accuracy and interobserver agreement. Histopathology, 2015, 66(7): 922-938.
33. Trejo Bittar HE, Incharoen P, Althouse AD, et al. Accuracy of the IASLC/ATS/ERS histological subtyping of stageⅠlung adenocarcinoma on intraoperative frozen sections. Mod Pathol, 2015, 28(8): 1058-1063.
34. Romanov V, Brooks BD. Antibody printing technologies. Methods Mol Biol, 2021, 2237: 151-177.
35. 刘嘉, 李冬, 王徐, 等. 蛋白质组学的研究进展. 现代医药卫生, 2019, 35(9): 1380-1384.
36. Shan Q, Lou X, Xiao T, et al. A cancer/testis antigen microarray to screen autoantibody biomarkers of non-small cell lung cancer. Cancer Lett, 2013, 328(1): 160-167.
37. 邓安梅, 仲人前, 陈孙孝, 等. 用蛋白质芯片联合检测肺癌患者血清中的肿瘤标志物. 中国免疫学杂志, 2002, 18(10): 701-702.
38. Han MK, Oh YH, Kang J, et al. Protein profiling in human sera for identification of potential lung cancer biomarkers using antibody microarray. Proteomics, 2009, 9(24): 5544-5552.
39. Nagano K. Development and evaluation of antibody proteomics technology for rapid and comprehensive identification of potential biomarkers and therapeutic targets. Biol Pharm Bull, 2018, 41(5): 663-669.
40. Hirakawa H, Shibata K, Ohzono E. Use of a semi-dry dot-blot for rapid detection of lymph node metastasis. Clin Chim Acta, 2010, 411(15-16): 1149-1150.
41. Otsubo R, Oikawa M, Hirakawa H, et al. Novel diagnostic procedure for determining metastasis to sentinel lymph nodes in breast cancer using a semi-dry dot-blot method. Int J Cancer, 2014, 134(4): 905-912.
42. Tomoshige K, Tsuchiya T, Otsubo R, et al. Intraoperative diagnosis of lymph node metastasis in non-small-cell lung cancer by a semi-dry dot-blot method. Eur J Cardiothorac Surg, 2016, 49(2): 617-622.
43. Otsubo R, Hirakawa H, Oikawa M, et al. Validation of a novel diagnostic kit using the semidry dot-blot method to detect metastatic lymph nodes in breast cancer: Distinguishing macrometastases from nonmacrometastases. Clin Breast Cancer, 2018, 18(3): e345-e351.
44. Perez CJ, Bagga AK, Prova SS, et al. Review and perspectives on the applications of mass spectrometry imaging under ambient conditions. Rapid Commun Mass Spectrom, 2019, 33 Suppl 3: 27-53.
45. Zhang L, Zhu B, Zeng Y, et al. Clinical lipidomics in understanding of lung cancer: Opportunity and challenge. Cancer Lett, 2020, 470: 75-83.
46. Lu H, Zhang H, Wei Y, et al. Ambient mass spectrometry for the molecular diagnosis of lung cancer. Analyst, 2020, 145(2): 313-320.
47. Lee GK, Lee HS, Park YS, et al. Lipid MALDI profile classifies non-small cell lung cancers according to the histologic type. Lung Cancer, 2012, 76(2): 197-203.
48. 徐建军, 陈立如, 魏益平, 等. 常压直接质谱技术在快速鉴别肺癌与癌旁组织中的应用. 广东医学, 2014, 35(8): 1179-1182.
49. Zhang J, Huang Y, Tang X, et al. Roles of lipid profiles in human non-small cell lung cancer. Technol Cancer Res Treat, 2021, 20: 15330338211041472.
50. 董来东, 黄果. 基于CT影像的人工智能辅助诊断系统对4771例肺癌诊断价值的系统评价与Meta分析. 中国胸心血管外科临床杂志, 2021, 28(10): 1183-1191.
51. 陶雪敏, 方瑞, 吴重重, 等. 深度学习模型对纯磨玻璃结节肺腺癌病理亚型的预测分析. 中国医学科学院学报, 2020, 42(4): 477-484.
52. Wang Y, Zhou L, Wang M, et al. Combination of generative adversarial network and convolutional neural network for automatic subcentimeter pulmonary adenocarcinoma classification. Quant Imaging Med Surg, 2020, 10(6): 1249-1264.
53. He B, Song Y, Wang L, et al. A machine learning-based prediction of the micropapillary/solid growth pattern in invasive lung adenocarcinoma with radiomics. Transl Lung Cancer Res, 2021, 10(2): 955-964.
54. 余婷, 王鹏, 钱菁, 等. 数字图像分析在病理诊断中的应用综述. 解放军医学院学报, 2020, 41(5): 539-542.
55. Yu KH, Zhang C, Berry GJ, et al. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nat Commun, 2016, 7: 12474.
56. Teramoto A, Tsukamoto T, Kiriyama Y, et al. Automated classification of lung cancer types from cytological images using deep convolutional neural networks. Biomed Res Int, 2017, 2017: 4067832.
57. Yu KH, Wang F, Berry GJ, et al. Classifying non-small cell lung cancer types and transcriptomic subtypes using convolutional neural networks. J Am Med Inform Assoc, 2020, 27(5): 757-769.
58. Wei JW, Tafe LJ, Linnik YA, et al. Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks. Sci Rep, 2019, 9(1): 3358.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Research progress on the characteristics and rapid diagnostic tools of early lung adenocarcinoma subtypes

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