Progress in early identification of high-grade lung adenocarcinoma_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

ZHANG Shurui ¹ , WEN Bing ² , LI Yanjun ² , LI Mingqian ² , CAO An ³ ,  YANG Chaokun ²

1. North Sichuan Medical College, Nanchong, 637100, Sichuan, P. R. China;
2. Yibin Second People's Hospital, Yibin, 644002, Sichuan, P. R. China;
3. Daye People's Hospital, Daye, 435100, Sichuan, P. R. China;

Corresponding author：

YANG Chaokun, Email: yangck31@163.com

Keywords：

Lung adenocarcinoma; high-grade histologic subtype; preoperative prediction; radiomics; molecular subtyping; review

DOI：

10.7507/1007-4848.202505030

Video：

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Abstract Full text Figures/Tables Video References Cited by

[Abstract] High-grade histologic subtypes of lung adenocarcinoma, such as micropapillary and solid patterns, are characterized by high invasiveness, increased risk of recurrence, and poor prognosis. Early preoperative identification of these subtypes is crucial for achieving individualized treatment and improving clinical outcomes. This review summarizes the clinical features, imaging manifestations, molecular mechanisms, and diagnostic advances related to these aggressive patterns. Studies have shown that micropapillary and solid subtypes are more common in male smokers, often present as solid nodules, and demonstrate strong predictive value in FDG-PET metabolic parameters and CT-based radiomics models. At the molecular level, EGFR mutations are more frequently observed in micropapillary types, whereas solid subtypes are often associated with high PD-L1 expression and TP53 mutations, indicating distinct therapeutic strategies for targeted and immunotherapies. In addition, serum markers such as CEA and CYFRA21-1, along with inflammatory indices like NLR and SII, may serve as auxiliary tools for subtype identification. Histologic subtypes of lung adenocarcinoma are evolving from descriptive classifications into critical determinants of treatment decisions and precision management. Clinicians should incorporate comprehensive histologic evaluation into individualized therapeutic planning. Multimodal integration technologies, combined with artificial intelligence algorithms, are advancing the accurate preoperative prediction and management of high-risk subtypes, thereby facilitating early diagnosis and stratified treatment of lung adenocarcinoma.

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3.	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.
4.	Boland JM, Froemming AT, Wampfler JA, et al. Adenocarcinoma in situ, minimally invasive adenocarcinoma, and invasive pulmonary adenocarcinoma: analysis of interobserver agreement, survival, radiographic characteristics, and gross pathology in 296 nodules. Hum Pathol, 2016, 51: 41-50.
5.	Shang J, Jiang H, Zhao Y, et al. Molecular subtyping of stageⅠ lung adenocarcinoma via molecular alterations in pre-invasive lesion progression. J Transl Med, 2025, 23(1): 263.
6.	Fang W, Zhang G, Yu Y, et al. Identification of pathological subtypes of early lung adenocarcinoma based on artificial intelligence parameters and CT signs. Biosci Rep, 2022, 42(1): BSR20212416.
7.	Gou Q, Gou Q, Gan X, et al. Novel therapeutic strategies for rare mutations in non-small cell lung cancer. Sci Rep, 2024, 14(1): 10317.
8.	Xin S, Wen M, Tian Y, et al. Impact of histopathological subtypes on invasive lung adenocarcinoma: from epidemiology to tumour microenvironment to therapeutic strategies. World J Surg Oncol, 2025, 23(1): 66.
9.	Nicholson AG, Tsao MS, Beasley MB, et al. The 2021 WHO classification of lung tumors: impact of advances since 2015. J Thorac Oncol, 2022, 17(3): 362-387.
10.	E H, Wu J, Ren Y, et al. The IASLC grading system for invasive pulmonary adenocarcinoma: a potential prognosticator for patients receiving neoadjuvant therapy. Ther Adv Med Oncol, 2023, 15: 17588359221148028.
11.	Deng C, Zheng Q, Zhang Y, et al. Validation of the novel International Association for the Study of Lung Cancer grading system for invasive pulmonary adenocarcinoma and association with common driver mutations. J Thorac Oncol, 2021, 16(10): 1684-1693.
12.	Moreira AL, Ocampo PSS, Xia Y, et al. A grading system for invasive pulmonary adenocarcinoma: a proposal from the International Association for the Study of Lung Cancer Pathology Committee. J Thorac Oncol, 2020, 15(10): 1599-1610.
13.	Feng J, Shao X, Gao J, et al. Application and progress of non-invasive imaging in predicting lung invasive non-mucinous adenocarcinoma under the new IASLC grading guidelines. Insights Imaging, 2025, 16(1): 4.
14.	Huang M, Wang Y, Yang X, et al. Establishing a threshold for maximum standardized uptake value on 18F-fluorodeoxyglucose PET/CT to predict high-grade lung adenocarcinoma and its prognostic significance. Nuclear Medicine Communications, 2025: 10.1097.
15.	Liang M, Tang W, Tan F, et al. Preoperative prognostic prediction for stageⅠ lung adenocarcinomas: Impact of the computed tomography features associated with the new histological grading system. Front Oncol, 2023, 13: 1103269.
16.	Wang Z, Zhang N, Liu J, et al. Predicting micropapillary or solid pattern of lung adenocarcinoma with CT-based radiomics, conventional radiographic and clinical features. Respir Res, 2023, 24(1): 282.
17.	Zheng S, Liu J, Xie J, et al. Differentiating high-grade patterns and predominant subtypes for IASLC grading in invasive pulmonary adenocarcinoma using radiomics and clinical-semantic features. Cancer Imaging, 2025, 25(1): 42.
18.	Travis WD, Brambilla E, Noguchi M, et al. International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society international multidisciplinary classification of lung adenocarcinoma. J Thorac Oncol, 2011, 6(2): 244-285.
19.	Mikubo M, Tamagawa S, Kondo Y, et al. Micropapillary and solid components as high-grade patterns in IASLC grading system of lung adenocarcinoma: clinical implications and management. Lung Cancer, 2024 Jan: 187: 107445.
20.	Moreira AL, Ocampo PSS, Xia Y, et al. A grading system for invasive pulmonary adenocarcinoma: a proposal from the International Association for the Study of Lung Cancer Pathology Committee. J Thorac Oncol, 2020, 15(10): 1599-1610.
21.	Fujikawa R, Muraoka Y, Kashima J, et al. Clinicopathologic and genotypic features of lung adenocarcinoma characterized by the International Association for the Study of Lung Cancer Grading System. J Thorac Oncol, 2022, 17(5): 700-707.
22.	Kameda K, Eguchi T, Lu S, et al. Implications of the eighth edition of the TNM proposal: invasive versus total tumor size for the T descriptor in pathologic stage Ⅰ-ⅡA lung adenocarcinoma. J Thorac Oncol, 2018, 13(12): 1919-1929.
23.	Succony L, Rassl DM, Barker AP, et al. Adenocarcinoma spectrum lesions of the lung: detection, pathology and treatment strategies. Cancer Treat Rev, 2021, 99: 102237.
24.	Divisi D, Barone M, Zaccagna G, et al. Fluorine-18 fluorodeoxyglucose positron emission tomography in the management of solitary pulmonary nodule: a review. Ann Med, 2017, 49(7): 626-635.
25.	Park S, Lee SM, Kim S, et al. Volume doubling times of lung adenocarcinomas: correlation with predominant histologic subtypes and prognosis. Radiology, 2020, 295(3): 703-712.
26.	Park S, Lee SM, Noh HN, et al. Differentiation of predominant subtypes of lung adenocarcinoma using a quantitative radiomics approach on CT. Eur Radiol, 2020, 30(9): 4883-4892.
27.	Ninomiya K, Yanagawa M, Tsubamoto M, et al. Prediction of solid and micropapillary components in lung invasive adenocarcinoma: radiomics analysis from high-spatial-resolution CT data with 1024 matrix. Jpn J Radiol, 2024, 42(6): 590-598.
28.	Xia X, Gong J, Hao W, et al. Comparison and fusion of deep learning and radiomics features of ground-glass nodules to predict the invasiveness risk of stage-Ⅰ lung adenocarcinomas in CT scan. Front Oncol, 2020 Mar 31: 10: 418.
29.	Ding H, Xia W, Zhang L, et al. CT-based deep learning model for invasiveness classification and micropapillary pattern prediction within lung adenocarcinoma. Front Oncol, 2020 Jul 22: 10: 1186.
30.	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.
31.	Wang F, Wang CL, Yi YQ, et al. Comparison and fusion prediction model for lung adenocarcinoma with micropapillary and solid pattern using clinicoradiographic, radiomics and deep learning features. Sci Rep, 2023, 13(1): 9302.
32.	Huo J, Min X, Luo T, et al. Computed tomography-based 3D convolutional neural network deep learning model for predicting micropapillary or solid growth pattern of invasive lung adenocarcinoma. Radiol Med, 2024, 129(5): 776-784.
33.	Choi Y, Aum J, Lee SH, et al. Deep learning analysis of ct images reveals high-grade pathological features to predict survival in lung adenocarcinoma. Cancers (Basel), 2021, 13(16): 4077.
34.	Yang SM, Chen LW, Wang HJ, et al. Extraction of radiomic values from lung adenocarcinoma with near-pure subtypes in the International Association for the Study of Lung Cancer/the American Thoracic Society/the European Respiratory Society (IASLC/ATS/ERS) classification. Lung Cancer, 2018 May: 119: 56-63.119-156.
35.	Chen LW, Yang SM, Chuang CC, et al. Solid attenuation components attention deep learning model to predict micropapillary and solid patterns in lung adenocarcinomas on computed tomography. Ann Surg Oncol, 2022, 29(12): 7473-7482.
36.	Li M, Ruan Y, Feng Z, et al. Preoperative CT-based radiomics combined with nodule type to predict the micropapillary pattern in lung adenocarcinoma of size 2 cm or less: a multicenter study. Front Oncol, 2021, 11: 788424.
37.	Zhou L, Sun J, Long H, et al. Imaging phenotyping using 18F-FDG PET/CT radiomics to predict micropapillary and solid pattern in lung adenocarcinoma. Insights Imaging, 2024, 15(1): 5.
38.	Chang C, Sun X, Zhao W, et al. Minor components of micropapillary and solid subtypes in lung invasive adenocarcinoma (≤3 cm): PET/CT findings and correlations with lymph node metastasis. Radiol Med, 2020, 125(3): 257-264.
39.	Lococo F, Guerrera F, Rena O, et al. Accuracy of 18F-FDG in detecting stageⅠ lung adenocarcinomas according to IASLC/ATS/ERS classification. Heart Lung Circ, 2022, 31(5): 726-732.
40.	Choi W, Liu CJ, Alam SR, et al. Preoperative 18F-FDG PET/CT and CT radiomics for identifying aggressive histopathological subtypes in early stage lung adenocarcinoma. Comput Struct Biotechnol J, 2023 Nov 4: 21: 5601-5608.21-5601.
41.	Lee S, Lee CY, Kim NY, et al. Feasibility of UTE-MRI-based radiomics model for prediction of histopathologic subtype of lung adenocarcinoma: in comparison with CT-based radiomics model. Eur Radiol, 2024, 34(5): 3422-3430.
42.	Ahn B, Yoon S, Kim D, et al. Clinicopathologic and genomic features of high-grade pattern and their subclasses in lung adenocarcinoma. Lung Cancer, 2022, 170: 176-184.
43.	Li X, Chen Y, Lan R, et al. Transmembrane mucins in lung adenocarcinoma: understanding of current molecular mechanisms and clinical applications. Cell Death Discov, 2025, 11(1): 163.
44.	Ito Y, Usui G, Seki M, et al. Association of frequent hypermethylation with high grade histological subtype in lung adenocarcinoma. Cancer Sci, 2023, 114(7): 3003-3013.
45.	Jain D, Satapathy S, Bubendorf L. Diagnostic and predictive immunocytochemistry in lung cancer. Acta Cytologica, 2025, 69(1): 69-76.
46.	Chen C, Chen ZJ, Li WJ, et al. Evaluation of the preoperative neutrophil-to-lymphocyte ratio as a predictor of the micropapillary component of stage IA lung adenocarcinoma. J Int Med Res, 2024, 52(4): 3000605241245016.
47.	Huang T, Peng Q, Zhang Y, et al. The systemic immune-inflammation index (SII) and coronary artery lesions in Kawasaki disease. Clin Exp Med, 2024, 24(1): 4.
48.	Wei B, Zhang Y, Shi K, et al. Predictive value of systemic immune-inflammation index in the high-grade subtypes components of small-sized lung adenocarcinoma. J Cardiothorac Surg, 2024, 19(1): 39.
49.	李志华, 潘相龙, 吴卫兵. 肺腺癌患者术前血清肿瘤标志物与微乳头、实体成分的关联研究. 南京医科大学学报 (自然科学版), 2022, 42(6): 843-860.Li ZH, Pan XL, Wu WB. Correlation between preoperative serum tumor markers and micropapillary and solid components in patients with lung adenocarcinoma. J Nanjing Med Univ (Nat Sci Ed), 2022, 42(6): 843-860.
50.	Liu Y, Chang Y, Zha X, et al. A combination of radiomic features, imaging characteristics, and serum tumor biomarkers to predict the possibility of the high-grade subtypes of lung adenocarcinoma. Acad Radiol, 2022, 29(12): 1792-1801.
51.	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.
52.	Almeida GL, Pinto BM, Pinto VM, et al. Tumor spread through air spaces in lung cancer: prospective analysis of the accuracy of intraoperative frozen section examination. J Bras Pneumol, 2024, 50(4): e20240165.

1. Myers DJ, Wallen JM. Lung Adenocarcinoma. In: StatPearls. StatPearls Publishing, Treasure Island (FL), 2025.
2. Li C, Lei S, Ding L, et al. Global burden and trends of lung cancer incidence and mortality. Chin Med J (Engl), 2023, 136(13): 1583-1590.
3. 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.
4. Boland JM, Froemming AT, Wampfler JA, et al. Adenocarcinoma in situ, minimally invasive adenocarcinoma, and invasive pulmonary adenocarcinoma: analysis of interobserver agreement, survival, radiographic characteristics, and gross pathology in 296 nodules. Hum Pathol, 2016, 51: 41-50.
5. Shang J, Jiang H, Zhao Y, et al. Molecular subtyping of stageⅠ lung adenocarcinoma via molecular alterations in pre-invasive lesion progression. J Transl Med, 2025, 23(1): 263.
6. Fang W, Zhang G, Yu Y, et al. Identification of pathological subtypes of early lung adenocarcinoma based on artificial intelligence parameters and CT signs. Biosci Rep, 2022, 42(1): BSR20212416.
7. Gou Q, Gou Q, Gan X, et al. Novel therapeutic strategies for rare mutations in non-small cell lung cancer. Sci Rep, 2024, 14(1): 10317.
8. Xin S, Wen M, Tian Y, et al. Impact of histopathological subtypes on invasive lung adenocarcinoma: from epidemiology to tumour microenvironment to therapeutic strategies. World J Surg Oncol, 2025, 23(1): 66.
9. Nicholson AG, Tsao MS, Beasley MB, et al. The 2021 WHO classification of lung tumors: impact of advances since 2015. J Thorac Oncol, 2022, 17(3): 362-387.
10. E H, Wu J, Ren Y, et al. The IASLC grading system for invasive pulmonary adenocarcinoma: a potential prognosticator for patients receiving neoadjuvant therapy. Ther Adv Med Oncol, 2023, 15: 17588359221148028.
11. Deng C, Zheng Q, Zhang Y, et al. Validation of the novel International Association for the Study of Lung Cancer grading system for invasive pulmonary adenocarcinoma and association with common driver mutations. J Thorac Oncol, 2021, 16(10): 1684-1693.
12. Moreira AL, Ocampo PSS, Xia Y, et al. A grading system for invasive pulmonary adenocarcinoma: a proposal from the International Association for the Study of Lung Cancer Pathology Committee. J Thorac Oncol, 2020, 15(10): 1599-1610.
13. Feng J, Shao X, Gao J, et al. Application and progress of non-invasive imaging in predicting lung invasive non-mucinous adenocarcinoma under the new IASLC grading guidelines. Insights Imaging, 2025, 16(1): 4.
14. Huang M, Wang Y, Yang X, et al. Establishing a threshold for maximum standardized uptake value on 18F-fluorodeoxyglucose PET/CT to predict high-grade lung adenocarcinoma and its prognostic significance. Nuclear Medicine Communications, 2025: 10.1097.
15. Liang M, Tang W, Tan F, et al. Preoperative prognostic prediction for stageⅠ lung adenocarcinomas: Impact of the computed tomography features associated with the new histological grading system. Front Oncol, 2023, 13: 1103269.
16. Wang Z, Zhang N, Liu J, et al. Predicting micropapillary or solid pattern of lung adenocarcinoma with CT-based radiomics, conventional radiographic and clinical features. Respir Res, 2023, 24(1): 282.
17. Zheng S, Liu J, Xie J, et al. Differentiating high-grade patterns and predominant subtypes for IASLC grading in invasive pulmonary adenocarcinoma using radiomics and clinical-semantic features. Cancer Imaging, 2025, 25(1): 42.
18. Travis WD, Brambilla E, Noguchi M, et al. International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society international multidisciplinary classification of lung adenocarcinoma. J Thorac Oncol, 2011, 6(2): 244-285.
19. Mikubo M, Tamagawa S, Kondo Y, et al. Micropapillary and solid components as high-grade patterns in IASLC grading system of lung adenocarcinoma: clinical implications and management. Lung Cancer, 2024 Jan: 187: 107445.
20. Moreira AL, Ocampo PSS, Xia Y, et al. A grading system for invasive pulmonary adenocarcinoma: a proposal from the International Association for the Study of Lung Cancer Pathology Committee. J Thorac Oncol, 2020, 15(10): 1599-1610.
21. Fujikawa R, Muraoka Y, Kashima J, et al. Clinicopathologic and genotypic features of lung adenocarcinoma characterized by the International Association for the Study of Lung Cancer Grading System. J Thorac Oncol, 2022, 17(5): 700-707.
22. Kameda K, Eguchi T, Lu S, et al. Implications of the eighth edition of the TNM proposal: invasive versus total tumor size for the T descriptor in pathologic stage Ⅰ-ⅡA lung adenocarcinoma. J Thorac Oncol, 2018, 13(12): 1919-1929.
23. Succony L, Rassl DM, Barker AP, et al. Adenocarcinoma spectrum lesions of the lung: detection, pathology and treatment strategies. Cancer Treat Rev, 2021, 99: 102237.
24. Divisi D, Barone M, Zaccagna G, et al. Fluorine-18 fluorodeoxyglucose positron emission tomography in the management of solitary pulmonary nodule: a review. Ann Med, 2017, 49(7): 626-635.
25. Park S, Lee SM, Kim S, et al. Volume doubling times of lung adenocarcinomas: correlation with predominant histologic subtypes and prognosis. Radiology, 2020, 295(3): 703-712.
26. Park S, Lee SM, Noh HN, et al. Differentiation of predominant subtypes of lung adenocarcinoma using a quantitative radiomics approach on CT. Eur Radiol, 2020, 30(9): 4883-4892.
27. Ninomiya K, Yanagawa M, Tsubamoto M, et al. Prediction of solid and micropapillary components in lung invasive adenocarcinoma: radiomics analysis from high-spatial-resolution CT data with 1024 matrix. Jpn J Radiol, 2024, 42(6): 590-598.
28. Xia X, Gong J, Hao W, et al. Comparison and fusion of deep learning and radiomics features of ground-glass nodules to predict the invasiveness risk of stage-Ⅰ lung adenocarcinomas in CT scan. Front Oncol, 2020 Mar 31: 10: 418.
29. Ding H, Xia W, Zhang L, et al. CT-based deep learning model for invasiveness classification and micropapillary pattern prediction within lung adenocarcinoma. Front Oncol, 2020 Jul 22: 10: 1186.
30. 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.
31. Wang F, Wang CL, Yi YQ, et al. Comparison and fusion prediction model for lung adenocarcinoma with micropapillary and solid pattern using clinicoradiographic, radiomics and deep learning features. Sci Rep, 2023, 13(1): 9302.
32. Huo J, Min X, Luo T, et al. Computed tomography-based 3D convolutional neural network deep learning model for predicting micropapillary or solid growth pattern of invasive lung adenocarcinoma. Radiol Med, 2024, 129(5): 776-784.
33. Choi Y, Aum J, Lee SH, et al. Deep learning analysis of ct images reveals high-grade pathological features to predict survival in lung adenocarcinoma. Cancers (Basel), 2021, 13(16): 4077.
34. Yang SM, Chen LW, Wang HJ, et al. Extraction of radiomic values from lung adenocarcinoma with near-pure subtypes in the International Association for the Study of Lung Cancer/the American Thoracic Society/the European Respiratory Society (IASLC/ATS/ERS) classification. Lung Cancer, 2018 May: 119: 56-63.119-156.
35. Chen LW, Yang SM, Chuang CC, et al. Solid attenuation components attention deep learning model to predict micropapillary and solid patterns in lung adenocarcinomas on computed tomography. Ann Surg Oncol, 2022, 29(12): 7473-7482.
36. Li M, Ruan Y, Feng Z, et al. Preoperative CT-based radiomics combined with nodule type to predict the micropapillary pattern in lung adenocarcinoma of size 2 cm or less: a multicenter study. Front Oncol, 2021, 11: 788424.
37. Zhou L, Sun J, Long H, et al. Imaging phenotyping using 18F-FDG PET/CT radiomics to predict micropapillary and solid pattern in lung adenocarcinoma. Insights Imaging, 2024, 15(1): 5.
38. Chang C, Sun X, Zhao W, et al. Minor components of micropapillary and solid subtypes in lung invasive adenocarcinoma (≤3 cm): PET/CT findings and correlations with lymph node metastasis. Radiol Med, 2020, 125(3): 257-264.
39. Lococo F, Guerrera F, Rena O, et al. Accuracy of 18F-FDG in detecting stageⅠ lung adenocarcinomas according to IASLC/ATS/ERS classification. Heart Lung Circ, 2022, 31(5): 726-732.
40. Choi W, Liu CJ, Alam SR, et al. Preoperative 18F-FDG PET/CT and CT radiomics for identifying aggressive histopathological subtypes in early stage lung adenocarcinoma. Comput Struct Biotechnol J, 2023 Nov 4: 21: 5601-5608.21-5601.
41. Lee S, Lee CY, Kim NY, et al. Feasibility of UTE-MRI-based radiomics model for prediction of histopathologic subtype of lung adenocarcinoma: in comparison with CT-based radiomics model. Eur Radiol, 2024, 34(5): 3422-3430.
42. Ahn B, Yoon S, Kim D, et al. Clinicopathologic and genomic features of high-grade pattern and their subclasses in lung adenocarcinoma. Lung Cancer, 2022, 170: 176-184.
43. Li X, Chen Y, Lan R, et al. Transmembrane mucins in lung adenocarcinoma: understanding of current molecular mechanisms and clinical applications. Cell Death Discov, 2025, 11(1): 163.
44. Ito Y, Usui G, Seki M, et al. Association of frequent hypermethylation with high grade histological subtype in lung adenocarcinoma. Cancer Sci, 2023, 114(7): 3003-3013.
45. Jain D, Satapathy S, Bubendorf L. Diagnostic and predictive immunocytochemistry in lung cancer. Acta Cytologica, 2025, 69(1): 69-76.
46. Chen C, Chen ZJ, Li WJ, et al. Evaluation of the preoperative neutrophil-to-lymphocyte ratio as a predictor of the micropapillary component of stage IA lung adenocarcinoma. J Int Med Res, 2024, 52(4): 3000605241245016.
47. Huang T, Peng Q, Zhang Y, et al. The systemic immune-inflammation index (SII) and coronary artery lesions in Kawasaki disease. Clin Exp Med, 2024, 24(1): 4.
48. Wei B, Zhang Y, Shi K, et al. Predictive value of systemic immune-inflammation index in the high-grade subtypes components of small-sized lung adenocarcinoma. J Cardiothorac Surg, 2024, 19(1): 39.
49. 李志华, 潘相龙, 吴卫兵. 肺腺癌患者术前血清肿瘤标志物与微乳头、实体成分的关联研究. 南京医科大学学报 (自然科学版), 2022, 42(6): 843-860.Li ZH, Pan XL, Wu WB. Correlation between preoperative serum tumor markers and micropapillary and solid components in patients with lung adenocarcinoma. J Nanjing Med Univ (Nat Sci Ed), 2022, 42(6): 843-860.
50. Liu Y, Chang Y, Zha X, et al. A combination of radiomic features, imaging characteristics, and serum tumor biomarkers to predict the possibility of the high-grade subtypes of lung adenocarcinoma. Acad Radiol, 2022, 29(12): 1792-1801.
51. 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.
52. Almeida GL, Pinto BM, Pinto VM, et al. Tumor spread through air spaces in lung cancer: prospective analysis of the accuracy of intraoperative frozen section examination. J Bras Pneumol, 2024, 50(4): e20240165.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Latest ArticlesProgress in early identification of high-grade lung adenocarcinoma

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