Research progress of artificial intelligence in pathological subtypes classification and gene expression analysis of lung adenocarcinoma_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

ZHOU Liantian ^1,2 ,  ZHAO Kehui ¹ , ZHANG Zhiqiang ¹

1. College of Intelligence and Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan, 250300, P. R. China;
2. Heze Traditional Chinese Medicine Hospital, Heze, 274000, Shandong, P. R. China;

Corresponding author：

ZHAO Kehui, Email: zhaokh1202@sina.com

Keywords：

Lung adenocarcinoma; pathological subtypes; gene expression; artificial intelligence; machine learning; deep learning; review

DOI：

10.7507/1007-4848.202304001

Video：

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Lung adenocarcinoma is a prevalent histological subtype of non-small cell lung cancer with different morphologic and molecular features that are critical for prognosis and treatment planning. In recent years, with the development of artificial intelligence technology, its application in the study of pathological subtypes and gene expression of lung adenocarcinoma has gained widespread attention. This paper reviews the research progress of machine learning and deep learning in pathological subtypes classification and gene expression analysis of lung adenocarcinoma, and some problems and challenges at the present stage are summarized and the future directions of artificial intelligence in lung adenocarcinoma research are foreseen.

Citation： ZHOU Liantian, ZHAO Kehui, ZHANG Zhiqiang. Research progress of artificial intelligence in pathological subtypes classification and gene expression analysis of lung adenocarcinoma. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2024, 31(1): 145-152. doi: 10.7507/1007-4848.202304001 Copy

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3.	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.
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8.	Chalela R, Curull V, Enríquez C, et al. Lung adenocarcinoma: From molecular basis to genome-guided therapy and immunotherapy. J Thorac Dis, 2017, 9(7): 2142-2158.
9.	Lin JJ, Cardarella S, Lydon CA, et al. Five-year survival in EGFR-mutant metastatic lung adenocarcinoma treated with EGFR-TKIs. J Thorac Oncol, 2016, 11(4): 556-565.
10.	Okayama H, Kohno T, Ishii Y, et al. Identification of genes upregulated in ALK-positive and EGFR/KRAS/ALK-negative lung adenocarcinomas. Cancer Res, 2012, 72(1): 100-111.
11.	Sholl LM, Sun H, Butaney M, et al. ROS1 immunohistochemistry for detection of ROS1-rearranged lung adenocarcinomas. Am J Surg Pathol, 2013, 37(9): 1441-1449.
12.	Villaruz LC, Socinski MA, Abberbock S, et al. Clinicopathologic features and outcomes of patients with lung adenocarcinomas harboring BRAF mutations in the Lung Cancer Mutation Consortium. Cancer, 2015, 121(3): 448-456.
13.	Skoulidis F, Byers LA, Diao L, et al. Co-occurring genomic alterations define major subsets of KRAS-mutant lung adenocarcinoma with distinct biology, immune profiles, and therapeutic vulnerabilities. Cancer Discov, 2015, 5(8): 860-877.
14.	Olmedo ME, Cervera R, Cabezon-Gutierrez L, et al. New horizons for uncommon mutations in non-small cell lung cancer: BRAF, KRAS, RET, MET, NTRK, HER2. World J Clin Oncol, 2022, 13(4): 276-286.
15.	Jiang Y, Yang M, Wang S, et al. Emerging role of deep learning-based artificial intelligence in tumor pathology. Cancer Commun (Lond), 2020, 40(4): 154-166.
16.	Liu Y, Wang H, Gu Y, et al. Image classification toward lung cancer recognition by learning deep quality model. J Vis Commun Image Represent, 2019, 63: 102570.
17.	Kirienko M, Sollini M, Corbetta M, et al. Radiomics and gene expression profile to characterise the disease and predict outcome in patients with lung cancer. Eur J Nucl Med Mol Imaging, 2021, 48(11): 3643-3655.
18.	Sun GZ, Zhao TW. Lung adenocarcinoma pathology stages related gene identification. Math Biosci Eng, 2019, 17(1): 737-746.
19.	Jia TY, Xiong JF, Li XY, et al. Identifying EGFR mutations in lung adenocarcinoma by noninvasive imaging using radiomics features and random forest modeling. Eur Radiol, 2019, 29(9): 4742-4750.
20.	Pinheiro G, Pereira T, Dias C, et al. Identifying relationships between imaging phenotypes and lung cancer-related mutation status: EGFR and KRAS. Sci Rep, 2020, 10(1): 3625.
21.	Zhang T, Xu Z, Liu G, et al. Simultaneous identification of EGFR, KRAS, ERBB2, and TP53 mutations in patients with non-small cell lung cancer by machine learning-derived three-dimensional radiomics. Cancers (Basel), 2021, 13(8): 1814.
22.	Le NQK, Kha QH, Nguyen VH, et al. Machine learning-based radiomics signatures for EGFR and KRAS mutations prediction in non-small-cell lung cancer. Int J Mol Sci, 2021, 22(17): 9254.
23.	Yuan F, Lu L, Zou Q. Analysis of gene expression profiles of lung cancer subtypes with machine learning algorithms. Biochim Biophys Acta Mol Basis Dis, 2020, 1866(8): 165822.
24.	Gu Q, Feng Z, Liang Q, et al. Machine learning-based radiomics strategy for prediction of cell proliferation in non-small cell lung cancer. Eur J Radiol, 2019, 118: 32-37.
25.	McWilliams A, Tammemagi MC, Mayo JR, et al. Probability of cancer in pulmonary nodules detected on first screening CT. N Engl J Med, 2013, 369(10): 910-919.
26.	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.
27.	Winkler Wille MM, van Riel SJ, Saghir Z, et al. Predictive accuracy of the pancan lung cancer risk prediction model-external validation based on CT from the danish lung cancer screening trial. Eur Radiol, 2015, 25(10): 3093-3099.
28.	Kriegsmann M, Casadonte R, Kriegsmann J, et al. Reliable entity subtyping in non-small cell lung cancer by matrix-assisted laser desorption/ionization imaging mass spectrometry on formalin-fixed paraffin-embedded tissue specimens. Mol Cell Proteomics, 2016, 15(10): 3081-3089.
29.	Hong D, Xu K, Zhang L, et al. Radiomics signature as a predictive factor for EGFR mutations in advanced lung adenocarcinoma. Front Oncol, 2020, 10: 28.
30.	Huang S, Cai N, Pacheco PP, et al. Applications of support vector machine (SVM) learning in cancer genomics. Cancer Genomics Proteomics, 2018, 15(1): 41-51.
31.	He R, Yang X, Li T, et al. A machine learning-based predictive model of epidermal growth factor mutations in lung adenocarcinomas. Cancers (Basel), 2022, 14(19): 4664.
32.	Coudray N, Ocampo PS, Sakellaropoulos T, et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat Med, 2018, 24(10): 1559-1567.
33.	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.
34.	Mobadersany P, Yousefi S, Amgad M, et al. Predicting cancer outcomes from histology and genomics using convolutional networks. Proc Natl Acad Sci U S A, 2018, 115(13): E2970-E2979.
35.	Gertych A, Swiderska-Chadaj Z, Ma Z, et al. Convolutional neural networks can accurately distinguish four histologic growth patterns of lung adenocarcinoma in digital slides. Sci Rep, 2019, 9(1): 1483.
36.	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.
37.	Wang S, Rong R, Yang DM, et al. Computational staining of pathology images to study the tumor microenvironment in lung cancer. Cancer Res, 2020, 80(10): 2056-2066.
38.	Chen CL, Chen CC, Yu WH, et al. An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning. Nat Commun, 2021, 12(1): 1193.
39.	Khosravi P, Kazemi E, Imielinski M, et al. Deep convolutional neural networks enable discrimination of heterogeneous digital pathology images. EBioMedicine, 2018, 27: 317-328.
40.	Li Y, Wu X, Yang P, et al. Machine learning for lung cancer diagnosis, treatment, and prognosis. Genomics Proteomics Bioinformatics, 2022, 20(5): 850-866.

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. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA Cancer J Clin, 2020, 70(1): 7-30.
3. 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.
4. Myers DJ, Wallen JM. Lung adenocarcinoma. Treasure Island (FL): StatPearls Publishing, 2023. https://www.ncbi.nlm.nih.gov/books/NBK519578/. Accessed on 2023-03-25.
5. 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.
6. Calvayrac O, Pradines A, Pons E, et al. Molecular biomarkers for lung adenocarcinoma. Eur Respir J, 2017, 49(4): 1601734.
7. Cancer Genome Atlas Research Network. Comprehensive molecular profiling of lung adenocarcinoma. Nature, 2014, 511(7511): 543-550.
8. Chalela R, Curull V, Enríquez C, et al. Lung adenocarcinoma: From molecular basis to genome-guided therapy and immunotherapy. J Thorac Dis, 2017, 9(7): 2142-2158.
9. Lin JJ, Cardarella S, Lydon CA, et al. Five-year survival in EGFR-mutant metastatic lung adenocarcinoma treated with EGFR-TKIs. J Thorac Oncol, 2016, 11(4): 556-565.
10. Okayama H, Kohno T, Ishii Y, et al. Identification of genes upregulated in ALK-positive and EGFR/KRAS/ALK-negative lung adenocarcinomas. Cancer Res, 2012, 72(1): 100-111.
11. Sholl LM, Sun H, Butaney M, et al. ROS1 immunohistochemistry for detection of ROS1-rearranged lung adenocarcinomas. Am J Surg Pathol, 2013, 37(9): 1441-1449.
12. Villaruz LC, Socinski MA, Abberbock S, et al. Clinicopathologic features and outcomes of patients with lung adenocarcinomas harboring BRAF mutations in the Lung Cancer Mutation Consortium. Cancer, 2015, 121(3): 448-456.
13. Skoulidis F, Byers LA, Diao L, et al. Co-occurring genomic alterations define major subsets of KRAS-mutant lung adenocarcinoma with distinct biology, immune profiles, and therapeutic vulnerabilities. Cancer Discov, 2015, 5(8): 860-877.
14. Olmedo ME, Cervera R, Cabezon-Gutierrez L, et al. New horizons for uncommon mutations in non-small cell lung cancer: BRAF, KRAS, RET, MET, NTRK, HER2. World J Clin Oncol, 2022, 13(4): 276-286.
15. Jiang Y, Yang M, Wang S, et al. Emerging role of deep learning-based artificial intelligence in tumor pathology. Cancer Commun (Lond), 2020, 40(4): 154-166.
16. Liu Y, Wang H, Gu Y, et al. Image classification toward lung cancer recognition by learning deep quality model. J Vis Commun Image Represent, 2019, 63: 102570.
17. Kirienko M, Sollini M, Corbetta M, et al. Radiomics and gene expression profile to characterise the disease and predict outcome in patients with lung cancer. Eur J Nucl Med Mol Imaging, 2021, 48(11): 3643-3655.
18. Sun GZ, Zhao TW. Lung adenocarcinoma pathology stages related gene identification. Math Biosci Eng, 2019, 17(1): 737-746.
19. Jia TY, Xiong JF, Li XY, et al. Identifying EGFR mutations in lung adenocarcinoma by noninvasive imaging using radiomics features and random forest modeling. Eur Radiol, 2019, 29(9): 4742-4750.
20. Pinheiro G, Pereira T, Dias C, et al. Identifying relationships between imaging phenotypes and lung cancer-related mutation status: EGFR and KRAS. Sci Rep, 2020, 10(1): 3625.
21. Zhang T, Xu Z, Liu G, et al. Simultaneous identification of EGFR, KRAS, ERBB2, and TP53 mutations in patients with non-small cell lung cancer by machine learning-derived three-dimensional radiomics. Cancers (Basel), 2021, 13(8): 1814.
22. Le NQK, Kha QH, Nguyen VH, et al. Machine learning-based radiomics signatures for EGFR and KRAS mutations prediction in non-small-cell lung cancer. Int J Mol Sci, 2021, 22(17): 9254.
23. Yuan F, Lu L, Zou Q. Analysis of gene expression profiles of lung cancer subtypes with machine learning algorithms. Biochim Biophys Acta Mol Basis Dis, 2020, 1866(8): 165822.
24. Gu Q, Feng Z, Liang Q, et al. Machine learning-based radiomics strategy for prediction of cell proliferation in non-small cell lung cancer. Eur J Radiol, 2019, 118: 32-37.
25. McWilliams A, Tammemagi MC, Mayo JR, et al. Probability of cancer in pulmonary nodules detected on first screening CT. N Engl J Med, 2013, 369(10): 910-919.
26. 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.
27. Winkler Wille MM, van Riel SJ, Saghir Z, et al. Predictive accuracy of the pancan lung cancer risk prediction model-external validation based on CT from the danish lung cancer screening trial. Eur Radiol, 2015, 25(10): 3093-3099.
28. Kriegsmann M, Casadonte R, Kriegsmann J, et al. Reliable entity subtyping in non-small cell lung cancer by matrix-assisted laser desorption/ionization imaging mass spectrometry on formalin-fixed paraffin-embedded tissue specimens. Mol Cell Proteomics, 2016, 15(10): 3081-3089.
29. Hong D, Xu K, Zhang L, et al. Radiomics signature as a predictive factor for EGFR mutations in advanced lung adenocarcinoma. Front Oncol, 2020, 10: 28.
30. Huang S, Cai N, Pacheco PP, et al. Applications of support vector machine (SVM) learning in cancer genomics. Cancer Genomics Proteomics, 2018, 15(1): 41-51.
31. He R, Yang X, Li T, et al. A machine learning-based predictive model of epidermal growth factor mutations in lung adenocarcinomas. Cancers (Basel), 2022, 14(19): 4664.
32. Coudray N, Ocampo PS, Sakellaropoulos T, et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat Med, 2018, 24(10): 1559-1567.
33. 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.
34. Mobadersany P, Yousefi S, Amgad M, et al. Predicting cancer outcomes from histology and genomics using convolutional networks. Proc Natl Acad Sci U S A, 2018, 115(13): E2970-E2979.
35. Gertych A, Swiderska-Chadaj Z, Ma Z, et al. Convolutional neural networks can accurately distinguish four histologic growth patterns of lung adenocarcinoma in digital slides. Sci Rep, 2019, 9(1): 1483.
36. 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.
37. Wang S, Rong R, Yang DM, et al. Computational staining of pathology images to study the tumor microenvironment in lung cancer. Cancer Res, 2020, 80(10): 2056-2066.
38. Chen CL, Chen CC, Yu WH, et al. An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning. Nat Commun, 2021, 12(1): 1193.
39. Khosravi P, Kazemi E, Imielinski M, et al. Deep convolutional neural networks enable discrimination of heterogeneous digital pathology images. EBioMedicine, 2018, 27: 317-328.
40. Li Y, Wu X, Yang P, et al. Machine learning for lung cancer diagnosis, treatment, and prognosis. Genomics Proteomics Bioinformatics, 2022, 20(5): 850-866.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Research progress of artificial intelligence in pathological subtypes classification and gene expression analysis of lung adenocarcinoma

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