Research progress on artificial intelligence in precise pathological diagnosis of lung cancer_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

FENG Shi ,  TENG Xiaodong

Department of Pathology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, P.R.China;

Corresponding author：

TENG Xiaodong, Email: teng1102069@zju.edu.cn

Keywords：

Lung cancer; artificial intelligence; pathology; image analysis; deep learning

DOI：

10.7507/1007-4848.202101013

Video：

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The incidence of lung cancer has increased significantly during the past decades. Pathology is the gold standard for diagnosis and the corresponding treatment measures selection of lung cancer. In recent years, with the development of artificial intelligence and digital pathology, the researches of pathological image analysis have achieved remarkable progresses in lung cancer. In this review, we will introduce the research progress on artificial intelligence in pathological classification, mutation genes and prognosis of lung cancer. Artificial intelligence is expected to further accelerate the pace of precision pathology.

Citation： FENG Shi, TENG Xiaodong. Research progress on artificial intelligence in precise pathological diagnosis of lung cancer. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2021, 28(5): 592-596. doi: 10.7507/1007-4848.202101013 Copy

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17.	Marx A, Chan JK, Coindre JM, et al. The 2015 World Health Organization classification of tumors of the thymus: Continuity and changes. J Thorac Oncol, 2015, 10(10): 1383-1395.
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19.	Tsao MS, Marguet S, Le Teuff G, et al. Subtype classification of lung adenocarcinoma predicts benefit from adjuvant chemotherapy in patients undergoing complete resection. J Clin Oncol, 2015, 33(30): 3439-3446.
20.	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.
21.	Larsen JE, Cascone T, Gerber DE, et al. Targeted therapies for lung cancer: Clinical experience and novel agents. Cancer J, 2011, 17(6): 512-527.
22.	Li Q, Wang X, Liang F, et al. A Bayesian hidden potts mixture model for analyzing lung cancer pathology images. Biostatistics, 2019, 20(4): 565-581.
23.	Choi H, Na KJ. A risk stratification model for lung cancer based on gene coexpression network and deep learning. Biomed Res Int, 2018, 2018: 2914280.
24.	Mezheyeuski A, Bergsland CH, Backman M, et al. Multispectral imaging for quantitative and compartment-specific immune infiltrates reveals distinct immune profiles that classify lung cancer patients. J Pathol, 2018, 244(4): 421-431.
25.	Trivella M, Pezzella F, Pastorino U, et al. Microvessel density as a prognostic factor in non-small-cell lung carcinoma: A meta-analysis of individual patient data. Lancet Oncol, 2007, 8(6): 488-499.
26.	Yi F, Yang L, Wang S, et al. Microvessel prediction in H& E stained pathology images using fully convolutional neural networks. BMC Bioinformatics, 2018, 19(1): 64.
27.	Wang S, Wang T, Yang L, et al. ConvPath: A software tool for lung adenocarcinoma digital pathological image analysis aided by a convolutional neural network. EBioMedicine, 2019, 50: 103-110.
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1. Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2018, 68(6): 394-424.
2. Lin HT, Liu FC, Wu CY, et al. Epidemiology and survival outcomes of lung cancer: A population-based study. Biomed Res Int, 2019, 2019: 8148156.
3. Holmes JH, Sacchi L, Bellazzi R, et al. Artificial intelligence in medicine AIME 2015. Artif Intell Med, 2017, 81: 1-2.
4. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521(7553): 436-444.
5. Lee JG, Jun S, Cho YW, et al. Deep learning in medical imaging: General overview. Korean J Radiol, 2017, 18(4): 570-584.
6. Kermany DS, Goldbaum M, Cai W, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell, 2018, 172(5): 1122-1131.
7. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature, 2017, 542(7639): 115-118.
8. Danaee P, Ghaeini R, Hendrix DA. A deep learning approach for cancer detection and relevant gene identification. Pac Symp Biocomput, 2017, 22: 219-229.
9. Litjens G, Sánchez CI, Timofeeva N, et al. Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci Rep, 2016, 6: 26286.
10. Ertosun MG, Rubin DL. Automated grading of gliomas using deep learning in digital pathology images: A modular approach with ensemble of convolutional neural networks. AMIA Annu Symp Proc, 2015, 2015: 1899-1908.
11. Hanna N, Johnson D, Temin S, et al. Systemic therapy for stage Ⅳ non-small-cell lung cancer: American Society of Clinical Oncology clinical practice guideline update. J Clin Oncol, 2017, 35(30): 3484-3515.
12. 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.
13. Khosravi P, Kazemi E, Imielinski M, et al. Deep convolutional neural networks enable discrimination of heterogeneous digital pathology images. EBioMedicine, 2018, 27: 317-328.
14. 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.
15. Wang X, Chen H, Gan C, et al. Weakly supervised deep learning for whole slide lung cancer image analysis. IEEE Trans Cybern, 2020, 50(9): 3950-3962.
16. Song SH, Park H, Lee G, et al. Imaging phenotyping using radiomics to predict micropapillary pattern within lung adenocarcinoma. J Thorac Oncol, 2017, 12(4): 624-632.
17. Marx A, Chan JK, Coindre JM, et al. The 2015 World Health Organization classification of tumors of the thymus: Continuity and changes. J Thorac Oncol, 2015, 10(10): 1383-1395.
18. Hung JJ, Yeh YC, Jeng WJ, et al. Predictive value of the International Association for the Sudy of Lung Cancer/American Thoracic Society/European Respiratory Society classification of lung adenocarcinoma in tumor recurrence and patient survival. J Clin Oncol, 2014, 32(22): 2357-2364.
19. Tsao MS, Marguet S, Le Teuff G, et al. Subtype classification of lung adenocarcinoma predicts benefit from adjuvant chemotherapy in patients undergoing complete resection. J Clin Oncol, 2015, 33(30): 3439-3446.
20. 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.
21. Larsen JE, Cascone T, Gerber DE, et al. Targeted therapies for lung cancer: Clinical experience and novel agents. Cancer J, 2011, 17(6): 512-527.
22. Li Q, Wang X, Liang F, et al. A Bayesian hidden potts mixture model for analyzing lung cancer pathology images. Biostatistics, 2019, 20(4): 565-581.
23. Choi H, Na KJ. A risk stratification model for lung cancer based on gene coexpression network and deep learning. Biomed Res Int, 2018, 2018: 2914280.
24. Mezheyeuski A, Bergsland CH, Backman M, et al. Multispectral imaging for quantitative and compartment-specific immune infiltrates reveals distinct immune profiles that classify lung cancer patients. J Pathol, 2018, 244(4): 421-431.
25. Trivella M, Pezzella F, Pastorino U, et al. Microvessel density as a prognostic factor in non-small-cell lung carcinoma: A meta-analysis of individual patient data. Lancet Oncol, 2007, 8(6): 488-499.
26. Yi F, Yang L, Wang S, et al. Microvessel prediction in H& E stained pathology images using fully convolutional neural networks. BMC Bioinformatics, 2018, 19(1): 64.
27. Wang S, Wang T, Yang L, et al. ConvPath: A software tool for lung adenocarcinoma digital pathological image analysis aided by a convolutional neural network. EBioMedicine, 2019, 50: 103-110.
28. 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.
29. 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.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Research progress on artificial intelligence in precise pathological diagnosis of lung cancer

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