Research progress on the application of artificial intelligence in the pathology and prognosis of non-small cell lung cancer_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

WANG Zhilin , ZHU Xiaolei , ZHANG Xiaowen , FENG Yihui , PAN Jianyun ,  JIANG Jie ,  GENG Guojun

Department of Thoracic Surgery, The First Affilated Hospital of Xiamen University, Xiamen, 361003, Fujian, P. R. China;

Corresponding author：

JIANG Jie, Email: jiangjie06@126.com; GENG Guojun, Email: ggj622@126.com

Keywords：

Non-small cell lung cancer; artificial intelligence; pathology; prognosis prediction; review

DOI：

10.7507/1007-4848.202205033

Video：

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Abstract Full text Figures/Tables Video References Cited by

Non-small cell lung cancer is the main cause of cancer death in the world, and its incidence is increasing year by year, seriously endangering human health. Early non-small cell lung cancer is generally difficult to be detected based on symptoms and signs. Therefore, accurate pathological diagnosis and accurate prediction of prognosis are crucial for formulating the best treatment plan for non-small cell lung cancer patients and improving their survival. The application of artificial intelligence in the diagnosis and treatment of non-small cell lung cancer has shown good performance and great potential effect. This paper introduces the research progress of artificial intelligence in predicting the classification, staging, genomics and prognosis of non-small cell lung cancer.

Citation： WANG Zhilin, ZHU Xiaolei, ZHANG Xiaowen, FENG Yihui, PAN Jianyun, JIANG Jie, GENG Guojun. Research progress on the application of artificial intelligence in the pathology and prognosis of non-small cell lung cancer. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2022, 29(9): 1223-1229. doi: 10.7507/1007-4848.202205033 Copy

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19.	Bin L, Yuan T, Zhaohui S, et al. A deep learning-based dual-omics prediction model for radiation pneumonitis. Med Phys, 2021, 48(10): 6247-6256.
20.	Chaunzwa TL, Hosny A, Xu Y, et al. Deep learning classification of lung cancer histology using CT images. Sci Rep, 2021, 11(1): 5471.
21.	Tau N, Stundzia A, Yasufuku K, et al. Convolutional neural networks in predicting nodal and distant metastatic potential of newly diagnosed non-small cell lung cancer on FDG PET images. AJR Am J Roentgenol, 2020, 215(1): 192-197.
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27.	Bortolotto C, Lancia A, Stelitano C, et al. Radiomics features as predictive and prognostic biomarkers in NSCLC. Expert Rev Anticancer Ther, 2021, 21(3): 257-266.
28.	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.
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.
30.	Khosravi P, Kazemi E, Imielinski M, et al. Deep convolutional neural networks enable discrimination of heterogeneous digital pathology images. EBioMedicine, 2018, 27: 317-328.
31.	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.
32.	Tsao MS, Nicholson AG, Maleszewski JJ, et al. Introduction to 2021 WHO classification of thoracic tumors. J Thorac Oncol, 2022, 17(1): e1-e4.
33.	Warth A, Cortis J, Fink L, et al. Training increases concordance in classifying pulmonary adenocarcinomas according to the novel IASLC/ATS/ERS classification. Virchows Arch, 2012, 461(2): 185-193.
34.	Girard N, Deshpande C, Lau C, et al. Comprehensive histologic assessment helps to differentiate multiple lung primary nonsmall cell carcinomas from metastases. Am J Surg Pathol, 2009, 33(12): 1752-1764.
35.	Wang C, Shao J, Lv J, et al. Deep learning for predicting subtype classification and survival of lung adenocarcinoma on computed tomography. Transl Oncol, 2021, 14(8): 101141.
36.	Xiao Y, Wu J, Lin Z, et al. A deep learning-based multi-model ensemble method for cancer prediction. Comput Methods Programs Biomed, 2018, 153: 1-9.
37.	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.
38.	Li Y, Chen D, Wu X, et al. A narrative review of artificial intelligence-assisted histopathologic diagnosis and decision-making for non-small cell lung cancer: Achievements and limitations. J Thorac Dis, 2021, 13(12): 7006-7020.
39.	Crinò L, Weder W, van Meerbeeck J, et al. Early stage and locally advanced (non-metastatic) non-small-cell lung cancer: ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Ann Oncol, 2010, 21 Suppl 5: v103-v115.
40.	Yang Z, Yin H, Shi L, et al. A novel microRNA signature for pathological grading in lung adenocarcinoma based on TCGA and GEO data. Int J Mol Med, 2020, 45(5): 1397-1408.
41.	Choi J, Cho HH, Kwon J, et al. A cascaded neural network for staging in non-small cell lung cancer using pre-treatment CT. Diagnostics (Basel), 2021, 11(6): 1047.
42.	Dong Y, Yang W, Wang J, et al. MLW-gcForest: A multi-weighted gcForest model towards the staging of lung adenocarcinoma based on multi-modal genetic data. BMC Bioinformatics, 2019, 20(1): 578.
43.	Shi L, He Y, Yuan Z, et al. Radiomics for response and outcome assessment for non-small cell lung cancer. Technol Cancer Res Treat, 2018, 17: 1533033818782788.
44.	Shiri I, Maleki H, Hajianfar G, et al. Next-generation radiogenomics sequencing for prediction of EGFR and KRAS mutation status in NSCLC patients using multimodal imaging and machine learning algorithms. Mol Imaging Biol, 2020, 22(4): 1132-1148.
45.	Kobayashi K, Bolatkan A, Shiina S, et al. Fully-connected neural networks with reduced parameterization for predicting histological types of lung cancer from somatic mutations. Biomolecules, 2020, 10(9): 1249.
46.	Wang J, Xie X, Shi J, et al. Denoising autoencoder, a deep learning algorithm, aids the identification of a novel molecular signature of lung adenocarcinoma. Genomics Proteomics Bioinformatics, 2020, 18(4): 468-480.
47.	Chen HY, Yu SL, Chen CH, et al. A five-gene signature and clinical outcome in non-small-cell lung cancer. N Engl J Med, 2007, 356(1): 11-20.
48.	Shedden K, Taylor JM, et al. Gene expression-based survival prediction in lung adenocarcinoma: A multi-site, blinded validation study. Nat Med, 2008, 14(8): 822-827.
49.	Gentles AJ, Bratman SV, Lee LJ, et al. Integrating tumor and stromal gene expression signatures with clinical indices for survival stratification of early-stage non-small cell lung cancer. J Natl Cancer Inst, 2015, 107(10): djv211.
50.	Huang P, Cheng CL, Chang YH, et al. Molecular gene signature and prognosis of non-small cell lung cancer. Oncotarget, 2016, 7(32): 51898-51907.
51.	Krzystanek M, Moldvay J, Szüts D, et al. A robust prognostic gene expression signature for early stage lung adenocarcinoma. Biomark Res, 2016, 4: 4.
52.	Shahid M, Choi TG, Nguyen MN, et al. An 8-gene signature for prediction of prognosis and chemoresponse in non-small cell lung cancer. Oncotarget, 2016, 7(52): 86561-86572.
53.	Zhou M, Leung A, Echegaray S, et al. Non-small cell lung cancer radiogenomics map identifies relationships between molecular and imaging phenotypes with prognostic implications. Radiology, 2018, 286(1): 307-315.
54.	Wong CW, Chaudhry A. Radiogenomics of lung cancer. J Thorac Dis, 2020, 12(9): 5104-5109.
55.	Lee B, Chun SH, Hong JH, et al. DeepBTS: Prediction of recurrence-free survival of non-small cell lung cancer using a time-binned deep neural network. Sci Rep, 2020, 10(1): 1952.
56.	Alaa AM, Bolton T, Di Angelantonio E, et al. Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423, 604 UK Biobank participants. PLoS One, 2019, 14(5): e0213653.
57.	Kanda E, Epureanu BI, Adachi T, et al. Application of explainable ensemble artificial intelligence model to categorization of hemodialysis-patient and treatment using nationwide-real-world data in Japan. PLoS One, 2020, 15(5): e0233491.
58.	Matsuo K, Purushotham S, Jiang B, et al. Survival outcome prediction in cervical cancer: Cox models vs deep-learning model. Am J Obstet Gynecol, 2019, 220(4): 381.e1-381.e14.
59.	Stevens LM, Linstead E, Hall JL, et al. Association between coffee intake and incident heart failure risk: A machine learning analysis of the FHS, the ARIC Study, and the CHS. Circ Heart Fail, 2021, 14(2): e006799.
60.	Siah KW, Khozin S, Wong CH, et al. Machine-learning and stochastic tumor growth models for predicting outcomes in patients with advanced non-small-cell lung cancer. JCO Clin Cancer Inform, 2019, 3: 1-11.

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. Zheng R, Zhang S, Zeng H, et al. Cancer incidence and mortality in China, 2016. JNCC, 2022, 2(1): 1-9.
3. Molina JR, Yang P, Cassivi SD, et al. Non-small cell lung cancer: Epidemiology, risk factors, treatment, and survivorship. Mayo Clin Proc, 2008, 83(5): 584-594.
4. Hirsch FR, Scagliotti GV, Mulshine JL, et al. Lung cancer: Current therapies and new targeted treatments. Lancet, 2017, 389(10066): 299-311.
5. Schegoleva AA, Khozyainova AA, Fedorov AA, et al. Prognosis of different types of non-small cell lung cancer progression: Current state and perspectives. Cell Physiol Biochem, 2021, 55(S2): 29-48.
6. Holmes JH, Sacchi L, Bellazzi R, et al. Artificial intelligence in medicine AIME 2015. Artif Intell Med, 2017, 81: 1-2.
7. Schaffter T, Buist DSM, Lee CI, et al. Evaluation of combined artificial intelligence and radiologist assessment to interpret screening mammograms. JAMA Netw Open, 2020, 3(3): e200265.
8. Das K, Cockerell CJ, Patil A, et al. Machine learning and its application in skin cancer. Int J Environ Res Public Health, 2021, 18(24): 13409.
9. Gandi C, Vaccarella L, Bientinesi R, et al. Bladder cancer in the time of machine learning: Intelligent tools for diagnosis and management. Urologia, 2021, 88(2): 94-102.
10. Goldenberg SL, Nir G, Salcudean SE. A new era: Artificial intelligence and machine learning in prostate cancer. Nat Rev Urol, 2019, 16(7): 391-403.
11. Howard FM, Kochanny S, Koshy M, et al. Machine learning-guided adjuvant treatment of head and neck cancer. JAMA Netw Open, 2020, 3(11): e2025881.
12. Ji GW, Wang K, Xia YX, et al. Integrating machine learning and tumor immune signature to predict oncologic outcomes in resected biliary tract cancer. Ann Surg Oncol, 2021, 28(7): 4018-4029.
13. Sultan AS, Elgharib MA, Tavares T, et al. The use of artificial intelligence, machine learning and deep learning in oncologic histopathology. J Oral Pathol Med, 2020, 49(9): 849-856.
14. Lemaître G, Martí R, Freixenet J, et al. Computer-aided detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review. Comput Biol Med, 2015, 60: 8-31.
15. 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.
16. Hosny A, Parmar C, Coroller TP, et al. Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study. PLoS Med, 2018, 15(11): e1002711.
17. She Y, Jin Z, Wu J, et al. Development and validation of a deep learning model for non-small cell lung cancer survival. JAMA Netw Open, 2020, 3(6): e205842.
18. Xu Y, Hosny A, Zeleznik R, et al. Deep learning predicts lung cancer treatment response from serial medical imaging. Clin Cancer Res, 2019, 25(11): 3266-3275.
19. Bin L, Yuan T, Zhaohui S, et al. A deep learning-based dual-omics prediction model for radiation pneumonitis. Med Phys, 2021, 48(10): 6247-6256.
20. Chaunzwa TL, Hosny A, Xu Y, et al. Deep learning classification of lung cancer histology using CT images. Sci Rep, 2021, 11(1): 5471.
21. Tau N, Stundzia A, Yasufuku K, et al. Convolutional neural networks in predicting nodal and distant metastatic potential of newly diagnosed non-small cell lung cancer on FDG PET images. AJR Am J Roentgenol, 2020, 215(1): 192-197.
22. Wang H, Zhou Z, Li Y, et al. Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18 F-FDG PET/CT images. EJNMMI Res, 2017, 7(1): 11.
23. Yu Y, Zeng D, Ou Q, et al. Association of survival and immune-related biomarkers with immunotherapy in patients with non-small cell lung cancer: A meta-analysis and individual patient-level analysis. JAMA Netw Open, 2019, 2(7): e196879.
24. Mosquera-Lopez C, Agaian S, Velez-Hoyos A, et al. Computer-aided prostate cancer diagnosis from digitized histopathology: A review on texture-based systems. IEEE Rev Biomed Eng, 2015, 8: 98-113.
25. Yu W, Tang C, Hobbs BP, et al. Development and validation of a predictive radiomics model for clinical outcomes in stage Ⅰ non-small cell lung cancer. Int J Radiat Oncol Biol Phys, 2018, 102(4): 1090-1097.
26. Avanzo M, Stancanello J, Pirrone G, et al. Radiomics and deep learning in lung cancer. Strahlenther Onkol, 2020, 196(10): 879-887.
27. Bortolotto C, Lancia A, Stelitano C, et al. Radiomics features as predictive and prognostic biomarkers in NSCLC. Expert Rev Anticancer Ther, 2021, 21(3): 257-266.
28. 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.
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.
30. Khosravi P, Kazemi E, Imielinski M, et al. Deep convolutional neural networks enable discrimination of heterogeneous digital pathology images. EBioMedicine, 2018, 27: 317-328.
31. 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.
32. Tsao MS, Nicholson AG, Maleszewski JJ, et al. Introduction to 2021 WHO classification of thoracic tumors. J Thorac Oncol, 2022, 17(1): e1-e4.
33. Warth A, Cortis J, Fink L, et al. Training increases concordance in classifying pulmonary adenocarcinomas according to the novel IASLC/ATS/ERS classification. Virchows Arch, 2012, 461(2): 185-193.
34. Girard N, Deshpande C, Lau C, et al. Comprehensive histologic assessment helps to differentiate multiple lung primary nonsmall cell carcinomas from metastases. Am J Surg Pathol, 2009, 33(12): 1752-1764.
35. Wang C, Shao J, Lv J, et al. Deep learning for predicting subtype classification and survival of lung adenocarcinoma on computed tomography. Transl Oncol, 2021, 14(8): 101141.
36. Xiao Y, Wu J, Lin Z, et al. A deep learning-based multi-model ensemble method for cancer prediction. Comput Methods Programs Biomed, 2018, 153: 1-9.
37. 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.
38. Li Y, Chen D, Wu X, et al. A narrative review of artificial intelligence-assisted histopathologic diagnosis and decision-making for non-small cell lung cancer: Achievements and limitations. J Thorac Dis, 2021, 13(12): 7006-7020.
39. Crinò L, Weder W, van Meerbeeck J, et al. Early stage and locally advanced (non-metastatic) non-small-cell lung cancer: ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Ann Oncol, 2010, 21 Suppl 5: v103-v115.
40. Yang Z, Yin H, Shi L, et al. A novel microRNA signature for pathological grading in lung adenocarcinoma based on TCGA and GEO data. Int J Mol Med, 2020, 45(5): 1397-1408.
41. Choi J, Cho HH, Kwon J, et al. A cascaded neural network for staging in non-small cell lung cancer using pre-treatment CT. Diagnostics (Basel), 2021, 11(6): 1047.
42. Dong Y, Yang W, Wang J, et al. MLW-gcForest: A multi-weighted gcForest model towards the staging of lung adenocarcinoma based on multi-modal genetic data. BMC Bioinformatics, 2019, 20(1): 578.
43. Shi L, He Y, Yuan Z, et al. Radiomics for response and outcome assessment for non-small cell lung cancer. Technol Cancer Res Treat, 2018, 17: 1533033818782788.
44. Shiri I, Maleki H, Hajianfar G, et al. Next-generation radiogenomics sequencing for prediction of EGFR and KRAS mutation status in NSCLC patients using multimodal imaging and machine learning algorithms. Mol Imaging Biol, 2020, 22(4): 1132-1148.
45. Kobayashi K, Bolatkan A, Shiina S, et al. Fully-connected neural networks with reduced parameterization for predicting histological types of lung cancer from somatic mutations. Biomolecules, 2020, 10(9): 1249.
46. Wang J, Xie X, Shi J, et al. Denoising autoencoder, a deep learning algorithm, aids the identification of a novel molecular signature of lung adenocarcinoma. Genomics Proteomics Bioinformatics, 2020, 18(4): 468-480.
47. Chen HY, Yu SL, Chen CH, et al. A five-gene signature and clinical outcome in non-small-cell lung cancer. N Engl J Med, 2007, 356(1): 11-20.
48. Shedden K, Taylor JM, et al. Gene expression-based survival prediction in lung adenocarcinoma: A multi-site, blinded validation study. Nat Med, 2008, 14(8): 822-827.
49. Gentles AJ, Bratman SV, Lee LJ, et al. Integrating tumor and stromal gene expression signatures with clinical indices for survival stratification of early-stage non-small cell lung cancer. J Natl Cancer Inst, 2015, 107(10): djv211.
50. Huang P, Cheng CL, Chang YH, et al. Molecular gene signature and prognosis of non-small cell lung cancer. Oncotarget, 2016, 7(32): 51898-51907.
51. Krzystanek M, Moldvay J, Szüts D, et al. A robust prognostic gene expression signature for early stage lung adenocarcinoma. Biomark Res, 2016, 4: 4.
52. Shahid M, Choi TG, Nguyen MN, et al. An 8-gene signature for prediction of prognosis and chemoresponse in non-small cell lung cancer. Oncotarget, 2016, 7(52): 86561-86572.
53. Zhou M, Leung A, Echegaray S, et al. Non-small cell lung cancer radiogenomics map identifies relationships between molecular and imaging phenotypes with prognostic implications. Radiology, 2018, 286(1): 307-315.
54. Wong CW, Chaudhry A. Radiogenomics of lung cancer. J Thorac Dis, 2020, 12(9): 5104-5109.
55. Lee B, Chun SH, Hong JH, et al. DeepBTS: Prediction of recurrence-free survival of non-small cell lung cancer using a time-binned deep neural network. Sci Rep, 2020, 10(1): 1952.
56. Alaa AM, Bolton T, Di Angelantonio E, et al. Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423, 604 UK Biobank participants. PLoS One, 2019, 14(5): e0213653.
57. Kanda E, Epureanu BI, Adachi T, et al. Application of explainable ensemble artificial intelligence model to categorization of hemodialysis-patient and treatment using nationwide-real-world data in Japan. PLoS One, 2020, 15(5): e0233491.
58. Matsuo K, Purushotham S, Jiang B, et al. Survival outcome prediction in cervical cancer: Cox models vs deep-learning model. Am J Obstet Gynecol, 2019, 220(4): 381.e1-381.e14.
59. Stevens LM, Linstead E, Hall JL, et al. Association between coffee intake and incident heart failure risk: A machine learning analysis of the FHS, the ARIC Study, and the CHS. Circ Heart Fail, 2021, 14(2): e006799.
60. Siah KW, Khozin S, Wong CH, et al. Machine-learning and stochastic tumor growth models for predicting outcomes in patients with advanced non-small-cell lung cancer. JCO Clin Cancer Inform, 2019, 3: 1-11.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Research progress on the application of artificial intelligence in the pathology and prognosis of non-small cell lung cancer

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