Research progress of artificial intelligence combined with omics data in the diagnosis and treatment of non-small cell lung cancer_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

SUN Siying ,  LI Hecheng

Department of Thoracic Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, P. R. China;

Corresponding author：

LI Hecheng, Email: lihecheng2000@hotmail.com

Keywords：

Artificial intelligence; omics; non-small cell lung cancer; review

DOI：

10.7507/1007-4848.202202051

Video：

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In recent years, the computer science represented by artificial intelligence and high-throughput sequencing technology represented by omics play a significant role in the medical field. This paper reviews the research progress of the application of artificial intelligence combined with omics data analysis in the diagnosis and treatment of non-small cell lung cancer (NSCLC), aiming to provide ideas for the development of a more effective artificial intelligence algorithm, and improve the diagnosis rate and prognosis of patients with early NSCLC through a non-invasive way.

Citation： SUN Siying, LI Hecheng. Research progress of artificial intelligence combined with omics data in the diagnosis and treatment of non-small cell lung cancer. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2023, 30(2): 305-312. doi: 10.7507/1007-4848.202202051 Copy

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, Fuchs HE, et al. Cancer statistics, 2021. CA Cancer J Clin, 2021, 71(1): 7-33.
3.	Deo RC. Machine learning in medicine. Circulation, 2015, 132(20): 1920-1930.
4.	Kann BH, Hosny A, Aerts HJWL. Artificial intelligence for clinical oncology. Cancer Cell, 2021, 39(7): 916-927.
5.	Lecun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521(7553): 436-44.
6.	王昕玥, 渠鸿竹, 方向东. 组学大数据和医学人工智能. 遗传, 2021, 43(10): 930-937.
7.	Li J, Chen H, Wang Y, et al. Next-generation analytics for omics data. Cancer Cell, 2021, 39(1): 3-6.
8.	Gould MK, Huang BZ, Tammemagi MC, et al. Machine learning for early lung cancer identification using routine clinical and laboratory data. Am J Respir Crit Care Med, 2021, 204(4): 445-453.
9.	Hanahan D, Weinberg RA. Hallmarks of cancer: The next generation. Cell, 2011, 144(5): 646-674.
10.	Huang L, Wang L, Hu X, et al. Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma. Nat Commun, 2020, 11(1): 3556.
11.	Mayerhoefer ME, Materka A, Langs G, et al. Introduction to radiomics. J Nucl Med, 2020, 61(4): 488-495.
12.	Blanc D, Racine V, Khalil A, et al. Artificial intelligence solution to classify pulmonary nodules on CT. Diagn Interv Imaging, 2020, 101(12): 803-810.
13.	Hawkins S, Wang H, Liu Y, et al. Predicting malignant nodules from screening CT scans. J Thorac Oncol, 2016, 11(12): 2120-2128.
14.	刘晓鹏, 周海英, 胡志雄, 等. 人工智能识别技术在T1期肺癌诊断中的临床应用研究. 中国肺癌杂志, 2019, 22(5): 319-323.
15.	Ardila D, Kiraly AP, Bharadwaj S, et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med, 2019, 25(6): 954-961.
16.	Baldwin DR, Gustafson J, Pickup L, et al. External validation of a convolutional neural network artificial intelligence tool to predict malignancy in pulmonary nodules. Thorax, 2020, 75(4): 306-312.
17.	Ciompi F, Chung K, van Riel SJ, et al. Towards automatic pulmonary nodule management in lung cancer screening with deep learning. Sci Rep, 2017, 7: 46479.
18.	Krarup MMK, Krokos G, Subesinghe M, et al. Artificial intelligence for the characterization of pulmonary nodules, lung tumors and mediastinal nodes on PET/CT. Semin Nucl Med, 2021, 51(2): 143-156.
19.	Zhong Z, Kim Y, Plichta K, et al. Simultaneous cosegmentation of tumors in PET-CT images using deep fully convolutional networks. Med Phys, 2019, 46(2): 619-633.
20.	Zhong Y, She Y, Deng J, et al. Deep learning for prediction of N2 metastasis and survival for clinical stage Ⅰ non-small cell lung cancer. Radiology, 2022, 302(1): 200-211.
21.	Ulahannan D, Khalifa J, Faivre-Finn C, et al. Emerging treatment paradigms for brain metastasis in non-small-cell lung cancer: An overview of the current landscape and challenges ahead. Ann Oncol, 2017, 28(12): 2923-2931.
22.	Jünger ST, Hoyer UCI, Schaufler D, et al. Fully automated MR detection and segmentation of brain metastases in non-small cell lung cancer using deep learning. J Magn Reson Imaging, 2021, 54(5): 1608-1622.
23.	Pan C, Schoppe O, Parra-Damas A, et al. Deep learning reveals cancer metastasis and therapeutic antibody targeting in the entire body. Cell, 2019, 179(7): 1661-1676.
24.	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.
25.	Rossi G, Barabino E, Fedeli A, et al. Radiomic detection of EGFR mutations in NSCLC. Cancer Res, 2021, 81(3): 724-731.
26.	Mu W, Jiang L, Zhang J, et al. Non-invasive decision support for NSCLC treatment using PET/CT radiomics. Nat Commun, 2020, 11(1): 5228.
27.	Herbst RS, Morgensztern D, Boshoff C. The biology and management of non-small cell lung cancer. Nature, 2018, 553(7689): 446-454.
28.	Song Z, Liu T, Shi L, et al. The deep learning model combining CT image and clinicopathological information for predicting ALK fusion status and response to ALK-TKI therapy in non-small cell lung cancer patients. Eur J Nucl Med Mol Imaging, 2021, 48(2): 361-371.
29.	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.
30.	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.
31.	Cui S, Ten Haken RK, El Naqa I. Integrating multiomics information in deep learning architectures for joint actuarial outcome prediction in non-small cell lung cancer patients after radiation therapy. Int J Radiat Oncol Biol Phys, 2021, 110(3): 893-904.
32.	Arbour KC, Luu AT, Luo J, et al. Deep learning to estimate RECIST in patients with NSCLC treated with PD-1 blockade. Cancer Discov, 2021, 11(1): 59-67.
33.	Tian P, He B, Mu W, et al. Assessing PD-L1 expression in non-small cell lung cancer and predicting responses to immune checkpoint inhibitors using deep learning on computed tomography images. Theranostics, 2021, 11(5): 2098-2107.
34.	Trebeschi S, Drago SG, Birkbak NJ, et al. Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers. Ann Oncol, 2019, 30(6): 998-1004.
35.	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.
36.	Song J, Wang L, Ng NN, et al. Development and validation of a machine learning model to explore tyrosine kinase inhibitor response in patients with stage Ⅳ EGFR variant-positive non-small cell lung cancer. JAMA Netw Open, 2020, 3(12): e2030442.
37.	AbdulJabbar K, Raza SEA, Rosenthal R, et al. Geospatial immune variability illuminates differential evolution of lung adenocarcinoma. Nat Med, 2020, 26(7): 1054-1062.
38.	Combes AJ, Samad B, Tsui J, et al. Discovering dominant tumor immune archetypes in a pan-cancer census. Cell, 2022, 185(1): 184-203.
39.	Luo R, Song J, Xiao X, et al. Identifying CpG methylation signature as a promising biomarker for recurrence and immunotherapy in non-small-cell lung carcinoma. Aging (Albany NY), 2020, 12(14): 14649-14676.
40.	Saeidi H, Opfermann JD, Kam M, et al. Autonomous robotic laparoscopic surgery for intestinal anastomosis. Sci Robot, 2022, 7(62): eabj2908.

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, Fuchs HE, et al. Cancer statistics, 2021. CA Cancer J Clin, 2021, 71(1): 7-33.
3. Deo RC. Machine learning in medicine. Circulation, 2015, 132(20): 1920-1930.
4. Kann BH, Hosny A, Aerts HJWL. Artificial intelligence for clinical oncology. Cancer Cell, 2021, 39(7): 916-927.
5. Lecun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521(7553): 436-44.
6. 王昕玥, 渠鸿竹, 方向东. 组学大数据和医学人工智能. 遗传, 2021, 43(10): 930-937.
7. Li J, Chen H, Wang Y, et al. Next-generation analytics for omics data. Cancer Cell, 2021, 39(1): 3-6.
8. Gould MK, Huang BZ, Tammemagi MC, et al. Machine learning for early lung cancer identification using routine clinical and laboratory data. Am J Respir Crit Care Med, 2021, 204(4): 445-453.
9. Hanahan D, Weinberg RA. Hallmarks of cancer: The next generation. Cell, 2011, 144(5): 646-674.
10. Huang L, Wang L, Hu X, et al. Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma. Nat Commun, 2020, 11(1): 3556.
11. Mayerhoefer ME, Materka A, Langs G, et al. Introduction to radiomics. J Nucl Med, 2020, 61(4): 488-495.
12. Blanc D, Racine V, Khalil A, et al. Artificial intelligence solution to classify pulmonary nodules on CT. Diagn Interv Imaging, 2020, 101(12): 803-810.
13. Hawkins S, Wang H, Liu Y, et al. Predicting malignant nodules from screening CT scans. J Thorac Oncol, 2016, 11(12): 2120-2128.
14. 刘晓鹏, 周海英, 胡志雄, 等. 人工智能识别技术在T1期肺癌诊断中的临床应用研究. 中国肺癌杂志, 2019, 22(5): 319-323.
15. Ardila D, Kiraly AP, Bharadwaj S, et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med, 2019, 25(6): 954-961.
16. Baldwin DR, Gustafson J, Pickup L, et al. External validation of a convolutional neural network artificial intelligence tool to predict malignancy in pulmonary nodules. Thorax, 2020, 75(4): 306-312.
17. Ciompi F, Chung K, van Riel SJ, et al. Towards automatic pulmonary nodule management in lung cancer screening with deep learning. Sci Rep, 2017, 7: 46479.
18. Krarup MMK, Krokos G, Subesinghe M, et al. Artificial intelligence for the characterization of pulmonary nodules, lung tumors and mediastinal nodes on PET/CT. Semin Nucl Med, 2021, 51(2): 143-156.
19. Zhong Z, Kim Y, Plichta K, et al. Simultaneous cosegmentation of tumors in PET-CT images using deep fully convolutional networks. Med Phys, 2019, 46(2): 619-633.
20. Zhong Y, She Y, Deng J, et al. Deep learning for prediction of N2 metastasis and survival for clinical stage Ⅰ non-small cell lung cancer. Radiology, 2022, 302(1): 200-211.
21. Ulahannan D, Khalifa J, Faivre-Finn C, et al. Emerging treatment paradigms for brain metastasis in non-small-cell lung cancer: An overview of the current landscape and challenges ahead. Ann Oncol, 2017, 28(12): 2923-2931.
22. Jünger ST, Hoyer UCI, Schaufler D, et al. Fully automated MR detection and segmentation of brain metastases in non-small cell lung cancer using deep learning. J Magn Reson Imaging, 2021, 54(5): 1608-1622.
23. Pan C, Schoppe O, Parra-Damas A, et al. Deep learning reveals cancer metastasis and therapeutic antibody targeting in the entire body. Cell, 2019, 179(7): 1661-1676.
24. 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.
25. Rossi G, Barabino E, Fedeli A, et al. Radiomic detection of EGFR mutations in NSCLC. Cancer Res, 2021, 81(3): 724-731.
26. Mu W, Jiang L, Zhang J, et al. Non-invasive decision support for NSCLC treatment using PET/CT radiomics. Nat Commun, 2020, 11(1): 5228.
27. Herbst RS, Morgensztern D, Boshoff C. The biology and management of non-small cell lung cancer. Nature, 2018, 553(7689): 446-454.
28. Song Z, Liu T, Shi L, et al. The deep learning model combining CT image and clinicopathological information for predicting ALK fusion status and response to ALK-TKI therapy in non-small cell lung cancer patients. Eur J Nucl Med Mol Imaging, 2021, 48(2): 361-371.
29. 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.
30. 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.
31. Cui S, Ten Haken RK, El Naqa I. Integrating multiomics information in deep learning architectures for joint actuarial outcome prediction in non-small cell lung cancer patients after radiation therapy. Int J Radiat Oncol Biol Phys, 2021, 110(3): 893-904.
32. Arbour KC, Luu AT, Luo J, et al. Deep learning to estimate RECIST in patients with NSCLC treated with PD-1 blockade. Cancer Discov, 2021, 11(1): 59-67.
33. Tian P, He B, Mu W, et al. Assessing PD-L1 expression in non-small cell lung cancer and predicting responses to immune checkpoint inhibitors using deep learning on computed tomography images. Theranostics, 2021, 11(5): 2098-2107.
34. Trebeschi S, Drago SG, Birkbak NJ, et al. Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers. Ann Oncol, 2019, 30(6): 998-1004.
35. 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.
36. Song J, Wang L, Ng NN, et al. Development and validation of a machine learning model to explore tyrosine kinase inhibitor response in patients with stage Ⅳ EGFR variant-positive non-small cell lung cancer. JAMA Netw Open, 2020, 3(12): e2030442.
37. AbdulJabbar K, Raza SEA, Rosenthal R, et al. Geospatial immune variability illuminates differential evolution of lung adenocarcinoma. Nat Med, 2020, 26(7): 1054-1062.
38. Combes AJ, Samad B, Tsui J, et al. Discovering dominant tumor immune archetypes in a pan-cancer census. Cell, 2022, 185(1): 184-203.
39. Luo R, Song J, Xiao X, et al. Identifying CpG methylation signature as a promising biomarker for recurrence and immunotherapy in non-small-cell lung carcinoma. Aging (Albany NY), 2020, 12(14): 14649-14676.
40. Saeidi H, Opfermann JD, Kam M, et al. Autonomous robotic laparoscopic surgery for intestinal anastomosis. Sci Robot, 2022, 7(62): eabj2908.

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Research progress of artificial intelligence combined with omics data in the diagnosis and treatment of non-small cell lung cancer

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