Current situation and prospects of machine learning applications in the study of esophageal cancer_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

WU Yuefeng ¹ , WANG Qi ² ,  WU Ming ²

1. Zhejiang University-University of Edinburgh Institute, Haining, 314400, Zhejiang, P. R. China;
2. Department of Thoracic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, P. R. China;

Corresponding author：

WU Ming, Email: iwuming22@zju.edu.cn

Keywords：

Machine learning; immunohistochemistry; medical image; esophageal cancer; molecular mechanism; review; diagnosis

DOI：

10.7507/1007-4848.202203015

Video：

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China is one of the countries in the world with the highest rate of esophageal cancer. Early detection, accurate diagnosis, and treatment of esophageal cancer are critical for improving patients’ prognosis and survival. Machine learning technology has become widely used in cancer, which is benefited from the accumulation of medical images and advancement of artificial intelligence technology. Therefore, the learning model, image type, data type and application efficiency of current machine learning technology in esophageal cancer are summarized in this review. The major challenges are identified, and solutions are proposed in medical image machine learning for esophageal cancer. Machine learning's potential future directions in esophageal cancer diagnosis and treatment are discussed, with a focus on the possibility of establishing a link between medical images and molecular mechanisms. The general rules of machine learning application in the medical field are summarized and forecasted on this foundation. By drawing on the advanced achievements of machine learning in other cancers and focusing on interdisciplinary cooperation, esophageal cancer research will be effectively promoted.

Citation： WU Yuefeng, WANG Qi, WU Ming. Current situation and prospects of machine learning applications in the study of esophageal cancer. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2022, 29(6): 770-776. doi: 10.7507/1007-4848.202203015 Copy

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16.	赫捷, 邵康. 中国食管癌流行病学现状、诊疗现状及未来对策. 中国癌症杂志, 2011, 21(7): 4.
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32.	王姗姗, 翟晓梅. 人工智能医学应用的伦理问题. 中国医学伦理学, 2019, 32(8): 972-976.
33.	周吉银, 刘丹, 曾圣雅. 人工智能在医疗领域中应用的挑战与对策. 中国医学伦理学, 2019, 32(3): 281-286.
34.	Pearson J D, Bremner R. Simplifying cancer: Binary pan-cancer superclasses stratified by opposite YAP/TEAD effects. Mol Cell Oncol, 2021, 8(5): 1981111.
35.	Doshi-Velez F, Kim B. Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv: 1702.08608, 2017.
36.	纪守领, 李进锋, 杜天宇, 等. 机器学习模型可解释性方法, 应用与安全研究综述. 计算机研究与发展, 2019, 56(10): 2071.
37.	Rajpurkar P, Irvin J, Ball RL, et al. Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Med, 2018, 15(11): e1002686.
38.	Chen Q, Kuai Y, Wang S, et al. Deep learning-based classification of epithelial-mesenchymal transition for predicting response to therapy in clear cell renal cell carcinoma. Front Oncol, 2021, 11: 782515.
39.	张珺倩, 张远, 尹勇, 等. 机器学习在肿瘤放射治疗领域应用进展. 生物医学工程学杂志, 2019, 36(5): 879-884.

1. 肖宝臣, 江虹. X线胸片与CT诊断肺癌临床分析. 中国医药指南, 2008, 6(15): 256.
2. 孙红, 白友贤. X线胸片与CT诊断1cm左右周围型小肺癌. 中国医学影像学杂志, 1998, 6(1): 3.
3. Haugeland J. Artificial intelligence: The very idea. Philosophical Review, 1985, 7: 3-11.
4. 夏黎明, 沈坚, 张荣国, 等. 深度学习技术在医学影像领域的应用. 协和医学杂志, 2018, 9(1): 5.
5. 杨文静, 吕章艳, 冯小双, 等. 人工智能在癌症研究领域的文献可视化分析. 肿瘤防治研究, 2021, 48(2): 7.
6. 成科扬, 王宁, 师文喜, 等. 深度学习可解释性研究进展. 计算机研究与发展, 2020, 57(6): 10.
7. Zhang H, Zhang H. LungSeek: 3D selective kernel residual network for pulmonary nodule diagnosis. Vis Comput, 2022, 27: 1-14.
8. Kasinathan G, Jayakumar S. Cloud-based lung tumor detection and stage classification using deep learning techniques. BioMed Res Int, 2022, 2022: 4185835.
9. Wang C, Xu X, Shao J, et al. Deep learning to predict EGFR mutation and PD-L1 expression status in non-small-cell lung cancer on computed tomography images. J Oncol, 2021, 2021: 5499385.
10. Noorbakhsh J, Farahmand S, Namburi S, et al. Deep learning-based cross-classifications reveal conserved spatial behaviors within tumor histological images. Nat Commun, 2020, 11(1): 1-14.
11. 国家卫生健康委员会. 食管癌诊疗规范(2018年版). 中华消化病与影像杂志(电子版), 2019, 9(4): 158-192.
12. Chen W, Zheng R, Baade PD, et al. Cancer statistics in China, 2015. CA, 2016, 66(2): 115-132.
13. Cai Z, Liu Q. Understanding the Global Cancer Statistics 2018: Implications for cancer control. Sci China Life Sci, 2021, 64(6): 1017-1020.
14. 冯祥, 宋统球, 钱东福, 等. 江苏省扬中市食管癌高危人群危险因素及其聚集性. 中华疾病控制杂志, 2021, 25(3): 6.
15. 武育卫, 彭贵勇, 龙庆林, 等. 早期食管癌内镜治疗与外科手术疗效对比研究. 中国肿瘤, 2009, 18(9): 3.
16. 赫捷, 邵康. 中国食管癌流行病学现状、诊疗现状及未来对策. 中国癌症杂志, 2011, 21(7): 4.
17. 刘元芬, 段青. 食管癌X线气钡双重造影与CT检查的对比分析. 中国医药科学, 2012, 2(5): 2.
18. Ishihara R, Takeuchi Y, Chatani R, et al. Prospective evaluation of narrow-band imaging endoscopy for screening of esophageal squamous mucosal high-grade neoplasia in experienced and less experienced endoscopists. Dis Esophagus, 2010, 23(6): 480-486.
19. Guo LJ, Xiao X, Wu CC, et al. Real-time automated diagnosis of precancerous lesions and early esophageal squamous cell carcinoma using a deep learning model (with videos). Gastrointest Endosc, 2020, 91(1): 41-51.
20. Gehrung M, Crispin-Ortuzar M, Berman A G, et al. Triage-driven diagnosis of Barrett’s esophagus for early detection of esophageal adenocarcinoma using deep learning. Nat Med, 2021, 27(5): 833-841.
21. Sato F, Shimada Y, Selaru FM, et al. Prediction of survival in patients with esophageal carcinoma using artificial neural networks. Cancer, 2005, 103(8): 1596-1605.
22. Mofidi R, Deans C, Duff MD, et al. Prediction of survival from carcinoma of oesophagus and oesophago-gastric junction following surgical resection using an artificial neural network. Eur J Surg Oncol, 2006, 32(5): 533-539.
23. Liu XL, Shao CY, Sun L, et al. An artificial neural network model predicting pathologic nodal metastases in clinical stage Ⅰ–Ⅱesophageal squamous cell carcinoma patients. J Thorac Dis, 2020, 12(10): 5580.
24. Moghtadaei M, Golpayegani MRH, Almasganj F, et al. Predicting the risk of squamous dysplasia and esophageal squamous cell carcinoma using minimum classification error method. Comput Bio Med, 2014, 45: 51-57.
25. Hayashida Y, Honda K, Osaka Y, et al. Possible prediction of chemoradiosensitivity of esophageal cancer by serum protein profiling. Clin Cancer Res, 2005, 11(22): 8042-8047.
26. Wang Y, Huang Y, Zhao Q, et al. Esophageal wall thickness on CT scans: Can it predict the T stage of primary thoracic esophageal squamous cell carcinoma? Esophagus, 2021: 1-9.
27. Yeh JCY, Yu WH, Yang CK, et al. Predicting aggressive histopathological features in esophageal cancer with positron emission tomography using a deep convolutional neural network. Ann Transl Med, 2021, 9(1): 37.
28. Mourikis TP, Benedetti L, Foxall E, et al. Patient-specific cancer genes contribute to recurrently perturbed pathways and establish therapeutic vulnerabilities in esophageal adenocarcinoma. Nat Commun, 2019, 10(1): 1-17.
29. Hackl H, Charoentong P, Finotello F, et al. Computational genomics tools for dissecting tumour–immune cell interactions. Nat Rev Genet, 2016, 17(8): 441-458.
30. Pratama R, Hwang JJ, Lee JH, et al. Authentication of differential gene expression in oral squamous cell carcinoma using machine learning applications. BMC Oral Health, 2021, 21(1): 1-8.
31. Li MX, Sun XM, Cheng WG, et al. Using a machine learning approach to identify key prognostic molecules for esophageal squamous cell carcinoma. BMC Cancer, 2021, 21(1): 1-11.
32. 王姗姗, 翟晓梅. 人工智能医学应用的伦理问题. 中国医学伦理学, 2019, 32(8): 972-976.
33. 周吉银, 刘丹, 曾圣雅. 人工智能在医疗领域中应用的挑战与对策. 中国医学伦理学, 2019, 32(3): 281-286.
34. Pearson J D, Bremner R. Simplifying cancer: Binary pan-cancer superclasses stratified by opposite YAP/TEAD effects. Mol Cell Oncol, 2021, 8(5): 1981111.
35. Doshi-Velez F, Kim B. Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv: 1702.08608, 2017.
36. 纪守领, 李进锋, 杜天宇, 等. 机器学习模型可解释性方法, 应用与安全研究综述. 计算机研究与发展, 2019, 56(10): 2071.
37. Rajpurkar P, Irvin J, Ball RL, et al. Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Med, 2018, 15(11): e1002686.
38. Chen Q, Kuai Y, Wang S, et al. Deep learning-based classification of epithelial-mesenchymal transition for predicting response to therapy in clear cell renal cell carcinoma. Front Oncol, 2021, 11: 782515.
39. 张珺倩, 张远, 尹勇, 等. 机器学习在肿瘤放射治疗领域应用进展. 生物医学工程学杂志, 2019, 36(5): 879-884.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Current situation and prospects of machine learning applications in the study of esophageal cancer

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