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人工智能辅助内镜下识别食管癌研究进展
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Abstract:
食管癌目前是我国最常见的癌症之一，食管癌及时的发现及治疗能明显延长患者生存期。随着计算机技术的日益进步，越来越多基于深度学习技术的计算机辅助诊断模型应用于消化系统疾病的研究当中。目前基于深度学习技术建立的模型已经部分应用于消化内镜下辅助诊断食管癌，并且获得较高诊断率，本文就目前的研究进展做出总结，并对其未来方向做出预测。
Esophageal cancer is currently one of the most common cancers in China, and timely detection and treatment of esophageal cancer can significantly prolong the survival of patients. With the advancement of computer technology, more and more computer-aided diagnostic models based on deep learning technology are applied to the study of digestive diseases. At present, the model based on deep learning technology has been partially applied to the auxiliary diagnosis of esophageal cancer under digestive endoscopy, and has obtained a high diagnosis rate. This article reviews the current research progress and looks forward to its future.
| [1] | 中华医学会肿瘤学分会早诊早治学组. 中国食管癌早诊早治专家共识[J]. 中华肿瘤杂志, 2022, 44(10): 1066-1075. |
| [2] | Lagergren, J. and Lagergren, P. (2010) Oesophageal Cancer. British Medical Journal, 341, c6280. https://doi.org/10.1136/bmj.c6280 |
| [3] | Chen, W., Zheng, R., Zhang, S., Zeng, H., Xia, C., Zuo, T., et al. (2017) Cancer Incidence and Mortality in China, 2013. Cancer Letters, 401, 63-71. https://doi.org/10.1016/j.canlet.2017.04.024 |
| [4] | Yoshii, S., Mabe, K., Watano, K., Ohno, M., Matsumoto, M., Ono, S., et al. (2019) Validity of Endoscopic Features for the Diagnosis of Helicobacter pylori Infection Status Based on the Kyoto Classification of Gastritis. Digestive Endoscopy, 32, 74-83. https://doi.org/10.1111/den.13486 |
| [5] | Jayatilake, S.M.D.A.C. and Ganegoda, G.U. (2021) Involvement of Machine Learning Tools in Healthcare Decision Making. Journal of Healthcare Engineering, 2021, 1-20. https://doi.org/10.1155/2021/6679512 |
| [6] | 张佳, 孙凯. 人工智能深度学习在心血管影像诊断中的研究进展[J]. 中国医学装备, 2020, 17(4): 183-186. |
| [7] | de Souza, L.A., Palm, C., Mendel, R., Hook, C., Ebigbo, A., Probst, A., et al. (2018) A Survey on Barrett’s Esophagus Analysis Using Machine Learning. Computers in Biology and Medicine, 96, 203-213. https://doi.org/10.1016/j.compbiomed.2018.03.014 |
| [8] | Horie, Y., Yoshio, T., Aoyama, K., Yoshimizu, S., Horiuchi, Y., Ishiyama, A., et al. (2019) Diagnostic Outcomes of Esophageal Cancer by Artificial Intelligence Using Convolutional Neural Networks. Gastrointestinal Endoscopy, 89, 25-32. https://doi.org/10.1016/j.gie.2018.07.037 |
| [9] | 鲍瀛, 李瑞瑶, 何明远, 等. 基于深度学习的图像识别技术在食管癌早期筛查中的应用研究[J]. 中国数字医学, 2021, 16(9): 90-93. |
| [10] | Li, B., Cai, S., Tan, W., Li, J., Yalikong, A., Feng, X., et al. (2021) Comparative Study on Artificial Intelligence Systems for Detecting Early Esophageal Squamous Cell Carcinoma between Narrow-Band and White-Light Imaging. World Journal of Gastroenterology, 27, 281-293. https://doi.org/10.3748/wjg.v27.i3.281 |
| [11] | Yang, X., Li, Z., Shao, X., Ji, R., Qu, J., Zheng, M., et al. (2021) Real-Time Artificial Intelligence for Endoscopic Diagnosis of Early Esophageal Squamous Cell Cancer (with Video). Digestive Endoscopy, 33, 1075-1084. https://doi.org/10.1111/den.13908 |
| [12] | Iwagami, H., Ishihara, R., Aoyama, K., Fukuda, H., Shimamoto, Y., Kono, M., et al. (2020) Artificial Intelligence for the Detection of Esophageal and Esophagogastric Junctional Adenocarcinoma. Journal of Gastroenterology and Hepatology, 36, 131-136. https://doi.org/10.1111/jgh.15136 |
| [13] | Hashimoto, R., Requa, J., Dao, T., Ninh, A., Tran, E., Mai, D., et al. (2020) Artificial Intelligence Using Convolutional Neural Networks for Real-Time Detection of Early Esophageal Neoplasia in Barrett’s Esophagus (with Video). Gastrointestinal Endoscopy, 91, 1264-1271.e1. https://doi.org/10.1016/j.gie.2019.12.049 |
| [14] | Yuan, X., Guo, L., Liu, W., Zeng, X., Mou, Y., Bai, S., et al. (2021) Artificial Intelligence for Detecting Superficial Esophageal Squamous Cell Carcinoma under Multiple Endoscopic Imaging Modalities: A Multicenter Study. Journal of Gastroenterology and Hepatology, 37, 169-178. https://doi.org/10.1111/jgh.15689 |
| [15] | Waki, K., Ishihara, R., Kato, Y., Shoji, A., Inoue, T., Matsueda, K., et al. (2021) Usefulness of an Artificial Intelligence System for the Detection of Esophageal Squamous Cell Carcinoma Evaluated with Videos Simulating Overlooking Situation. Digestive Endoscopy, 33, 1101-1109. https://doi.org/10.1111/den.13934 |
| [16] | 中华人民共和国国家卫生健康委员会医政医管局. 食管癌诊疗指南(2022年版) [J]. 中华消化外科杂志, 2022, 21(10): 1247-1268. |
| [17] | Tokai, Y., Yoshio, T., Aoyama, K., Horie, Y., Yoshimizu, S., Horiuchi, Y., et al. (2020) Application of Artificial Intelligence Using Convolutional Neural Networks in Determining the Invasion Depth of Esophageal Squamous Cell Carcinoma. Esophagus, 17, 250-256. https://doi.org/10.1007/s10388-020-00716-x |
| [18] | Nakagawa, K., Ishihara, R., Aoyama, K., Ohmori, M., Nakahira, H., Matsuura, N., et al. (2019) Classification for Invasion Depth of Esophageal Squamous Cell Carcinoma Using a Deep Neural Network Compared with Experienced Endoscopists. Gastrointestinal Endoscopy, 90, 407-414. https://doi.org/10.1016/j.gie.2019.04.245 |
| [19] | Shimamoto, Y., Ishihara, R., Kato, Y., Shoji, A., Inoue, T., Matsueda, K., et al. (2020) Real-Time Assessment of Video Images for Esophageal Squamous Cell Carcinoma Invasion Depth Using Artificial Intelligence. Journal of Gastroenterology, 55, 1037-1045. https://doi.org/10.1007/s00535-020-01716-5 |
| [20] | Liu, W., Yuan, X., Guo, L., Pan, F., Wu, C., Sun, Z., et al. (2022) Artificial Intelligence for Detecting and Delineating Margins of Early ESCC under WLI Endoscopy. Clinical and Translational Gastroenterology, 13, e00433. https://doi.org/10.14309/ctg.0000000000000433 |
| [21] | Yuan, X., Zeng, X., He, L., Ye, L., Liu, W., Hu, Y., et al. (2022) Artificial Intelligence for Detecting and Delineating a Small Flat-Type Early Esophageal Squamous Cell Carcinoma under Multimodal Imaging. Endoscopy, 55, E141-E142. https://doi.org/10.1055/a-1956-0569 |
| [22] | Oyama, T., Inoue, H., Arima, M., Momma, K., Omori, T., Ishihara, R., et al. (2016) Prediction of the Invasion Depth of Superficial Squamous Cell Carcinoma Based on Microvessel Morphology: Magnifying Endoscopic Classification of the Japan Esophageal Society. Esophagus, 14, 105-112. https://doi.org/10.1007/s10388-016-0527-7 |
| [23] | Zhao, Y., Xue, D., Wang, Y., Zhang, R., Sun, B., Cai, Y., et al. (2018) Computer-Assisted Diagnosis of Early Esophageal Squamous Cell Carcinoma Using Narrow-Band Imaging Magnifying Endoscopy. Endoscopy, 51, 333-341. https://doi.org/10.1055/a-0756-8754 |
| [24] | Everson, M., Herrera, L., Li, W., Luengo, I.M., Ahmad, O., Banks, M., et al. (2019) Artificial Intelligence for the Real-Time Classification of Intrapapillary Capillary Loop Patterns in the Endoscopic Diagnosis of Early Oesophageal Squamous Cell Carcinoma: A Proof-of-Concept Study. United European Gastroenterology Journal, 7, 297-306. https://doi.org/10.1177/2050640618821800 |
| [25] | Yuan, X., Liu, W., Liu, Y., Zeng, X., Mou, Y., Wu, C., et al. (2022) Artificial Intelligence for Diagnosing Microvessels of Precancerous Lesions and Superficial Esophageal Squamous Cell Carcinomas: A Multicenter Study. Surgical Endoscopy, 36, 8651-8662. https://doi.org/10.1007/s00464-022-09353-0 |
| [26] | 中国医院协会介入医学中心分会. 人工智能应用于食管癌临床诊疗的专家共识[J]. 中华介入放射学电子杂志, 2021, 9(3): 235-246. |
| [27] | Gao, Y., Xin, L., Feng, Y., Yao, B., Lin, H., Sun, C., et al. (2021) Feasibility and Accuracy of Artificial Intelligence-Assisted Sponge Cytology for Community-Based Esophageal Squamous Cell Carcinoma Screening in China. American Journal of Gastroenterology, 116, 2207-2215. https://doi.org/10.14309/ajg.0000000000001499 |
| [28] | Gao, Y., Xin, L., Lin, H., Yao, B., Zhang, T., Zhou, A., et al. (2023) Machine Learning-Based Automated Sponge Cytology for Screening of Oesophageal Squamous Cell Carcinoma and Adenocarcinoma of the Oesophagogastric Junction: A Nationwide, Multicohort, Prospective Study. The Lancet Gastroenterology & Hepatology, 8, 432-445. https://doi.org/10.1016/s2468-1253(23)00004-3 |
| [29] | 沈雨雯, 石逸秋, 解添淞, 等. 人工智能在胰腺癌影像诊断和评价中的研究进展[J]. 临床放射学杂志, 2023, 42(9): 1527-1530. |
| [30] | Chen, H., Zhou, X., Tang, X., Li, S. and Zhang, G. (2020) Prediction of Lymph Node Metastasis in Superficial Esophageal Cancer Using a Pattern Recognition Neural Network. Cancer Management and Research, 12, 12249-12258. https://doi.org/10.2147/cmar.s270316 |
