卷积神经网络在淋巴结超声诊断中的应用
Application of Convolutional Neural Network in Ultrasound Diagnosis of Lymph Nodes
摘要: 淋巴结是人类身体内重要的免疫器官,当细菌、毒素、肿瘤细胞等物质随着淋巴管进入淋巴结,淋巴结细胞数和体积增加,造成淋巴结肿大。淋巴结有助于阻止和清除这些细菌和毒素,所以异常的淋巴结通常表明该区域内发生了病变,从而根据淋巴结的分布对相关疾病进行诊断非常重要,特别是对于某些传染病的诊断。超声成像技术具备检查准确、实时、无辐射等优点,成为淋巴结疾病首选检查方式。在当代人工智能的大数据背景下,超声医学也进入人工智能的大时代。本文就人工智能在淋巴结超声诊断中的应用展开论述。
Abstract: Lymph node is an important immune organ in the human body. When bacteria, toxins, tumor cells, and other antigen substances enter the lymph node with the lymphatic vessels, the number and volume of lymph node cells increase, causing lymph node swelling. Lymph nodes help prevent and remove these bacteria and toxins. Therefore, abnormal lymph nodes usually indicate that lesions have occurred in the region, so it is very important to diagnose related diseases according to the distribution of lymph nodes, especially for some infectious diseases. Ultrasound imaging technology has the advantages of accurate, real-time, non radiation and so on, and has become the preferred examination method for lymph node diseases. Under the background of big data of contemporary AI, ultrasonic medicine has also entered the era of AI. This article discusses the application of artifi-cial intelligence in ultrasonic diagnosis of lymph nodes.
文章引用:伍杰, 任艳, 王晓荣. 卷积神经网络在淋巴结超声诊断中的应用[J]. 临床医学进展, 2023, 13(10): 16349-16354. https://doi.org/10.12677/ACM.2023.13102287

参考文献

[1] Wang, G., et al. (2019) Clinical Value of Ultrasonic Imaging in Diagnosis of Hypopharyngeal Cancer with Cervical Lymph Node Metastasis. Oncology Letters, 18, 5917-5922. [Google Scholar] [CrossRef] [PubMed]
[2] 王晓荣. 颈部肿大淋巴结恶性风险评估的超声分级诊断初步研究[D]: [博士学位论文]. 乌鲁木齐: 新疆医科大学, 2017.
[3] 刘霞, 薛晓光, 蒲文全, 等. 彩色多普勒超声在甲状腺癌颈部转移性淋巴结诊断中的价值[J]. 临床医学研究与实践, 2021, 6(17): 130-132.
[4] Zhou, H., Liu, B., Liu, Y., Huang, Q.N. and Yan, W. (2022) Ultrasonic In-telligent Diagnosis of Papillary Thyroid Carcinoma Based on Machine Learning. Journal of Healthcare Engineering, 2022, Article ID: 6428796. [Google Scholar] [CrossRef] [PubMed]
[5] Yamashita, R., Nishio, M., Gian Do, R.K. and Togashi, K. (2018) Convolutional Neural Networks: An Overview and Application in Radiology. Insights Imaging, 9, 611-629. [Google Scholar] [CrossRef] [PubMed]
[6] Gao, F. and Ye, Z.W. (2021) [A Brief History of Intelligent Med-icine]. Chinese Journal of Medical History, 51, 97-102.
[7] Hong, L., Cheng, X. and Zheng, D. (2021) Application of Artificial Intelligence in Emergency Nursing of Patients with Chronic Obstructive Pulmonary Disease. Contrast Media & Molecular Imaging, 2021, Article ID: 6423398. [Google Scholar] [CrossRef] [PubMed]
[8] Upton, R., et al. (2022) Automated Echocardiographic Detection of Se-vere Coronary Artery Disease Using Artificial Intelligence. JACC: Cardiovascular Imaging, 15, 715-727. [Google Scholar] [CrossRef] [PubMed]
[9] Zhao, C., et al. (2022) Intelligent Algorithm-Based Ultrasound Image for Evaluating the Effect of Comprehensive Nursing Scheme on Patients with Diabetic Kidney Disease. Computa-tional and Mathematical Methods in Medicine, 2022, Article ID: 6440138. [Google Scholar] [CrossRef] [PubMed]
[10] Xu, Y., et al. (2015) Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imaging. Clinical Cancer Research, 25, 3266-3275. [Google Scholar] [CrossRef
[11] Zhang, Y., et al. (2020) CNN-Based Survival Model for Pancreatic Ductal Adenocarcinoma in Medical Imaging. BMC Medical Imaging, 20, Article No. 11. [Google Scholar] [CrossRef] [PubMed]
[12] Zhang, Y., Gong, C.L., Zheng, L., Li, X.Y. and Yang, X.M. (2021) Deep Learning for Intelligent Recognition and Prediction of Endometrial Cancer. Journal of Healthcare Engi-neering, 2021, Article ID: 1148309. [Google Scholar] [CrossRef] [PubMed]
[13] Al-Saif, O., et al. (2010) Long-Term Efficacy of Lymph Node Reoper-ation for Persistent Papillary Thyroid Cancer. The Journal of Clinical Endocrinology & Metabolism, 95, 2187-2194. [Google Scholar] [CrossRef] [PubMed]
[14] Mazzaferri, E.L. and Kloos, R.T. (2001) Clinical Review 128: Current Approaches to Primary Therapy for Papillary and Follicular Thyroid Cancer. The Journal of Clinical Endocrinology & Metabolism, 86, 1447-1463. [Google Scholar] [CrossRef] [PubMed]
[15] Lim, Y.C., Choi, E.C., Yoon, Y.H., Kim, E.H. and Koo, B.S. (2009) Central Lymph Node Metastases in Unilateral Papillary Thyroid Microcarcinoma. British Journal of Surgery, 96, 253-257. [Google Scholar] [CrossRef] [PubMed]
[16] Wang, Y., Guan, Q. and Xiang, J. (2018) Nomogram for Predicting Central Lymph Node Metastasis in Papillary Thyroid Microcarcinoma: A Retrospective Cohort Study of 8668 Patients. International Journal of Surgery, 55, 98-102. [Google Scholar] [CrossRef] [PubMed]
[17] Chen, J., et al. (2018) Conventional Ultrasound, Immunohisto-chemical Factors and BRAF (V600E) Mutation in Predicting Central Cervical Lymph Node Metastasis of Papillary Thy-roid Carcinoma. Ultrasound in Medicine and Biology, 44, 2296-2306. [Google Scholar] [CrossRef] [PubMed]
[18] 李盈盈, 孙文轩, 廖献东, 等. 基于甲状腺超声图像建立甲状腺乳头状癌中央区淋巴结转移人工智能诊断模型[J]. 中国医学科学院学报, 2021, 43(6): 911-916.
[19] Cai, D., Lin, T., Jiang, K.L. and Sun, Z.Z. (2019) Diagnostic Value of MRI Combined with Ultrasound for Lymph Node Metastasis in Breast Cancer: Protocol for a Meta-Analysis. Medicine, 98, e16528. [Google Scholar] [CrossRef
[20] 陈文静, 许诺, 教召航, 等. 机器学习在乳腺癌荧光光谱诊断中的应用研究[J]. 光谱学与光谱分析, 2023, 43(8): 2407-2412.
[21] 郭晴, 张剑. HER2低表达乳腺癌的靶向治疗研究进展[J]. 中国癌症杂志, 2023, 33(2): 181-190.
[22] 王雅君, 赵燕南, 王碧芸. 抗HER2靶向药物治疗HER2阳性乳腺癌脑转移的研究进展[J]. 复旦学报(医学版), 2023, 50(1): 140-146.
[23] 胡雨舟, 李佳伟, 郭翌, 等. 浸润性乳腺癌超声高通量图像特征预测同侧腋窝淋巴结转移[J]. 肿瘤影像学, 2019, 28(2): 65-71.
[24] Toomey, A. and Lewis, C.R. (2023) Axillary Lymphadenectomy. StatPearls, Treasure Island.
[25] Abass, M.O., Gismalla, M.D.A., Alsheikh, A.A. and Elhassan, M.M.A. (2018) Axillary Lymph Node Dissection for Breast Cancer: Efficacy and Complication in Developing Countries. Journal of Global Oncology, 4, 1-8. [Google Scholar] [CrossRef
[26] Ibrahim, A., et al. (2020) Artificial Intelligence in Digital Breast Pa-thology: Techniques and Applications. Breast, 49, 267-273. [Google Scholar] [CrossRef] [PubMed]
[27] Zhou, L.Q., et al. (2020) Lymph Node Metastasis Prediction from Primary Breast Cancer US Images Using Deep Learning. Radiology, 294, 19-28. [Google Scholar] [CrossRef] [PubMed]
[28] 贺银付, 高德培. 非小细胞肺癌纵隔淋巴结转移的影像学评估现状[J]. 放射学实践, 2022, 37(1): 124-128.
[29] Wang, C., et al. (2004) [Clinical Analysis of the Characteristics of Thoracic Lymph Node Metastasis in Lung Cancer: A Report of 318 Cases]. Chinese Journal of Lung Cancer, 7, 438-441.
[30] 卢孔尧, 黄钢, 左艳. 非小细胞肺癌淋巴结转移预测模型研究[J]. 中国医学物理学杂志, 2022, 39(2): 182-187.
[31] Zhang, Y.K., et al. (2017) Association of Lymph Node Involvement with the Progno-sis of Pathological T1 Invasive Non-Small Cell Lung Cancer. World Journal of Surgical Oncology, 15, Article No. 64. [Google Scholar] [CrossRef] [PubMed]
[32] 王海星, 杨志清, 郭玲玲, 等. 基于大数据和人工智能的超声医学发展现状及问题研究[J]. 肿瘤影像学, 2020, 29(4): 410-413.
[33] 余绍德. 卷积神经网络和迁移学习在癌症影像分析中的研究[D]: [博士学位论文]. 北京: 中国科学院大学, 2018.