基于人工智能的临床决策支持系统在肝细胞癌中的应用
Application of Clinical Decision Support Sys-tem Based on Artificial Intelligence in Hepatocellular Carcinoma
DOI: 10.12677/ACM.2023.1371589, PDF,   
作者: 汪笑楠:青海大学临床医学院,青海 西宁;王海久*:青海大学附属医院普通外科学二科,青海 西宁
关键词: 人工智能(AI)临床决策支持系统(CDSS)肝细胞癌(HCC)Artificial Intelligence (AI) Clinical Decision Support System (CDSS) Hepatocellular Carcinoma (HCC)
摘要: 肝癌是世界范围内一种较为常见的恶性肿瘤,随着科技的飞速发展,目前在肝癌的早期诊断和治疗方面,基于人工智能的临床决策支持系统提供了新的解决方案。它能实时收集临床数据和医学知识,提供更准确的诊断和治疗建议。本文对近年来基于人工智能的临床决策系统进行了综述,包括肝癌的预测、诊断、个性化诊疗方案和预后监测等方面。同时,也讨论了该领域面临的挑战和未来的发展方向,总的来说,基于人工智能的临床决策支持系统对肝癌的早期诊断和治疗具有重要的应用价值。
Abstract: Liver cancer is a relatively common malignant tumor worldwide. With the rapid development of technology, artificial intelligence-based clinical decision support systems now provide new solutions in the early diagnosis and treatment of liver cancer. It can collect clinical data and medical knowledge in real time to provide more accurate diagnosis and treatment recommendations. This paper provides an overview of AI-based clinical decision systems in recent years, including the pre-diction, diagnosis, personalized treatment plan and prognosis monitoring of liver cancer. The chal-lenges and future directions in this field are also discussed. In general, AI-based clinical decision support systems have important application value for early diagnosis and treatment of liver cancer.
文章引用:汪笑楠, 王海久. 基于人工智能的临床决策支持系统在肝细胞癌中的应用[J]. 临床医学进展, 2023, 13(7): 11368-11374. https://doi.org/10.12677/ACM.2023.1371589

参考文献

[1] 国家卫生健康委办公厅. 原发性肝癌诊疗指南(2022年版) [J]. 临床肝胆病杂志, 2022, 38(2): 288.
[2] Rumgay, H., Arnold, M., Ferlay, J., et al. (2022) Global Burden of Primary Liver Cancer in 2020 and Predictions to 2040. Journal of Hepatology, 77, 1598-1606. [Google Scholar] [CrossRef] [PubMed]
[3] Oh, J.H. and Jun, D.W. (2023) The Latest Global Burden of Liver Cancer: A Past and Present Threat. Clinical and Molecular Hepatology, 29, 355-357. [Google Scholar] [CrossRef] [PubMed]
[4] Chen, W., Zheng, R., Baade, P.D., et al. (2016) Cancer Statistics in China, 2015. CA: A Cancer Journal for Clinicians, 66, 115-132. [Google Scholar] [CrossRef] [PubMed]
[5] 杨帆, 曹毛毛, 李贺, 等. 1990-2019年中国人群肝癌流行病学趋势分析及预测[J]. 中华消化外科杂志, 2022, 21(1): 106-113.
[6] Chen, W., Xia, C., Zheng, R., et al. (2019) Disparities by Province, Age, and Sex in Site-Specific Cancer Burden Attributable to 23 Potentially Modifiable Risk Factors in China: A Comparative Risk Assessment. The Lancet Global Health, 7, e257-e269. [Google Scholar] [CrossRef
[7] Muhiyaddin, R., Abd-Alrazaq, A.A., Househ, M., et al. (2020) The Impact of Clinical Decision Support Systems (CDSS) on Physicians: A Scoping Review. Studies in Health Technology and Informatics, 272, 470-473.
[8] Jimenez Perez, M. and Grande, R.G. (2020) Application of Artificial Intelligence in the Diagnosis and Treatment of Hepatocellular Carcinoma: A Review. World Journal of Gastroenterology, 26, 5617-5628. [Google Scholar] [CrossRef] [PubMed]
[9] 徐帆, 李红霞, 舒婷. 临床决策支持系统应用情况调查分析[J]. 中国卫生信息管理杂志, 2022, 19(6): 939-943.
[10] Calderaro, J., Seraphin, T.P., Luedde, T., et al. (2022) Artificial Intelligence for the Prevention and Clinical Management of Hepatocellular Carcinoma. Journal of Hepatology, 76, 1348-1361. [Google Scholar] [CrossRef] [PubMed]
[11] (2020) The Global, Regional, and National Burden of Cirrhosis by Cause in 195 Countries and Territories, 1990-2017: A Systematic Analysis for the Global Burden of Dis-ease Study 2017. The Lancet Gastroenterology & Hepatology, 5, 245-266.
[12] Terrault, N.A., Lok, A.S.F., Mcmahon, B.J., et al. (2018) Update on Prevention, Diagnosis, and Treatment of Chronic Hepatitis B: AASLD 2018 Hepatitis B Guidance. Hepatology (Baltimore, Md), 67, 1560-1599. [Google Scholar] [CrossRef] [PubMed]
[13] Ioannou, G.N., Tang, W., Beste, L.A., et al. (2020) Assessment of a Deep Learning Model to Predict Hepatocellular Carcinoma in Patients with Hepatitis C Cirrhosis. JAMA Network Open, 3, e2015626. [Google Scholar] [CrossRef] [PubMed]
[14] An, C., Choi, J.W., Lee, H.S., et al. (2021) Prediction of the Risk of Developing Hepatocellular Carcinoma in Health Screening Examinees: A Korean Cohort Study. BMC Cancer, 21, Article No. 755. [Google Scholar] [CrossRef] [PubMed]
[15] Fujiwara, N., Friedman, S.L., Goossens, N., et al. (2018) Risk Factors and Prevention of Hepatocellular Carcinoma in the Era of Precision Medicine. Journal of Hepatology, 68, 526-549. [Google Scholar] [CrossRef] [PubMed]
[16] Zhang, B.H., Yang, B.H. and Tang, Z.Y. (2004) Ran-domized Controlled Trial of Screening for Hepatocellular Carcinoma. Journal of Cancer Research and Clinical Oncology, 130, 417-422. [Google Scholar] [CrossRef] [PubMed]
[17] Schmauch, B., Herent, P., Jehanno, P., et al. (2019) Diagnosis of Focal Liver Lesions from Ultrasound Using Deep Learning. Diagnostic and Interventional Imaging, 100, 227-233. [Google Scholar] [CrossRef] [PubMed]
[18] Guo, J., Seo, Y., Ren, S., et al. (2016) Diagnostic Perfor-mance of Contrast-Enhanced Multidetector Computed Tomography and Gadoxetic Acid Disodium-Enhanced Magnetic Resonance Imaging in Detecting Hepatocellular Carcinoma: Direct Comparison and a Meta-Analysis. Abdominal Radi-ology (New York), 41, 1960-1972. [Google Scholar] [CrossRef] [PubMed]
[19] Mokrane, F.Z., Lu, L., Vavasseur, A., et al. (2020) Radiomics Machine-Learning Signature for Diagnosis of Hepatocellular Carcinoma in Cirrhotic Patients with Indeterminate Liver Nodules. European Radiology, 30, 558-570. [Google Scholar] [CrossRef] [PubMed]
[20] Yasaka, K., Akai, H., Abe, O., et al. (2018) Deep Learning with Convolutional Neural Network for Differentiation of Liver Masses at Dynamic Contrast-Enhanced CT: A Preliminary Study. Radiology, 286, 887-896. [Google Scholar] [CrossRef] [PubMed]
[21] Zhen, S.H., Cheng, M., Tao, Y.B., et al. (2020) Deep Learning for Accurate Diagnosis of Liver Tumor Based on Magnetic Resonance Imaging and Clinical Data. Frontiers in Oncology, 10, Article No. 680. [Google Scholar] [CrossRef] [PubMed]
[22] Liao, H., Long, Y., Han, R., et al. (2020) Deep Learning-Based Classification and Mutation Prediction from Histopathological Images of Hepatocellular Carcinoma. Clinical and Trans-lational Medicine, 10, e102. [Google Scholar] [CrossRef] [PubMed]
[23] Kiani, A., Uyumazturk, B., Rajpurkar, P., et al. (2020) Impact of a Deep Learning Assistant on the Histopathologic Classification of Liver Cancer. NPJ Digital Medicine, 3, 23. [Google Scholar] [CrossRef] [PubMed]
[24] Saillard, C., Schmauch, B., Laifa, O., et al. (2020) Predicting Sur-vival after Hepatocellular Carcinoma Resection Using Deep Learning on Histological Slides. Hepatology (Baltimore, MD), 72, 2000-2013. [Google Scholar] [CrossRef] [PubMed]
[25] Nam, J.Y., Lee, J.H., Bae, J., et al. (2020) Novel Model to Predict HCC Recurrence after Liver Transplantation Obtained Using Deep Learning: A Multicenter Study. Cancers, 12, Article No. 2791. [Google Scholar] [CrossRef] [PubMed]
[26] Ji, G.W., Zhu, F.P., Xu, Q., et al. (2019) Machine-Learning Analy-sis of Contrast-Enhanced CT Radiomics Predicts Recurrence of Hepatocellular Carcinoma after Resection: A Mul-ti-Institutional Study. EBioMedicine, 50, 156-165. [Google Scholar] [CrossRef] [PubMed]
[27] Oezdemir, I., Wessner, C.E., Shaw, C., et al. (2020) Tumor Vascular Networks Depicted in Contrast-Enhanced Ultrasound Images as a Predictor for Transarterial Chemoemboliza-tion Treatment Response. Ultrasound in Medicine & Biology, 46, 2276-2286. [Google Scholar] [CrossRef] [PubMed]
[28] 代涛. 卫生决策支持系统发展的国际经验[J]. 中国循证医学杂志, 2012, 12(3): 247-250.
[29] Moore, M. and Loper, K. (2011) An Introduction to Clinical Decision Support Systems. Journal of Electronic Resources in Medical Libraries, 8, 348-366. [Google Scholar] [CrossRef
[30] Miller, R.A. and Masarie, F.E. (1989) Use of the Quick Med-ical Reference (QMR) Program as a Tool for Medical Education. Methods of Information in Medicine, 28, 340-345. [Google Scholar] [CrossRef
[31] Ratner, M. (2015) IBM’s Watson Group Signs up Genomics Part-ners. Nature Biotechnology, 33, 10-11. [Google Scholar] [CrossRef] [PubMed]
[32] (2015) Oncologists Partner with Watson on Genomics. Cancer Discovery, 5, Article No. 788. [Google Scholar] [CrossRef
[33] Holt, M.E., Mittendorf, K.F., Lenoue-Newton, M., et al. (2021) My Cancer Genome: Coevolution of Precision Oncology and a Molecular Oncology Knowledgebase. JCO Clini-cal Cancer Informatics, 5, 995-1004. [Google Scholar] [CrossRef
[34] Carney, P.H. (2014) Information Technology and Precision Medicine. Seminars in Oncology Nursing, 30, 124-129. [Google Scholar] [CrossRef] [PubMed]
[35] 李军莲, 陈颖, 邓盼盼, 等. 国外基于人工智能的临床决策支持系统发展及启示[J]. 医学信息学杂志, 2018, 39(6): 2-6.
[36] 衡反修. 临床决策支持系统的既往和将来[J]. 科技新时代, 2018(4): 21.
[37] 中共中央 国务院印发《“健康中国2030”规划纲要》[J]. 中华人民共和国国务院公报, 2016(32): 5-20.
[38] 国务院办公厅关于促进“互联网+医疗健康”发展的意见[J]. 中华人民共和国国务院公报, 2018(14): 9-13.
[39] Yang, Q., Wei, J., Hao, X., et al. (2020) Improving B-Mode Ultrasound Diagnostic Performance for Focal Liver Lesions Using Deep Learning: A Multicentre Study. EBioMedicine, 56, Article ID: 102777. [Google Scholar] [CrossRef] [PubMed]
[40] Wang, R., He, Y., Yao, C., et al. (2020) Classification and Seg-mentation of Hyperspectral Data of Hepatocellular Carcinoma Samples Using 1-D Convolutional Neural Network. Cy-tometry Part A: The Journal of the International Society for Analytical Cytology, 97, 31-38. [Google Scholar] [CrossRef] [PubMed]
[41] Choi, G.H., Yun, J., Choi, J., et al. (2020) Development of Machine Learning-Based Clinical Decision Support System for Hepatocellular Carcinoma. Scientific Reports, 10, Article No. 14855. [Google Scholar] [CrossRef] [PubMed]
[42] Yang, J., Guo, F., Lyu, T., et al. (2020) Research of Arti-ficial Intelligence-Based Clinical Decision Support System for Primary Hepatocellular Carcinoma. Chinese Medical Journal, 100, 3870-3873.
[43] Zhou, N., Zhang, C.T., Lv, H.Y., et al. (2019) Concordance Study between IBM Watson for Oncology and Clinical Practice for Patients with Cancer in China. The Oncologist, 24, 812-819. [Google Scholar] [CrossRef] [PubMed]
[44] Zhang, W., Qi, S., Zhuo, J., et al. (2020) Concordance Study in Hepatectomy Recommendations between Watson for Oncology and Clinical Practice for Patients with Hepatocellular Carcinoma in China. World Journal of Surgery, 44, 1945-1953. [Google Scholar] [CrossRef] [PubMed]
[45] Chiang, S.J. and Daniel, B.H. (2010) Clinical Decision Support Systems: An Effective Pathway to Reduce Medical Errors and Improve Patient Safety. IntechOpen, Rijeka.
[46] 王帅. 浅谈目前我国医疗事故处理的现状及建议[C]//中国法医学会法医临床学专业委员会. 法医临床学专业理论与实践——中国法医学会∙全国第十九届法医临床学学术研讨会论文集. 哈尔滨: 黑龙江科学技术出版社, 2016: 1.