[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. https://doi.org/10.1016/j.jhep.2022.08.021
|
[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. https://doi.org/10.3350/cmh.2023.0070
|
[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. https://doi.org/10.3322/caac.21338
|
[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. https://doi.org/10.1016/S2214-109X(18)30488-1
|
[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.
https://doi.org/10.3748/wjg.v26.i37.5617
|
[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. https://doi.org/10.1016/j.jhep.2022.01.014
|
[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.
https://doi.org/10.1002/hep.29800
|
[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.
https://doi.org/10.1001/jamanetworkopen.2020.15626
|
[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.
https://doi.org/10.1186/s12885-021-08498-w
|
[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. https://doi.org/10.1016/j.jhep.2017.09.016
|
[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. https://doi.org/10.1007/s00432-004-0552-0
|
[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. https://doi.org/10.1016/j.diii.2019.02.009
|
[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.
https://doi.org/10.1007/s00261-016-0807-7
|
[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.
https://doi.org/10.1007/s00330-019-06347-w
|
[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.
https://doi.org/10.1148/radiol.2017170706
|
[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.
https://doi.org/10.3389/fonc.2020.00680
|
[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.
https://doi.org/10.1002/ctm2.102
|
[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. https://doi.org/10.1038/s41746-020-0232-8
|
[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. https://doi.org/10.1002/hep.31207
|
[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.
https://doi.org/10.3390/cancers12102791
|
[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.
https://doi.org/10.1016/j.ebiom.2019.10.057
|
[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. https://doi.org/10.1016/j.ultrasmedbio.2020.05.010
|
[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. https://doi.org/10.1080/15424065.2011.626345
|
[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. https://doi.org/10.1055/s-0038-1636814
|
[31]
|
Ratner, M. (2015) IBM’s Watson Group Signs up Genomics Part-ners. Nature Biotechnology, 33, 10-11.
https://doi.org/10.1038/nbt0115-10
|
[32]
|
(2015) Oncologists Partner with Watson on Genomics. Cancer Discovery, 5, Article No. 788.
https://doi.org/10.1158/2159-8290.CD-NB2015-090
|
[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.
https://doi.org/10.1200/CCI.21.00084
|
[34]
|
Carney, P.H. (2014) Information Technology and Precision Medicine. Seminars in Oncology Nursing, 30, 124-129.
https://doi.org/10.1016/j.soncn.2014.03.006
|
[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.
https://doi.org/10.1016/j.ebiom.2020.102777
|
[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. https://doi.org/10.1002/cyto.a.23871
|
[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. https://doi.org/10.1038/s41598-020-71796-z
|
[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.
https://doi.org/10.1634/theoncologist.2018-0255
|
[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. https://doi.org/10.1007/s00268-020-05401-9
|
[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.
|