肝细胞癌中机器学习的现状与分析
Current Status and Analysis of Machine Learning in Hepatocellular Carcinoma.
作者信息
Feng Sijia, Wang Jianhua, Wang Liheng, Qiu Qixuan, Chen Dongdong, Su Huo, Li Xiaoli, Xiao Yao, Lin Chiayen
机构信息
General Surgery, Central South University Xiangya Hospital, Changsha, Hunan, China.
出版信息
J Clin Transl Hepatol. 2023 Oct 28;11(5):1184-1191. doi: 10.14218/JCTH.2022.00077S. Epub 2023 May 17.
Abstract
Hepatocellular carcinoma (HCC) is a common tumor. Although the diagnosis and treatment of HCC have made great progress, the overall prognosis remains poor. As the core component of artificial intelligence, machine learning (ML) has developed rapidly in the past decade. In particular, ML has become widely used in the medical field, and it has helped in the diagnosis and treatment of cancer. Different algorithms of ML have different roles in diagnosis, treatment, and prognosis. This article reviews recent research, explains the application of different ML models in HCC, and provides suggestions for follow-up research.
摘要
肝细胞癌（HCC）是一种常见的肿瘤。尽管HCC的诊断和治疗已取得了巨大进展，但总体预后仍然很差。作为人工智能的核心组成部分，机器学习（ML）在过去十年中发展迅速。特别是，ML已在医学领域得到广泛应用，并有助于癌症的诊断和治疗。ML的不同算法在诊断、治疗和预后方面具有不同的作用。本文综述了近期的研究，解释了不同ML模型在HCC中的应用，并为后续研究提供了建议。
相似文献
1
Current Status and Analysis of Machine Learning in Hepatocellular Carcinoma.肝细胞癌中机器学习的现状与分析
J Clin Transl Hepatol. 2023 Oct 28;11(5):1184-1191. doi: 10.14218/JCTH.2022.00077S. Epub 2023 May 17.
2
Deep learning in hepatocellular carcinoma: Current status and future perspectives.肝细胞癌中的深度学习：现状与未来展望。
World J Hepatol. 2021 Dec 27;13(12):2039-2051. doi: 10.4254/wjh.v13.i12.2039.
3
Prognostic role of artificial intelligence among patients with hepatocellular cancer: A systematic review.人工智能在肝细胞癌患者中的预后作用：一项系统综述。
World J Gastroenterol. 2020 Nov 14;26(42):6679-6688. doi: 10.3748/wjg.v26.i42.6679.
4
Artificial intelligence in the diagnosis and management of hepatocellular carcinoma.人工智能在肝细胞癌的诊断和管理中的应用。
J Gastroenterol Hepatol. 2021 Mar;36(3):551-560. doi: 10.1111/jgh.15413.
5
Personalized surveillance for hepatocellular carcinoma in cirrhosis - using machine learning adapted to HCV status.基于机器学习和 HCV 状态调整的肝硬化肝细胞癌个体化监测
J Hepatol. 2020 Dec;73(6):1434-1445. doi: 10.1016/j.jhep.2020.05.052. Epub 2020 Jun 29.
6
Application of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma: A review.人工智能在肝细胞癌诊疗中的应用：综述
World J Gastroenterol. 2020 Oct 7;26(37):5617-5628. doi: 10.3748/wjg.v26.i37.5617.
7
Artificial intelligence: A review of current applications in hepatocellular carcinoma imaging.人工智能：肝细胞癌成像当前应用综述
Diagn Interv Imaging. 2023 Jan;104(1):24-36. doi: 10.1016/j.diii.2022.10.001. Epub 2022 Oct 19.
8
Machine learning-based survival rate prediction of Korean hepatocellular carcinoma patients using multi-center data.基于机器学习的多中心韩国肝细胞癌患者生存率预测。
BMC Gastroenterol. 2022 Feb 27;22(1):85. doi: 10.1186/s12876-022-02182-4.
9
Artificial intelligence for the prevention and clinical management of hepatocellular carcinoma.人工智能在肝细胞癌的预防和临床管理中的应用。
J Hepatol. 2022 Jun;76(6):1348-1361. doi: 10.1016/j.jhep.2022.01.014.
10
Application of artificial intelligence in preoperative imaging of hepatocellular carcinoma: Current status and future perspectives.人工智能在肝细胞癌术前影像学中的应用：现状与展望。
World J Gastroenterol. 2021 Aug 28;27(32):5341-5350. doi: 10.3748/wjg.v27.i32.5341.
引用本文的文献
1
Current and new strategies for hepatocellular carcinoma surveillance.肝细胞癌监测的当前及新策略
Gastroenterol Rep (Oxf). 2025 Jun 6;13:goaf045. doi: 10.1093/gastro/goaf045. eCollection 2025.
2
Machine Learning Approach and Bioinformatics Analysis Discovered Key Genomic Signatures for Hepatitis B Virus-Associated Hepatocyte Remodeling and Hepatocellular Carcinoma.机器学习方法与生物信息学分析发现了乙型肝炎病毒相关肝细胞重塑和肝细胞癌的关键基因组特征。
Cancer Inform. 2025 Apr 16;24:11769351251333847. doi: 10.1177/11769351251333847. eCollection 2025.
3
Multiple machine learning algorithms identified SLC6A8 as a diagnostic biomarker of the late stage of Hepatocellular carcinoma.多种机器学习算法将SLC6A8鉴定为肝细胞癌晚期的诊断生物标志物。
Discov Oncol. 2025 Apr 16;16(1):543. doi: 10.1007/s12672-025-02351-3.
4
Construction and Validation of a T Cell Exhaustion-Related Prognostic Signature in Cholangiocarcinoma.胆管癌中T细胞耗竭相关预后标志物的构建与验证
Int J Genomics. 2025 Mar 10;2025:8823837. doi: 10.1155/ijog/8823837. eCollection 2025.
5
Research progress of artificial intelligence and machine learning in pulmonary embolism.人工智能与机器学习在肺栓塞中的研究进展
Front Med (Lausanne). 2025 Mar 27;12:1577559. doi: 10.3389/fmed.2025.1577559. eCollection 2025.
6
Plasma lipidomic analysis reveals disruption of ether phosphatidylcholine biosynthesis and facilitates early detection of hepatitis B-related hepatocellular carcinoma.血浆脂质组学分析揭示醚磷脂酰胆碱生物合成的破坏并有助于早期检测乙型肝炎相关肝细胞癌。
Lipids Health Dis. 2025 Feb 24;24(1):69. doi: 10.1186/s12944-025-02475-z.
7
Correlation between the white blood cell/platelet ratio and 28-day all-cause mortality in cardiac arrest patients: a retrospective cohort study based on machine learning.心脏骤停患者白细胞/血小板比值与28天全因死亡率的相关性：一项基于机器学习的回顾性队列研究
Front Pharmacol. 2025 Jan 7;15:1527664. doi: 10.3389/fphar.2024.1527664. eCollection 2024.
8
Predicting the risk of pulmonary embolism in patients with tuberculosis using machine learning algorithms.使用机器学习算法预测肺结核患者发生肺栓塞的风险。
Eur J Med Res. 2024 Dec 23;29(1):618. doi: 10.1186/s40001-024-02218-3.
9
Global trends in machine learning applied to clinical research in liver cancer: Bibliometric and visualization analysis (2001-2024).应用于肝癌临床研究的机器学习全球趋势：文献计量与可视化分析（2001 - 2024年）
Medicine (Baltimore). 2024 Dec 6;103(49):e40790. doi: 10.1097/MD.0000000000040790.
10
Predicting waitlist dropout in hepatocellular carcinoma: a narrative review.预测肝细胞癌患者在等待名单上的退出情况：一项叙述性综述。
Transl Gastroenterol Hepatol. 2024 Aug 21;9:72. doi: 10.21037/tgh-24-24. eCollection 2024.
本文引用的文献
1
Novel machine learning models outperform risk scores in predicting hepatocellular carcinoma in patients with chronic viral hepatitis.新型机器学习模型在预测慢性病毒性肝炎患者的肝细胞癌方面优于风险评分。
JHEP Rep. 2022 Jan 22;4(3):100441. doi: 10.1016/j.jhepr.2022.100441. eCollection 2022 Mar.
2
Hepatocellular Carcinoma Detection Using Machine Learning Techniques.使用机器学习技术检测肝细胞癌。
Adv Exp Med Biol. 2021;1338:21-29. doi: 10.1007/978-3-030-78775-2_4.
3
Prediction of Early Treatment Response to Initial Conventional Transarterial Chemoembolization Therapy for Hepatocellular Carcinoma by Machine-Learning Model Based on Computed Tomography.基于计算机断层扫描的机器学习模型对肝细胞癌初始常规经动脉化疗栓塞治疗早期疗效的预测
J Hepatocell Carcinoma. 2021 Nov 30;8:1473-1484. doi: 10.2147/JHC.S334674. eCollection 2021.
4
Clinical Value of Machine Learning-Based Ultrasomics in Preoperative Differentiation Between Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma: A Multicenter Study.基于机器学习的超声组学在肝细胞癌与肝内胆管癌术前鉴别诊断中的临床价值：一项多中心研究
Front Oncol. 2021 Nov 5;11:749137. doi: 10.3389/fonc.2021.749137. eCollection 2021.
5
An artificial intelligence model to predict hepatocellular carcinoma risk in Korean and Caucasian patients with chronic hepatitis B.一种用于预测韩国和高加索慢性乙型肝炎患者肝细胞癌风险的人工智能模型。
J Hepatol. 2022 Feb;76(2):311-318. doi: 10.1016/j.jhep.2021.09.025. Epub 2021 Oct 2.
6
Machine Learning to Improve Prognosis Prediction of Early Hepatocellular Carcinoma After Surgical Resection.机器学习改善手术切除后早期肝细胞癌的预后预测
J Hepatocell Carcinoma. 2021 Aug 10;8:913-923. doi: 10.2147/JHC.S320172. eCollection 2021.
7
Machine Learning-Based Ultrasomics Improves the Diagnostic Performance in Differentiating Focal Nodular Hyperplasia and Atypical Hepatocellular Carcinoma.基于机器学习的超声组学提高了鉴别局灶性结节性增生和非典型肝细胞癌的诊断性能。
Front Oncol. 2021 Mar 26;11:544979. doi: 10.3389/fonc.2021.544979. eCollection 2021.
8
Artificial Intelligence in Cancer Research and Precision Medicine.人工智能在癌症研究和精准医学中的应用。
Cancer Discov. 2021 Apr;11(4):900-915. doi: 10.1158/2159-8290.CD-21-0090.
9
Machine Learning for Building Immune Genetic Model in Hepatocellular Carcinoma Patients.用于构建肝细胞癌患者免疫遗传模型的机器学习
J Oncol. 2021 Mar 17;2021:6676537. doi: 10.1155/2021/6676537. eCollection 2021.
10
A Machine Learning-Based Model to Predict Survival After Transarterial Chemoembolization for BCLC Stage B Hepatocellular Carcinoma.一种基于机器学习的模型，用于预测BCLC B期肝细胞癌经动脉化疗栓塞后的生存率。
Front Oncol. 2021 Mar 2;11:608260. doi: 10.3389/fonc.2021.608260. eCollection 2021.
Zotero 插件