• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

Correspondence to editorial on "Conventional and machine learning-based risk scores for patients with early-stage hepatocellular carcinoma".

作者信息

Ho Chun-Ting, Tan Elise Chia-Hui, Su Chien-Wei

机构信息

Division of Gastroenterology and Hepatology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.

Department of Health Service Administration, College of Public Health, China Medical University, Taichung, Taiwan.

出版信息

Clin Mol Hepatol. 2024 Oct;30(4):1016-1018. doi: 10.3350/cmh.2024.0365. Epub 2024 May 20.

DOI:10.3350/cmh.2024.0365
PMID:38768962
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11540342/
Abstract
摘要

相似文献

1
Correspondence to editorial on "Conventional and machine learning-based risk scores for patients with early-stage hepatocellular carcinoma".致关于“早期肝细胞癌患者的传统风险评分和基于机器学习的风险评分”的社论的信函。
Clin Mol Hepatol. 2024 Oct;30(4):1016-1018. doi: 10.3350/cmh.2024.0365. Epub 2024 May 20.
2
Unlocking the future: Machine learning sheds light on prognostication for early-stage hepatocellular carcinoma: Editorial on "Conventional and machine learning-based risk scores for patients with early-stage hepatocellular carcinoma".开启未来：机器学习为早期肝细胞癌的预后预测带来曙光：关于“早期肝细胞癌患者的传统及基于机器学习的风险评分”的社论
Clin Mol Hepatol. 2024 Oct;30(4):698-701. doi: 10.3350/cmh.2024.0327. Epub 2024 May 7.
3
Conventional and machine learning-based risk scores for patients with early-stage hepatocellular carcinoma.基于传统机器学习和基于机器学习的早期肝细胞癌患者风险评分。
Clin Mol Hepatol. 2024 Jul;30(3):406-420. doi: 10.3350/cmh.2024.0103. Epub 2024 Apr 11.
4
Prognosis prediction and risk stratification of transarterial chemoembolization or intraarterial chemotherapy for unresectable hepatocellular carcinoma based on machine learning.基于机器学习的经动脉化疗栓塞术或动脉内化疗对不可切除肝细胞癌的预后预测及风险分层
Eur Radiol. 2024 Aug;34(8):5094-5107. doi: 10.1007/s00330-024-10581-2. Epub 2024 Jan 30.
5
Leveraging machine learning for early recurrence prediction in hepatocellular carcinoma: A step towards precision medicine.利用机器学习进行肝细胞癌早期复发预测：迈向精准医学的一步。
World J Gastroenterol. 2024 Feb 7;30(5):424-428. doi: 10.3748/wjg.v30.i5.424.
6
Automated graded prognostic assessment for patients with hepatocellular carcinoma using machine learning.利用机器学习为肝细胞癌患者进行自动化分级预后评估。
Eur Radiol. 2024 Oct;34(10):6940-6952. doi: 10.1007/s00330-024-10624-8. Epub 2024 Mar 27.
7
Development and validation of an ensemble machine-learning model for predicting early mortality among patients with bone metastases of hepatocellular carcinoma.用于预测肝细胞癌骨转移患者早期死亡率的集成机器学习模型的开发与验证
Front Oncol. 2023 Feb 20;13:1144039. doi: 10.3389/fonc.2023.1144039. eCollection 2023.
8
Editorial Comment: MRI-Based Machine Learning Model for Prediction of Recurrence in Early-Stage Hepatocellular Carcinoma.编辑评论：基于磁共振成像的机器学习模型用于预测早期肝细胞癌复发
AJR Am J Roentgenol. 2023 Feb;220(2):256. doi: 10.2214/AJR.22.28432. Epub 2022 Aug 31.
9
Research on Predicting the Occurrence of Hepatocellular Carcinoma Based on Notch Signal-Related Genes Using Machine Learning Algorithms.基于机器学习算法的 Notch 信号相关基因预测肝细胞癌发生的研究。
Turk J Gastroenterol. 2023 Jul;34(7):760-770. doi: 10.5152/tjg.2023.22357.
10
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.

引用本文的文献

1
Use of artificial intelligence for liver diseases: A survey from the EASL congress 2024.人工智能在肝脏疾病中的应用：来自2024年欧洲肝脏研究学会大会的一项调查。
JHEP Rep. 2024 Sep 6;6(12):101209. doi: 10.1016/j.jhepr.2024.101209. eCollection 2024 Dec.
2
Correspondence to letter to the editor 1 on "Conventional and machine learning-based risk scores for patients with early-stage hepatocellular carcinoma".致编辑的信1关于“早期肝细胞癌患者的传统和基于机器学习的风险评分”的通信
Clin Mol Hepatol. 2025 Jan;31(1):e96-e97. doi: 10.3350/cmh.2024.0999. Epub 2024 Nov 11.

本文引用的文献

1
Unlocking the future: Machine learning sheds light on prognostication for early-stage hepatocellular carcinoma: Editorial on "Conventional and machine learning-based risk scores for patients with early-stage hepatocellular carcinoma".开启未来：机器学习为早期肝细胞癌的预后预测带来曙光：关于“早期肝细胞癌患者的传统及基于机器学习的风险评分”的社论
Clin Mol Hepatol. 2024 Oct;30(4):698-701. doi: 10.3350/cmh.2024.0327. Epub 2024 May 7.
2
Conventional and machine learning-based risk scores for patients with early-stage hepatocellular carcinoma.基于传统机器学习和基于机器学习的早期肝细胞癌患者风险评分。
Clin Mol Hepatol. 2024 Jul;30(3):406-420. doi: 10.3350/cmh.2024.0103. Epub 2024 Apr 11.
3
Machine learning improves the prediction of significant fibrosis in Asian patients with metabolic dysfunction-associated steatotic liver disease - The Gut and Obesity in Asia (GO-ASIA) Study.机器学习提高了代谢功能障碍相关脂肪性肝病的亚洲患者发生显著纤维化的预测能力 - 亚洲肠道和肥胖研究（GO-ASIA）。
Aliment Pharmacol Ther. 2024 Mar;59(6):774-788. doi: 10.1111/apt.17891. Epub 2024 Feb 1.
4
Artificial intelligence predicts direct-acting antivirals failure among hepatitis C virus patients: A nationwide hepatitis C virus registry program.人工智能预测丙型肝炎病毒患者直接作用抗病毒药物失败：一项全国性丙型肝炎病毒登记计划。
Clin Mol Hepatol. 2024 Jan;30(1):64-79. doi: 10.3350/cmh.2023.0287. Epub 2023 Nov 21.
5
Machine-learning model comprising five clinical indices and liver stiffness measurement can accurately identify MASLD-related liver fibrosis.机器学习模型包含五个临床指标和肝脏硬度测量值，可以准确识别 MASLD 相关的肝纤维化。
Liver Int. 2024 Mar;44(3):749-759. doi: 10.1111/liv.15818. Epub 2023 Dec 22.
6
Decision Tree Model for Predicting the Overall Survival of Patients with Diffused Large B-Cell Lymphoma in the Central Nervous System.用于预测中枢神经系统弥漫性大 B 细胞淋巴瘤患者总生存率的决策树模型。
World Neurosurg. 2022 Oct;166:e189-e198. doi: 10.1016/j.wneu.2022.06.139. Epub 2022 Jul 5.
7
Machine learning to predict waitlist dropout among liver transplant candidates with hepatocellular carcinoma.机器学习预测肝癌肝移植候选者的候补名单脱落率。
Cancer Med. 2022 Mar;11(6):1535-1541. doi: 10.1002/cam4.4538. Epub 2022 Jan 14.
8
The MELD Score Is Superior to the Maddrey Discriminant Function Score to Predict Short-Term Mortality in Alcohol-Associated Hepatitis: A Global Study.MELD 评分优于 Maddrey 判别函数评分预测酒精性肝炎短期死亡率：全球研究。
Am J Gastroenterol. 2022 Feb 1;117(2):301-310. doi: 10.14309/ajg.0000000000001596.
9
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.
10
Application of machine learning models for predicting acute kidney injury following donation after cardiac death liver transplantation.机器学习模型在预测心脏死亡后肝移植供体急性肾损伤中的应用。
Hepatobiliary Pancreat Dis Int. 2021 Jun;20(3):222-231. doi: 10.1016/j.hbpd.2021.02.001. Epub 2021 Mar 5.