Machine learning prediction model for post- hepatectomy liver failure in hepatocellular carcinoma: A multicenter study
- 1Xingtai Key Laboratory of Precision Medicine for Liver Cirrhosis and Portal Hypertension, Xingtai People’s Hospital, Xingtai, Hebei, China
- 2Center of Portal Hypertension, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, Jiangsu, China
- 3Artificial Intelligence Unit, Department of Medical Equipment Management, Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
- 4School of Information and Control Engineering, China University of Mining and Technology Xuzhou, Jiangsu, China
- 5Department of Hepatobiliary Surgery, Tongji Hospital Affiliated to Huazhong University of Science and Technology, Wuhan, Hubei, China
- 6Department of Hepatobiliary Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
- 7Department of Hepatobiliary Surgery, Fifth Medical Center of PLA General Hospital, Beijing, China
- 8Institute of Portal Hypertension, The First Hospital of Lanzhou University, Lanzhou, China
- 9Department of Microbiology and Infectious Disease Center, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China
- 10Department of Hepatobiliary Surgery, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
A Corrigendum on
Machine learning prediction model for post- hepatectomy liver failure in hepatocellular carcinoma: a multicenter study
by Wang J, Zheng T, Liao Y, Geng S, Li J, Zhang Z, Shang D, Liu C, Yu P, Huang Y, Liu C, Liu Y, Liu S, Wang M, Liu D, Miao H, Li S, Zhang B, Huang A, Zhang Y, Qi X and Chen S (2022) Front. Oncol. 12:986867. doi: 10.3389/fonc.2022.986867.
In the published article, there were two errors regarding the affiliations for Yifei Huang and Yanna Liu. The affiliation for Yifei Huang was displayed as “Center of Portal Hypertension, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, Jiangsu, China” and for Yanna Liu was displayed as “Center of Portal Hypertension, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, Jiangsu, China”. The correct affiliation for Yifei Huang is “Institute of Portal Hypertension, The First Hospital of Lanzhou University, Lanzhou, China” and for Yanna Liu is “Department of Microbiology and Infectious Disease Center, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China”.
The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.
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Keywords: hepatocellular carcinoma, liver resection, post-hepatectomy liver failure, artificial intelligence, machine learning
Citation: Wang J, Zheng T, Liao Y, Geng S, Li J, Zhang Z, Shang D, Liu C, Yu P, Huang Y, Liu C, Liu Y, Liu S, Wang M, Liu D, Miao H, Li S, Zhang B, Huang A, Zhang Y, Qi X and Chen S (2023) Corrigendum: Machine learning prediction model for post- hepatectomy liver failure in hepatocellular carcinoma: a multicenter study. Front. Oncol. 13:1278295. doi: 10.3389/fonc.2023.1278295
Received: 16 August 2023; Accepted: 18 August 2023;
Published: 31 August 2023.
Approved by:
Frontiers Editorial Office, Frontiers Media SA, SwitzerlandCopyright © 2023 Wang, Zheng, Liao, Geng, Li, Zhang, Shang, Liu, Yu, Huang, Liu, Liu, Liu, Wang, Liu, Miao, Li, Zhang, Huang, Zhang, Qi and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Shubo Chen, csb8160@126.com; Xiaolong Qi, qixiaolong@vip.163.com; Yewei Zhang, zhangyewei@njmu.edu.cn
†These authors have contributed equally to this work