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Citation of this article
Huang Z, Shu Z, Zhu RH, Xin JY, Wu LL, Wang HZ, Chen J, Zhang ZW, Luo HC, Li KY. Deep learning-based radiomics based on contrast-enhanced ultrasound predicts early recurrence and survival outcome in hepatocellular carcinoma. World J Gastrointest Oncol 2022; 14(12): 2380-2392 [PMID: 36568943 DOI: 10.4251/wjgo.v14.i12.2380]

Corresponding Author of This Article
Kai-Yan Li, MD, Director, Doctor, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 95 Jiefang Avenue, Qiaokou District, Wuhan 430030, Hubei Province, China. liky20006@126.com

Research Domain of This Article
Oncology

Article-Type of This Article
Observational Study

Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

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Dec 15, 2022 (publication date) through Apr 30, 2025
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Publication Name
World Journal of Gastrointestinal Oncology
ISSN
1948-5204
Publisher of This Article
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
Cited by in CrossRef
For: | Huang Z, Shu Z, Zhu RH, Xin JY, Wu LL, Wang HZ, Chen J, Zhang ZW, Luo HC, Li KY. Deep learning-based radiomics based on contrast-enhanced ultrasound predicts early recurrence and survival outcome in hepatocellular carcinoma. World J Gastrointest Oncol 2022; 14(12): 2380-2392 [PMID: 36568943 DOI: 10.4251/wjgo.v14.i12.2380] |
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URL: | https://www.wjgnet.com/1948-5204/full/v14/i12/2380.htm |
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