| ISSN |
1948-5204 (online) |
| Open Access |
This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (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: https://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Oncology |
| Manuscript Type |
Observational Study |
| Article Title |
Deep learning-based radiomics based on contrast-enhanced ultrasound predicts early recurrence and survival outcome in hepatocellular carcinoma
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Zhe Huang, Zhu Shu, Rong-Hua Zhu, Jun-Yi Xin, Ling-Ling Wu, Han-Zhang Wang, Jun Chen, Zhi-Wei Zhang, Hong-Chang Luo and Kai-Yan Li |
| Funding Agency and Grant Number |
|
| Corresponding Author |
Kai-Yan Li, MD, Director, Director, Doctor, 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 |
| Key Words |
Hepatocellular carcinoma; Deep learning; Overall survival; Early recurrence; Contrast-enhanced ultrasound |
| Core Tip |
Multivariate Cox regression analysis confirmed that age [hazard ratio (HR) = 1.01], carbohydrate antigen 19-9 (HR = 0.60), tumor size (HR = 1.11), echogenicity (HR = 0.82), and deep learning-based radiomics (DLR, HR = 4.33) were independent predictors of survival outcome (P < 0.05 for all). The concordance index of the clinical + DLR model in the training and testing cohorts was 0.800 and 0.759, respectively. We divided patients into four categories by dichotomizing predicted early recurrence and survival outcome. We found that for patients with class 1 (high early recurrence rate and low risk of survival outcome), retreatment after recurrence was associated with improved survival. |
| Publish Date |
2022-12-12 06:36 |
| Citation |
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 |
| URL |
https://www.wjgnet.com/1948-5204/full/v14/i12/2380.htm |
| DOI |
https://dx.doi.org/10.4251/wjgo.v14.i12.2380 |
