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©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Dec 15, 2022; 14(12): 2380-2392
Published online Dec 15, 2022. doi: 10.4251/wjgo.v14.i12.2380
Published online Dec 15, 2022. doi: 10.4251/wjgo.v14.i12.2380
Deep learning-based radiomics based on contrast-enhanced ultrasound predicts early recurrence and survival outcome in hepatocellular carcinoma
Zhe Huang, Zhu Shu, Jun-Yi Xin, Ling-Ling Wu, Hong-Chang Luo, Kai-Yan Li, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
Rong-Hua Zhu, Zhi-Wei Zhang, Institute of Hepato-pancreato-bililary Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
Han-Zhang Wang, Jun Chen, PDx Advanced Applications, GE Healthcare, Shanghai 200020, China
Author contributions: Huang Z contributed to study concept and design, data acquisition, analysis, and interpretation, and manuscript drafting; Wang HZ and Chen J contributed to the implementation and analysis of all machine learning methods; Huang Z, Zhu S, Wu XB, and Zhu RH contributed to image interpretation and segmentation; Zhu S, Wu XB, Zhang ZW, and Li KY contributed to data acquisition; Huang Z, Li KY, Wang HZ, and Zhu RH contributed to study concept and design, critical revision of the manuscript for important intellectual content, and study supervision.
Institutional review board statement: The study was reviewed and approved by the Ethics Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology (Approval No. TJ-IRB20190401).
Informed consent statement: The Ethics Committee of Tongji Hospital waived the requirement to obtain written informed consent of all participants.
Conflict-of-interest statement: There are no conflicts of interest to report.
Data sharing statement: All data generated or analyzed during this study are included in this article and/or its supplementary material files. Further inquiries can be directed to the corresponding author.
STROBE statement: The authors have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised according to the STROBE Statement—checklist of items.
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/
Corresponding author: 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.
Received: September 15, 2022
Peer-review started: September 15, 2022
First decision: October 19, 2022
Revised: October 21, 2022
Accepted: November 21, 2022
Article in press: November 21, 2022
Published online: December 15, 2022
Processing time: 87 Days and 15.1 Hours
Peer-review started: September 15, 2022
First decision: October 19, 2022
Revised: October 21, 2022
Accepted: November 21, 2022
Article in press: November 21, 2022
Published online: December 15, 2022
Processing time: 87 Days and 15.1 Hours
Core Tip
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.