Retrospective Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Apr 21, 2024; 30(15): 2128-2142
Published online Apr 21, 2024. doi: 10.3748/wjg.v30.i15.2128
Computed tomography-based radiomics to predict early recurrence of hepatocellular carcinoma post-hepatectomy in patients background on cirrhosis
Gui-Xiang Qian, Zi-Ling Xu, Yong-Hai Li, Jian-Lin Lu, Xiang-Yi Bu, Ming-Tong Wei, Wei-Dong Jia
Gui-Xiang Qian, Xiang-Yi Bu, Wei-Dong Jia, Department of Hepatic Surgery, Anhui Provincial Hospital, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Hefei 230001, Anhui Province, China
Zi-Ling Xu, Jian-Lin Lu, Ming-Tong Wei, Department of Hepatic Surgery, Anhui Provincial Hospital Affiliated to Anhui Medical University, Anhui Medical University, Hefei 230001, Anhui Province, China
Yong-Hai Li, Department of Anorectal Surgery, the First People’s Hospital of Hefei, Hefei 230001, Anhui Province, China
Co-first authors: Gui-Xiang Qian and Zi-Ling Xu.
Author contributions: Qian GX and Jia WD designed the research study; Qian GX, Xu ZL, Li YH, Bo XY, Wei MT and Lu JL collected the data; Xu ZL, Lu JL and Wei MT analyzed the data; all authors wrote the manuscript; Qian GX, Li YH, and Jia WD revised the manuscript; all authors have read and approve the final manuscript. Qian GX and Xu ZL have made equivalent contributions in this article. The reasons are as follows: First, the research covered in this manuscript was a collaborative team effort, with each author dedicating substantial time and effort. Qian GX was responsible for study design, method development, data collection, experimental data analysis, manuscript writing, and subsequent revisions. Meanwhile, Xu ZL played a significant role in data collection, data analysis, and initial manuscript drafting. Second, Xu ZL brings valuable clinical experience to the team. Throughout the research collaboration with Qian GX, Xu ZL continuously refined the study process, leveraging his accumulated knowledge to identify and rectify potential errors. On the other hand, Qian GX skillfully applied her clinical and machine learning expertise to ensure the study’s quality and reliability. Given these reasons, and to accurately reflect the efforts and contributions of each author, I, as the corresponding author, have designated Qian GX and Xu ZL as co-first authors for this study, acknowledging their equal contributions.
Supported by Anhui Provincial Key Research and Development Plan, No. 202104j07020048.
Institutional review board statement: This study was reviewed and approved by the Ethics Committee of the First Affiliated Hospital of the University of Science and Technology of China (Anhui Provincial Hospital) (2021-RE-043).
Informed consent statement: The need for informed consent was waived owing to the retrospective nature of the study. All procedures involving human participants were in accordance with the Declaration of Helsinki and its subsequent amendments.
Conflict-of-interest statement: All authors declare that they have no conflict of interest.
Data sharing statement: The datasets generated and/or analysed during the current study are not publicly available due to patient privacy and copyright issues but are available from the corresponding author upon reasonable request.
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: Wei-Dong Jia, PhD, Doctor, Department of Hepatic Surgery, Anhui Provincial Hospital, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, No. 17 Lujiang Road, Hefei 230001, Anhui Province, China. jwd1968@ustc.edu.cn
Received: December 30, 2023
Peer-review started: December 30, 2023
First decision: January 27, 2024
Revised: February 8, 2024
Accepted: March 12, 2024
Article in press: March 12, 2024
Published online: April 21, 2024
Processing time: 111 Days and 0.6 Hours
Core Tip

Core Tip: Hepatocellular carcinoma (HCC) ranks as the sixth most prevalent tumour and stands as the third leading cause of cancer-related deaths globally. In contrast to individuals with HCC in normal liver tissue, those with HCC in the context of cirrhosis frequently experience a higher recurrence rate. Therefore, a machine learning model aimed at predicting the early recurrence of post-hepatectomy HCC in patients with cirrhosis was developed. The study also aimed to stratify patients’ overall survival based on the predicted risk of recurrence.