Copyright
©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. Jun 26, 2024; 12(18): 3340-3350
Published online Jun 26, 2024. doi: 10.12998/wjcc.v12.i18.3340
Published online Jun 26, 2024. doi: 10.12998/wjcc.v12.i18.3340
Omics-imaging signature-based nomogram to predict the progression-free survival of patients with hepatocellular carcinoma after transcatheter arterial chemoembolization
Qing-Long Guan, Hai-Xiao Zhang, Jun-Peng Gu, Geng-Fei Cao, Department of Interventional Radiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830011, Xinjiang Uygur Autonomous region, China
Wei-Xin Ren, Department of Interventional Radiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830011, Xinjiang Uygur Autonomous Region, China
Co-first authors: Qing-Long Guan and Hai-Xiao Zhang.
Author contributions: Guan QL and Zhang HX contributed equally to this work as co-first authors. Guan QL and Ren WX contributed to the conception design; Zhang HX, Gu JP and Cao GF contributed to the collection and analysis of data, and manuscript writing; Cao GF contributed to the provision of study materials of patients and manuscript revision; all authors read and approved the final manuscript; all authors had full access to the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis as well as the decision to submit for publication.
Institutional review board statement: This study is a retrospective study, and the use of patient clinical data has passed ethical review, ethical review No. 211129-04.
Informed consent statement: The study received approval from the institutional review board of The First Affiliated Hospital of Xinjiang Medical University, and the requirement for informed consent was waived. Informed consent for treatment at admission from all patients is provided herein. The authors affirm the accuracy and comprehensiveness of the reported analyses, as well as the adherence to the study protocol.
Conflict-of-interest statement: The authors declare that there are no competing interests associated with the manuscript.
Data sharing statement: The data that support the findings of this study are not publicly available due to their containing information that could compromise the privacy of research participants but are available from the corresponding author Wei-Xin Ren upon reasonable request.
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: Wei-Xin Ren, MD, Chief Physician, Doctor, Postdoc, Postdoctoral Fellow, Department of Interventional Radiology, The First Affiliated Hospital of Xinjiang Medical University, No. 137 Liyushan South Road, Urumqi 830011, Xinjiang Uygur Auto
Received: March 4, 2024
Revised: April 17, 2024
Accepted: April 23, 2024
Published online: June 26, 2024
Processing time: 106 Days and 1.8 Hours
Revised: April 17, 2024
Accepted: April 23, 2024
Published online: June 26, 2024
Processing time: 106 Days and 1.8 Hours
Core Tip
Core Tip: In this study, traditional radiomics and deep learning (DL) were used to model hepatocellular carcinoma in order to reflect the heterogeneity of tumors through voxel changes in images. The main purpose of this method is to extract the regions of interest features of the three-phase images of enhanced magnetic resonance imaging obtained before transarterial chemoembolization (TACE), construct the prediction model and nomogram through feature screening and least absolute shrinkage and selection operator regression, to construct a prediction model and nomogram to evaluate the clinical value of DL radiomics omics feature model and to predict progression-free survival after TACE.