Retrospective Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. Sep 16, 2024; 12(26): 5908-5921
Published online Sep 16, 2024. doi: 10.12998/wjcc.v12.i26.5908
Magnetic resonance imaging-based radiomics model for preoperative assessment of risk stratification in endometrial cancer
Zhi-Yao Wei, Zhe Zhang, Dong-Li Zhao, Wen-Ming Zhao, Yuan-Guang Meng
Zhi-Yao Wei, Zhe Zhang, Dong-Li Zhao, Yuan-Guang Meng, Department of Obstetrics and Gynecology, Seventh Medical Center of Chinese People’s Liberation Army General Hospital, Beijing 100700, China
Wen-Ming Zhao, National Genomics Data Center and Chinese Academy of Sciences Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100700, China
Author contributions: Wei ZY, Zhang Z, and Zhao DL contributed to methodology; Wei ZY contributed to writing the original draft and formal analysis; Meng YG contributed to writing, reviewing, and editing; Wei ZY and Zhao DL contributed to supervision; All authors contributed to data curation and conceptualization and read and approved the final manuscript.
Institutional review board statement: This study was performed in line with the principles of the Declaration of Helsinki. The Institutional Ethics Review Board approved this retrospective study and waived the requirement for written informed consent (No: 2022-403).
Informed consent statement: The retrospective inquiry in question was exempt from the requirement of informed consent as it had obtained approval from the institutional review board.
Conflict-of-interest statement: The authors declare that they have no competing interests.
Data sharing statement: The datasets generated and analyzed during the current study are not publicly available but are available from the corresponding author on 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: Yuan-Guang Meng, PhD, Chief Doctor, Surgeon, Department of Obstetrics and Gynecology, Seventh Medical Center of Chinese People’s Liberation Army General Hospital, No. 5 Nanmencang, Dongsishitiao, Dongcheng District, Beijing 100700, China. meng6512@vip.sina.com
Received: May 26, 2024
Revised: June 19, 2024
Accepted: July 3, 2024
Published online: September 16, 2024
Processing time: 57 Days and 15.7 Hours
Core Tip

Core Tip: Our research focused on the utilization of clinical features and radiomics derived from magnetic resonance imaging (MRI) in order to predict the risk grade of endometrial cancer (EC). In our studies, we constructed a predictive model capable of predicting preoperative EC risk. The MRI-based radiomics model put forth in this research showed strong predictive ability and great potential value for assessing the level of EC risk. The integration of predictive models into clinical practice would greatly enhance the preoperative selection of customized therapy.