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©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Radiol. Aug 28, 2025; 17(8): 110307
Published online Aug 28, 2025. doi: 10.4329/wjr.v17.i8.110307
Published online Aug 28, 2025. doi: 10.4329/wjr.v17.i8.110307
Magnetic resonance imaging-based radiomics signature for predicting preoperative staging of esophageal cancer
Ri-Hui Yang, Wei-Xiong Fan, Department of Magnetic Resonance, Meizhou People’s Hospital, Meizhou 514031, Guangdong Province, China
Zhi-Ping Lin, GE Healthcare, Guangzhou 510623, Guangdong Province, China
Ting Dong, Gui-Hua Jiang, Department of Medical Imaging, Guangdong Second Province General Hospital, Guangzhou 510317, Guangdong Province, China
Hao-Dong Qin, Siemens Healthineers, Guangzhou 510317, Guangdong Province, China
Hai-Yang Dai, Department of Radiology, Huizhou Central People’s Hospital, Huizhou 516001, Guangdong Province, China
Co-first authors: Ri-Hui Yang and Zhi-Ping Lin.
Co-corresponding authors: Gui-Hua Jiang and Hai-Yang Dai.
Author contributions: Yang RH and Lin ZP participated in the conception and design of the study, and contributed equally to this work as co-first authors; Dong T was involved in the acquisition, analysis, or interpretation of data; Fan WX and Qin HD prepared the tables and figures; Yang RH wrote the first draft and subsequent versions; Jiang GH and Dai HY was responsible for project administration and supervision, and contributed equally to this work as co-corresponding authors; all authors critically reviewed and approved the final manuscript to be published.
Supported by Guangdong Medical Research Foundation, No. B2023272.
Institutional review board statement: This study was approved by the Ethics Committee on Clinical Researches and Novel Technologies of Meizhou People’s Hospital (No. 2023-C-45).
Informed consent statement: Patient informed consent was waived for this retrospective study.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: The data that support the findings of this study are available from the corresponding author upon reasonable request at d.ocean@163.com.
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: Hai-Yang Dai, MD, Department of Radiology, Huizhou Central People's Hospital, No. 41 North Eling Road, Huizhou 516001, Guangdong Province, China.
Received: June 4, 2025
Revised: June 24, 2025
Accepted: July 23, 2025
Published online: August 28, 2025
Processing time: 85 Days and 19.5 Hours
Revised: June 24, 2025
Accepted: July 23, 2025
Published online: August 28, 2025
Processing time: 85 Days and 19.5 Hours
Core Tip
Core Tip: This study developed a novel magnetic resonance imaging-based radiomics approach to noninvasively predict preoperative stage of esophageal cancer (EC). By integrating quantitative features from T2-weighted imaging and contrast-enhanced T1-weighted imaging sequences in 210 EC patients, a logistic regression model achieved high accuracy in distinguishing early-stage (I-II) from advanced-stage (III-IV) disease. This multimodal radiomics signature outperformed single-sequence models and offers a promising tool for guiding personalized treatment strategies.