Yang RH, Lin ZP, Dong T, Fan WX, Qin HD, Jiang GH, Dai HY. Magnetic resonance imaging-based radiomics signature for predicting preoperative staging of esophageal cancer. World J Radiol 2025; 17(8): 110307 [DOI: 10.4329/wjr.v17.i8.110307]
Corresponding Author of This Article
Hai-Yang Dai, MD, Department of Radiology, Huizhou Central People's Hospital, No. 41 North Eling Road, Huizhou 516001, Guangdong Province, China. d.ocean@163.com
Research Domain of This Article
Radiology, Nuclear Medicine & Medical Imaging
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
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. d.ocean@163.com
Received: June 4, 2025 Revised: June 24, 2025 Accepted: July 23, 2025 Published online: August 28, 2025 Processing time: 85 Days and 11.8 Hours
Abstract
BACKGROUND
Esophageal cancer (EC) is one of the most prevalent malignant gastrointestinal tumors; accurate prediction of EC staging has high significance before treatment.
AIM
To explore a rational radiomic approach for predicting preoperative staging of EC based on magnetic resonance imaging (MRI).
METHODS
This retrospective study included 210 patients with pathologically confirmed EC, randomly divided into a primary cohort (n = 147) and a validation cohort (n = 63) in a ratio of 7:3. All patients underwent a preoperative MRI scan from the neck to the abdomen. High-throughput and quantitative radiomics features were extracted from T2-weighted imaging (T2WI) and gadolinium contrast-enhanced T1-weighted imaging (T1WI)-Gd images. Radiomics signatures were selected using minimal redundancy maximal relevance and the least absolute shrinkage and selection operator. Then a logistic regression model was built to predict the EC stages. The diagnostic performance of the radiomics model for discriminating between stages I-II and III-IV was evaluated using the area under the curve (AUC), sensitivity (SEN), and specificity (SPE).
RESULTS
A total of 214 radiomics features were extracted. Following feature dimension reduction, the T1WI and T2WI sequences were retained, and 14 features from the T1WI sequence and 3 features from the T2WI sequence were selected to construct radiomics signatures. The radiomics signature combining T2WI with T1WI-Gd demonstrated superior discrimination of stages in the validation cohort (AUC: 0.851; SEN: 0.697; SPE: 0.793), which outperformed single-sequence models (AUC: 0.779, 0.844; SEN: 0.667, 0.636; SPE: 0.8, 0.8).
CONCLUSION
MRI-based radiomics signatures could identify EC stages before treatment, which could serve as a noninvasive and quantitative approach aiding personalized treatment planning.
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.
