Yang RH, Fan WX, Zhong Y, Lin ZP, Chen JP, Jiang GH, Dai HY. Predicting esophageal cancer response to neoadjuvant therapy with magnetic resonance imaging radiomics. World J Gastrointest Oncol 2025; 17(10): 110671 [PMID: 41114118 DOI: 10.4251/wjgo.v17.i10.110671]
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
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Retrospective Study
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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/
Oct 15, 2025 (publication date) through Oct 25, 2025
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Publication Name
World Journal of Gastrointestinal Oncology
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1948-5204
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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
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Yang RH, Fan WX, Zhong Y, Lin ZP, Chen JP, Jiang GH, Dai HY. Predicting esophageal cancer response to neoadjuvant therapy with magnetic resonance imaging radiomics. World J Gastrointest Oncol 2025; 17(10): 110671 [PMID: 41114118 DOI: 10.4251/wjgo.v17.i10.110671]
World J Gastrointest Oncol. Oct 15, 2025; 17(10): 110671 Published online Oct 15, 2025. doi: 10.4251/wjgo.v17.i10.110671
Predicting esophageal cancer response to neoadjuvant therapy with magnetic resonance imaging radiomics
Ri-Hui Yang, Wei-Xiong Fan, Yi Zhong, Zhi-Ping Lin, Jian-Ping Chen, Gui-Hua Jiang, Hai-Yang Dai
Ri-Hui Yang, Wei-Xiong Fan, Yi Zhong, Department of Magnetic Resonance, Meizhou People’s Hospital, Meizhou 514031, Guangdong Province, China
Zhi-Ping Lin, GE Healthcare, Guangzhou 510623, Guangdong Province, China
Jian-Ping Chen, Department of Intervention, Meizhou People’s Hospital, Meizhou 514031, Guangdong Province, China
Gui-Hua Jiang, Department of Medical Imaging, Guangdong Second Province General Hospital, 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 Wei-Xiong Fan.
Co-corresponding authors: Gui-Hua Jiang and Hai-Yang Dai.
Author contributions: Yang RH and Fan WX contribute equally to this study as co-first authors and they participated in the conception and design of the study; Zhong Y involved in the acquisition, analysis, or interpretation of data; Lin ZP and Chen JP 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 (grant 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: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 12, 2025 Revised: August 12, 2025 Accepted: September 19, 2025 Published online: October 15, 2025 Processing time: 124 Days and 18.8 Hours
Abstract
BACKGROUND
Predicting the pathological response of esophageal cancer (EC) to neoadjuvant therapy (NAT) is of significant clinical importance.
AIM
To evaluate the pathological response of NAT in EC patients using multiple machine learning algorithms based on magnetic resonance imaging (MRI) radiomics.
METHODS
This retrospective study included 132 patients with pathologically confirmed EC, were randomly divided into a training cohort (n = 92) and a validation cohort (n = 40) in a 7:3 ratio. 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). Radiomics signatures were selected using minimal redundancy maximal relevance and the least absolute shrinkage and selection operator. Nine classification algorithms were used to build the models, and the diagnostic performance of each model was evaluated using the area under the curve (AUC), sensitivity (SEN), and specificity (SPE).
RESULTS
A total of 1834 features were extracted. Following feature dimension reduction, ten radiomics features were selected to construct radiomics signatures. Among the nine classification algorithms, the ExtraTrees algorithm demonstrated the best diagnostic performance in both the training (AUC: 0.932; SEN: 0.906; SPE: 0.817) and validation cohorts (AUC: 0.900; SEN: 0.667; SPE: 0.700). The Delong test proved no significance in the diagnostic efficiency within these models (P > 0.05).
CONCLUSION
T2WI radiomics may aid in determining the pathological response to NAT in EC patients, serving as a noninvasive and quantitative tool to assist personalized treatment planning.
Core Tip: Few studies have utilized multiple radiomic algorithms to predict the pathological response to neoadjuvant therapy (NAT) in esophageal cancer (EC). In this study we found ten radiomics features related to pathological therapeutic response. The ExtraTrees algorithm performed good efficiency in the predictive and validation sets. Our study showed that the radiomics model derived from magnetic resonance imaging T2-weighted imaging images demonstrates good performance in determining the pathological response of NAT in EC, which would help individualized plans in EC.
