Zhao ZX. Radiomics-based model for predicting neoadjuvant therapy response in esophageal cancer: Limitations and suggestions. World J Gastrointest Oncol 2026; 18(2): 114981 [DOI: 10.4251/wjgo.v18.i2.114981]
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/
Feb 15, 2026 (publication date) through Feb 3, 2026
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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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Zhao ZX. Radiomics-based model for predicting neoadjuvant therapy response in esophageal cancer: Limitations and suggestions. World J Gastrointest Oncol 2026; 18(2): 114981 [DOI: 10.4251/wjgo.v18.i2.114981]
World J Gastrointest Oncol. Feb 15, 2026; 18(2): 114981 Published online Feb 15, 2026. doi: 10.4251/wjgo.v18.i2.114981
Radiomics-based model for predicting neoadjuvant therapy response in esophageal cancer: Limitations and suggestions
Zong-Xian Zhao
Zong-Xian Zhao, Department of Anorectal Surgery, Fuyang People’s Hospital, Fuyang 236000, Anhui Province, China
Author contributions: Zhao ZX designed and wrote the manuscript.
Conflict-of-interest statement: The author declares that he has no competing interests to disclose.
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/
Received: October 9, 2025 Revised: November 10, 2025 Accepted: November 25, 2025 Published online: February 15, 2026 Processing time: 122 Days and 16.7 Hours
Core Tip
Core Tip: Accurate prediction of neoadjuvant therapy response in esophageal cancer (EC) is critical to avoid ineffective treatment. Yang et al developed a non-invasive radiomics model based on T2-weighted magnetic resonance imaging (T2WI); however, it has several key limitations. These limitations include single-center, small-sample retrospective design, exclusive reliance on T2WI sequence, inadequate stability of manual segmentation, and insufficient clinical interpretability. Corresponding optimization suggestions are proposed in this editorial. Only through multicenter validation, multidisciplinary collaboration, and multimodal integration can radiomics-based machine learning models be truly translated into clinical practice, thereby supporting personalized treatment decision-making for patients with EC.
