Publication Name	World Journal of Radiology
Manuscript ID	110307
Country	China

Received	2025-06-04 07:52
Peer-Review Started	2025-06-04 07:52
To Make the First Decision
Return for Revision	2025-06-12 07:57
Revised	2025-06-24 11:13
Second Decision	2025-07-23 02:36
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2025-07-23 10:53
Articles in Press	2025-07-23 10:53
Publication Fee Transferred	2025-06-29 14:44
Edit the Manuscript by Language Editor
Typeset the Manuscript	2025-08-25 00:26
Publish the Manuscript Online	2025-08-28 08:34

ISSN	1949-8470 (online)
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/
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Category	Radiology, Nuclear Medicine & Medical Imaging
Manuscript Type	Retrospective Study
Article Title	Magnetic resonance imaging-based radiomics signature for predicting preoperative staging of esophageal cancer
Manuscript Source	Unsolicited Manuscript
All Author List	Ri-Hui Yang, Zhi-Ping Lin, Ting Dong, Wei-Xiong Fan, Hao-Dong Qin, Gui-Hua Jiang and Hai-Yang Dai
Funding Agency and Grant Number	Funding Agency Grant Number Guangdong Medical Research Foundation B2023272
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
Key Words	Esophageal cancer; Tumor staging; Magnetic resonance imaging; Radiomics; Logistic regression
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.
Publish Date	2025-08-28 08:34
Citation	<p>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. <i>World J Radiol</i> 2025; 17(8): 110307</p>
URL	https://www.wjgnet.com/1949-8470/full/v17/i8/110307.htm
DOI	https://dx.doi.org/10.4329/wjr.v17.i8.110307

Full Article (PDF)	WJR-17-110307-with-cover.pdf
Manuscript File	110307_Auto_Edited_020902.docx
Answering Reviewers	110307-answering-reviewers.pdf
Audio Core Tip	110307-audio.m4a
Biostatistics Review Certificate	110307-biostatistics-statement.pdf
Conflict-of-Interest Disclosure Form	110307-conflict-of-interest-statement.pdf
Copyright License Agreement	110307-copyright-assignment.pdf
Approved Grant Application Form(s) or Funding Agency Copy of any Approval Document(s)	110307-foundation-statement.pdf
Signed Informed Consent Form(s) or Document(s)	110307-informed-consent-statement.pdf
Institutional Review Board Approval Form or Document	110307-institutional-review-board-statement.pdf
Non-Native Speakers of English Editing Certificate	110307-non-native-speakers.pdf
Peer-review Report	110307-peer-reviews.pdf
Scientific Misconduct Check	110307-scientific-misconduct-check.png
Scientific Editor Work List	110307-scientific-editor-work-list.pdf
CrossCheck Report	110307-crosscheck-report.pdf