Publication Name	World Journal of Clinical Cases
Manuscript ID	97245
Country	China

Received	2024-05-26 15:15
Peer-Review Started	2024-05-26 15:15
To Make the First Decision
Return for Revision	2024-06-14 04:58
Revised	2024-06-19 15:03
Second Decision	2024-07-03 02:41
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2024-07-03 10:01
Articles in Press	2024-07-03 10:01
Publication Fee Transferred
Edit the Manuscript by Language Editor	2024-07-17 17:17
Typeset the Manuscript	2024-07-19 00:26
Publish the Manuscript Online	2024-07-23 06:37

ISSN	2307-8960 (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	Obstetrics & Gynecology
Manuscript Type	Retrospective Study
Article Title	Magnetic resonance imaging-based radiomics model for preoperative assessment of risk stratification in endometrial cancer
Manuscript Source	Unsolicited Manuscript
All Author List	Zhi-Yao Wei, Zhe Zhang, Dong-Li Zhao, Wen-Ming Zhao and Yuan-Guang Meng
ORCID	Author(s) ORCID Number Yuan-Guang Meng http://orcid.org/0000-0002-4957-3999
Funding Agency and Grant Number
Corresponding Author	Yuan-Guang Meng, PhD, Chief Doctor, Surgeon, Department of Obstetrics and Gynecology, Seventh Medical Center of Chinese People's Liberation Army General Hospital, No. 5 Nanmencang, Dongsishitiao, Dongcheng District, Beijing 100700, China. meng6512@vip.sina.com
Key Words	Endometrial cancer; Risk stratification; Radiomics; Machine learning; Nomogram
Core Tip	Our research focused on the utilization of clinical features and radiomics derived from magnetic resonance imaging (MRI) in order to predict the risk-grade of endometrial cancer (EC). In our studies, we constructed a predictive model capable of predicting preoperative EC risk. The MRI-based radiomics model put forth in this research showed strong predictive ability and great potential value for assessing the level of EC risk. The integration of predictive models into clinical practice would greatly enhance the preoperative selection of customized therapy.
Publish Date	2024-07-23 06:37
Citation	<p>Wei ZY, Zhang Z, Zhao DL, Zhao WM, Meng YG. Magnetic resonance imaging-based radiomics model for preoperative assessment of risk stratification in endometrial cancer. <i>World J Clin Cases</i> 2024; 12(26): 5908-5921</p>
URL	https://www.wjgnet.com/2307-8960/full/v12/i26/5908.htm
DOI	https://dx.doi.org/10.12998/wjcc.v12.i26.5908

Full Article (PDF)	WJCC-12-5908-with-cover.pdf
Manuscript File	97245_Auto_Edited_015202.docx
Answering Reviewers	97245-answering-reviewers.pdf
Audio Core Tip	97245-audio.mp3
Biostatistics Review Certificate	97245-biostatistics-statement.pdf
Conflict-of-Interest Disclosure Form	97245-conflict-of-interest-statement.pdf
Copyright License Agreement	97245-copyright-assignment.pdf
Signed Informed Consent Form(s) or Document(s)	97245-informed-consent-statement.pdf
Institutional Review Board Approval Form or Document	97245-institutional-review-board-statement.pdf
Non-Native Speakers of English Editing Certificate	97245-non-native-speakers.pdf
Peer-review Report	97245-peer-reviews.pdf
Scientific Misconduct Check	97245-scientific-misconduct-check.png
Scientific Editor Work List	97245-scientific-editor-work-list.pdf
CrossCheck Report	97245-crosscheck-report.png
CrossCheck Report	97245-crosscheck-report.pdf