Publication Name	World Journal of Gastrointestinal Oncology
Manuscript ID	98356
Country	China

Received	2024-06-25 05:00
Peer-Review Started	2024-06-25 05:01
To Make the First Decision
Return for Revision	2024-09-23 07:42
Revised	2024-10-02 11:48
Second Decision	2024-10-22 02:42
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2024-10-22 08:30
Articles in Press	2024-10-22 08:30
Publication Fee Transferred	2024-10-23 00:38
Edit the Manuscript by Language Editor
Typeset the Manuscript	2024-10-31 15:25
Publish the Manuscript Online	2024-11-12 12:23

ISSN	1948-5204 (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	Oncology
Manuscript Type	Retrospective Study
Article Title	Deep learning model combined with computed tomography features to preoperatively predicting the risk stratification of gastrointestinal stromal tumors
Manuscript Source	Unsolicited Manuscript
All Author List	Yi Li, Yan-Bei Liu, Xu-Bin Li, Xiao-Nan Cui, Dong-Hua Meng, Cong-Cong Yuan and Zhao-Xiang Ye
ORCID	Author(s) ORCID Number Xu-Bin Li http://orcid.org/0000-0002-9888-4267 Zhao-Xiang Ye http://orcid.org/0000-0003-3157-8393
Funding Agency and Grant Number	Funding Agency Grant Number The Chinese National Key Research and Development Project 2021YFC2500400 and 2021YFC2500402 Tianjin Key Medical Discipline(Specialty) Construction Project TJYXZDXK-009A
Corresponding Author	Zhao-Xiang Ye, PhD, Professor, Department of radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, Tianjin Key Laboratory of Digestive Cancer, Huanhuxi Road, Hexi District, Tianjin 300060, China. yezhaoxiang@163.com
Key Words	Gastrointestinal stromal tumors; Deep learning; Risk stratification; Tomography, X-ray computed; Prognosis
Core Tip	The deep learning model (DLM) was validated to accurately predict the risk classification of gastrointestinal stromal tumors. The combined DLM outperformed DLM in predicting risk stratification. The combined model has potential to guide and facilitate clinical decision-making.
Publish Date	2024-11-12 12:23
Citation	<p>Li Y, Liu YB, Li XB, Cui XN, Meng DH, Yuan CC, Ye ZX. Deep learning model combined with computed tomography features to preoperatively predicting the risk stratification of gastrointestinal stromal tumors. <i>World J Gastrointest Oncol</i> 2024; 16(12): 4663-4674</p>
URL	https://www.wjgnet.com/1948-5204/full/v16/i12/4663.htm
DOI	https://dx.doi.org/10.4251/wjgo.v16.i12.4663

Full Article (PDF)	WJGO-16-4663-with-cover.pdf
Manuscript File	98356_Auto_Edited_025710.docx
Answering Reviewers	98356-answering-reviewers.pdf
Audio Core Tip	98356-audio.mp3
Biostatistics Review Certificate	98356-biostatistics-statement.pdf
Conflict-of-Interest Disclosure Form	98356-conflict-of-interest-statement.pdf
Copyright License Agreement	98356-copyright-assignment.pdf
Approved Grant Application Form(s) or Funding Agency Copy of any Approval Document(s)	98356-foundation-statement.pdf
Signed Informed Consent Form(s) or Document(s)	98356-informed-consent-statement.pdf
Institutional Review Board Approval Form or Document	98356-institutional-review-board-statement.pdf
Non-Native Speakers of English Editing Certificate	98356-non-native-speakers.pdf
Supplementary Material	98356-supplementary-material.pdf
Peer-review Report	98356-peer-reviews.pdf
Scientific Misconduct Check	98356-scientific-misconduct-check.png
Scientific Editor Work List	98356-scientific-editor-work-list.pdf
CrossCheck Report	98356-crosscheck-report.pdf