Publication Name	World Journal of Gastrointestinal Oncology
Manuscript ID	88756
Country	China

Received	2023-10-08 03:16
Peer-Review Started	2023-10-08 03:17
To Make the First Decision
Return for Revision	2024-01-15 05:01
Revised	2024-01-23 03:28
Second Decision	2024-02-25 02:39
Accepted by Journal Editor-in-Chief
Accepted by Company Editor-in-Chief	2024-02-25 05:39
Articles in Press	2024-02-25 05:39
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2024-03-19 02:57
Publish the Manuscript Online	2024-04-11 02:23

ISSN	1948-5204 (online)
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Category	Gastroenterology & Hepatology
Manuscript Type	Observational Study
Article Title	Computed tomography radiogenomics: A potential tool for prediction of molecular subtypes in gastric stromal tumor
Manuscript Source	Unsolicited Manuscript
All Author List	Xiao-Nan Yin, Zi-Hao Wang, Li Zou, Cai-Wei Yang, Chao-Yong Shen, Bai-Ke Liu, Yuan Yin, Xi-Jiao Liu and Bo Zhang
ORCID	Author(s) ORCID Number Xiao-Nan Yin http://orcid.org/0000-0003-4525-1877 Cai-Wei Yang http://orcid.org/0000-0003-3335-3948 Chao-Yong Shen http://orcid.org/0000-0002-8426-3611 Xi-Jiao Liu http://orcid.org/0000-0002-6900-0696 Bo Zhang http://orcid.org/0000-0002-0254-5843
Funding Agency and Grant Number	Funding Agency Grant Number National Natural Science Foundation of China Program Grant No. 82203108 China Postdoctoral Science Foundation No. 2022M722275 Beijing Bethune Charitable Foundation No. WCJZL202105 Beijing Xisike Clinical Oncology Research Foundation No. Y-zai2021/zd-0185
Corresponding Author	Xi-Jiao Liu, PhD, Doctor, Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Lane, Wuhou District, Chengdu 610041, Sichuan Province, China. bless_jiao@163.com
Key Words	Gastrointestinal stromal tumor; Radiomics; Gene mutation; Computed tomography; Model
Core Tip	In this study, we developed and validated a radiomics model to predict the genotypes of gastric gastrointestinal stromal tumors (GISTs) using contrast-enhanced computed tomography images. Our findings demonstrated that the radiomics model exhibited a satisfactory performance in distinguishing gastric GISTs with KIT exon 11 mutations and GISTs with KIT exon 11 codons 557-558 deletions. Among the different models evaluated, the combined modelCT sign + rad + clinic demonstrated the highest predictive accuracy. This model holds promise as an effective and noninvasive approach to guide personalized treatment decisions prior to surgery.
Publish Date	2024-04-11 02:23
Citation	Yin XN, Wang ZH, Zou L, Yang CW, Shen CY, Liu BK, Yin Y, Liu XJ, Zhang B. Computed tomography radiogenomics: A potential tool for prediction of molecular subtypes in gastric stromal tumor. World J Gastrointest Oncol 2024; 16(4): 1296-1308
URL	https://www.wjgnet.com/1948-5204/full/v16/i4/1296.htm
DOI	https://dx.doi.org/10.4251/wjgo.v16.i4.1296

Full Article (PDF)	WJGO-16-1296-with-cover.pdf
STROBE Statement	88756-STROBE statement.pdf
Manuscript File	88756_Auto_Edited-YJP.docx
Answering Reviewers	88756-Answering reviewers.pdf
Audio Core Tip	88756-Audio core tip.mp3
Biostatistics Review Certificate	88756-Biostatistics statement.pdf
Conflict-of-Interest Disclosure Form	88756-Conflict-of-interest statement.pdf
Copyright License Agreement	88756-Copyright license agreement.pdf
Approved Grant Application Form(s) or Funding Agency Copy of any Approval Document(s)	88756-Grant application form(s).pdf
Signed Informed Consent Form(s) or Document(s)	88756-Informed consent statement.pdf
Institutional Review Board Approval Form or Document	88756-Institutional review board statement.pdf
Peer-review Report	88756-Peer-review(s).pdf
Scientific Misconduct Check	88756-Bing-Chen YL-2.png
Scientific Editor Work List	88756-Scientific editor work list.pdf