| 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/ |
| Copyright |
© The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Gastroenterology & Hepatology |
| Manuscript Type |
Retrospective Study |
| Article Title |
Computed tomography-based radiomic model for the prediction of neoadjuvant immunochemotherapy response in patients with advanced gastric cancer
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Jun Zhang, Qi Wang, Tian-Hui Guo, Wen Gao, Yi-Miao Yu, Rui-Feng Wang, Hua-Long Yu, Jing-Jing Chen, Ling-Ling Sun, Bi-Yuan Zhang and Hai-Ji Wang |
| ORCID |
|
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Affiliated Hospital of Qingdao University Horizontal Fund |
No. 3635 |
| Intramural Project Fund |
No. 4618 |
|
| Corresponding Author |
Hai-Ji Wang, MD, PhD, Chief Doctor, Full Professor, Department of Radiation Oncology, Affiliated Hospital of Qingdao University, No.59 Haier Road, Laoshan District, Qingdao 266000, Shandong Province, China. whaiji@163.com |
| Key Words |
Gastric cancer; Radiomics; Computed tomography; Neoadjuvant immunochemotherapy; Machine learning; Immunology |
| Core Tip |
We developed and validated a prediction model based on a radiomic signature and a clinical signature to assess the tumor regression grade in advanced gastric cancer (AGC) patients receiving neoadjuvant immunochemotherapy (nICT). The radiomic nomogram showed strong performance in predicting the tumor regression grade in both the training and internal test cohorts. This study represents the first application of radiomics for predicting the nICT response in AGC patients. |
| Publish Date |
2024-09-26 10:17 |
| Citation |
Zhang J, Wang Q, Guo TH, Gao W, Yu YM, Wang RF, Yu HL, Chen JJ, Sun LL, Zhang BY, Wang HJ. Computed tomography-based radiomic model for the prediction of neoadjuvant immunochemotherapy response in patients with advanced gastric cancer. World J Gastrointest Oncol 2024; 16(10): 4115-4128 |
| URL |
https://www.wjgnet.com/1948-5204/full/v16/i10/4115.htm |
| DOI |
https://dx.doi.org/10.4251/wjgo.v16.i10.4115 |
