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Articles Published Processes
2/25/2024 3:12:18 PM | Browse: 478 | Download: 1503
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Received |
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2023-12-04 14:13 |
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Peer-Review Started |
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2023-12-04 14:13 |
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First Decision by Editorial Office Director |
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2023-12-17 01:53 |
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Return for Revision |
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2023-12-17 14:47 |
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Revised |
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2024-01-01 15:04 |
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Publication Fee Transferred |
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Second Decision by Editor |
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2024-01-29 02:06 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2024-01-29 02:47 |
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Articles in Press |
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2024-01-29 02:47 |
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Edit the Manuscript by Language Editor |
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2024-01-31 09:58 |
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Typeset the Manuscript |
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2024-02-19 09:05 |
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Publish the Manuscript Online |
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2024-02-25 15:12 |
| ISSN |
1948-9366 (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: http://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 Cohort Study |
| Article Title |
Machine learning-based radiomics score improves prognostic prediction accuracy of stage II/III gastric cancer: A multi-cohort study
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| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Ying-Hao Xiang, Huan Mou, Bo Qu and Hui-Rong Sun |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Hui-Rong Sun, Doctor, Surgical Oncologist, Department of General Surgery, Lichuan People's Hospital, No. 12 Longchuan Avenue, Enshi 445400, Hubei Province, China. shr0339@163.com |
| Key Words |
Radiomics; Machine learning; Gastric cancer; Skeletal muscle density; Skeletal muscle index |
| Core Tip |
We investigated 141 patients with locally advanced gastric cancer and developed overall survival-associated computed tomography image radiomics score (OACRS) using machine learning and radiomics. The data revealed that OACRS was associated with overall survival (OS), as well as in the Cancer Imaging Archive cohort. Importantly, OACRS could improve the predicted accuracy of OS. |
| Publish Date |
2024-02-25 15:12 |
| Citation |
Xiang YH, Mou H, Qu B, Sun HR. Machine learning-based radiomics score improves prognostic prediction accuracy of stage II/III gastric cancer: A multi-cohort study. World J Gastrointest Surg 2024; 16(2): 345-356
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| URL |
https://www.wjgnet.com/1948-9366/full/v16/i2/345.htm |
| DOI |
https://dx.doi.org/10.4240/wjgs.v16.i2.345 |
Copyright © 1993-2026 Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA. All rights reserved, including rights relating to text and data mining, AI training, and similar technologies. For open-access content, the applicable copyright and licensing terms govern.
