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Articles Published Processes
5/15/2025 10:28:58 AM | Browse: 10 | Download: 32
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Received |
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2025-02-17 08:52 |
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Peer-Review Started |
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2025-02-17 08:53 |
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To Make the First Decision |
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Return for Revision |
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2025-02-26 07:44 |
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Revised |
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2025-03-08 13:14 |
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Second Decision |
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2025-03-31 02:34 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2025-03-31 05:15 |
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Articles in Press |
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2025-03-31 05:15 |
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Publication Fee Transferred |
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2025-03-14 02:04 |
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Edit the Manuscript by Language Editor |
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2025-04-01 02:28 |
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Typeset the Manuscript |
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2025-04-16 05:41 |
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Publish the Manuscript Online |
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2025-05-15 10:28 |
| 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 |
Oncology |
| Manuscript Type |
Retrospective Study |
| Article Title |
Computed tomography-based deep learning for preoperative prediction of tumor immune microenvironment in colorectal cancer
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| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Chuan Zhou, Yun-Feng Zhang, Zhi-Jun Yang, Yu-Qian Huang and Ming-Xu Da |
| ORCID |
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| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| National Natural Science Foundation of China |
No. 81860047 |
| Natural Science Foundation of Gansu Province |
No. 22JR5RA650 |
| Key Science and Technology Program in Gansu Province |
No. 21YF5FA016 |
| Gansu Provincial Hospital Scientific Research Foundation |
No. 23GSSYD-12 |
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| Corresponding Author |
Ming-Xu Da, Chief, PhD, The First Clinical Medical College of Lanzhou University, Lanzhou University, No. 222 South Tianshui Road, Lanzhou 730000, Gansu Province, China. lzu_damx@126.com |
| Key Words |
Deep learning; Radiomics; Computed tomography imaging; Colorectal cancer; Tumor immune microenvironment |
| Core Tip |
This study introduces a novel computed tomography (CT)-based deep learning (DL) radiomics approach for noninvasive assessment of the tumor immune microenvironment (TIME) in colorectal cancer. By analyzing preoperative CT scans from 315 patients, DL models achieved high predictive accuracy (area under the curves: 0.851-0.892) for key TIME features: Tumor-stroma ratio, lymphocyte infiltration, and immune scoring. Clinical validation through calibration and decision curve analyses confirmed the utility of this approach in guiding immunotherapy strategies. This method eliminates invasive biopsy requirements while enabling personalized treatment planning and enhanced prognostic evaluation. The findings establish DL radiomics as a paradigm-shifting tool for precision oncology in gastrointestinal malignancies. |
| Publish Date |
2025-05-15 10:28 |
| Citation |
<p>Zhou C, Zhang YF, Yang ZJ, Huang YQ, Da MX. Computed tomography-based deep learning for preoperative prediction of tumor immune microenvironment in colorectal cancer. <i>World J Gastrointest Oncol</i> 2025; 17(5): 106103</p> |
| URL |
https://www.wjgnet.com/1948-5204/full/v17/i5/106103.htm |
| DOI |
https://dx.doi.org/10.4251/wjgo.v17.i5.106103 |
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