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Articles Published Processes
3/12/2024 10:04:17 AM | Browse: 29 | Download: 120
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Received |
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2023-10-09 13:50 |
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Peer-Review Started |
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2023-10-09 13:52 |
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To Make the First Decision |
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Return for Revision |
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2023-12-06 07:18 |
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Revised |
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2023-12-30 04:45 |
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Second Decision |
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2024-01-29 02:06 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Company Editor-in-Chief |
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2024-01-29 05:55 |
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Articles in Press |
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2024-01-29 05:55 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2024-02-29 16:12 |
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Publish the Manuscript Online |
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2024-03-12 10:04 |
| 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: 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 |
Oncology |
| Manuscript Type |
Retrospective Study |
| Article Title |
T2-weighted imaging-based radiomic-clinical machine learning model for predicting the differentiation of colorectal adenocarcinoma
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| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Hui-Da Zheng, Qiao-Yi Huang, Qi-Ming Huang, Xiao-Ting Ke, Kai Ye, Shu Lin and Jian-Hua Xu |
| ORCID |
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| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Fujian Province Clinical Key Specialty Construction Project |
2022884 |
| Quanzhou Science and Technology Plan Project |
2021N034S |
| The Youth Research Project of Fujian Provincial Health Commission |
2022QNA067 |
| Malignant Tumor Clinical Medicine Research Center |
2020N090s |
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| Corresponding Author |
Jian-Hua Xu, MD, Chief Physician, Dean, Research Dean, Surgeon, Surgical Oncologist, Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Fujian Medical University, No. 950 Donghai Street, Fengze District, Quanzhou 362000, Fujian Province, China. xjh630913@126.com |
| Key Words |
Radiomics; Colorectal cancer; Differentiation grade; Machine learning; T2-weighted imaging |
| Core Tip |
In this study, a T2-weighted imaging-based radiomic-clinical machine learning model was developed to preoperatively predict the histological grade of colorectal cancer (CRC). The model showed good performance in both the training and validation cohorts. It provides an effective tool for accurately assessing the differentiation grade of CRC tissue before surgery, which is highly important for selecting the best treatment plan and predicting patient prognosis. |
| Publish Date |
2024-03-12 10:04 |
| Citation |
Zheng HD, Huang QY, Huang QM, Ke XT, Ye K, Lin S, Xu JH. T2-weighted imaging-based radiomic-clinical machine learning model for predicting the differentiation of colorectal adenocarcinoma. World J Gastrointest Oncol 2024; 16(3): 819-832 |
| URL |
https://www.wjgnet.com/1948-5204/full/v16/i3/819.htm |
| DOI |
https://dx.doi.org/10.4251/wjgo.v16.i3.819 |
© 2004-2024 Baishideng Publishing Group Inc. All rights reserved. 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
California Corporate Number: 3537345
