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Articles Published Processes
6/26/2024 11:31:10 AM | Browse: 69 | Download: 326
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Received |
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2024-03-09 15:43 |
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Peer-Review Started |
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2024-03-09 15:43 |
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To Make the First Decision |
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Return for Revision |
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2024-04-17 04:31 |
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Revised |
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2024-05-13 14:27 |
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Second Decision |
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2024-05-28 02:44 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2024-05-28 06:04 |
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Articles in Press |
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2024-05-28 06:04 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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2024-06-03 14:41 |
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Typeset the Manuscript |
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2024-06-06 01:13 |
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Publish the Manuscript Online |
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2024-06-26 11:31 |
| ISSN |
1949-8470 (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 |
Predicting distant metastasis in nasopharyngeal carcinoma using gradient boosting tree model based on detailed magnetic resonance imaging reports
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Yu-Liang Zhu, Xin-Lei Deng, Xu-Cheng Zhang, Li Tian, Chun-Yan Cui, Feng Lei, Gui-Qiong Xu, Hao-Jiang Li, Li-Zhi Liu and Hua-Li Ma |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Hua-Li Ma, MD, Doctor, Doctor, Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, No. 651 Dongfeng Road East, Guangzhou 510060, Guangdong Province, China. mahual@sysucc.org.cn |
| Key Words |
Nasopharyngeal carcinoma; Distant metastasis; Machine learning; Detailed magnetic resonance imaging report; Gradient boosting tree model |
| Core Tip |
A total of 469 imaging variables obtained from detailed magnetic resonance imaging (MRI) reports of 792 patients with nasopharyngeal carcinoma (NPC) with non-distant metastasis were evaluated in this retrospective study. Data were stratified and randomly split into training (50%) and testing (50%) sets. Gradient boosting tree (GBT) models were built based on the training set and used to select imaging variables to predict distant metastasis (DM). The number of metastatic cervical nodes was the top contributor for predicting DM based on the relative importance in GBT models. The GBT model outperformed the tumor node-staging system in predicting metastasis in NPC. |
| Publish Date |
2024-06-26 11:31 |
| Citation |
<p>Zhu YL, Deng XL, Zhang XC, Tian L, Cui CY, Lei F, Xu GQ, Li HJ, Liu LZ, Ma HL. Predicting distant metastasis in nasopharyngeal carcinoma using gradient boosting tree model based on detailed magnetic resonance imaging reports. <i>World J Radiol</i> 2024; 16(6): 203-210</p> |
| URL |
https://www.wjgnet.com/1949-8470/full/v16/i6/203.htm |
| DOI |
https://dx.doi.org/10.4329/wjr.v16.i6.203 |
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