| ISSN |
1007-9327 (print) and 2219-2840 (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) 2022. 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 gray-level co-occurrence matrix signature for predicting lymph node metastasis in undifferentiated-type early gastric cancer
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Xin Wei, Xue-Jiao Yan, Yu-Yan Guo, Jie Zhang, Guo-Rong Wang, Arsalan Fayyaz and Jiao Yu |
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| General Project-Social Development Field of Shaanxi Province Science and Technology Department |
2021SF-313 |
| Innovation Capability Support Plan of Shaanxi Science and Technology Department - Science and Technology Innovation Team |
2020TD-048 |
|
| Corresponding Author |
Jiao Yu, MD, Surgical Oncologist, Department of Radiotherapy, Shaanxi Provincial People’s Hospital, No. 256 Youyi West Road, Beilin District, Xi’an 710068, Shaanxi Province, China. shawn170215@163.com |
| Key Words |
Undifferentiated early gastric cancer; Machine learning; Lymph node metastasis; Gray-level co-occurrence matrix; Feature selection; Prediction |
| Core Tip |
Gray-level co-occurrence matrix-based feature extraction can be a robust and promising tool to improve the efficiency in predicting lymph node metastasis of individual undifferentiated early gastric cancer patients. Additionally, machine learning adopts more optimized algorithms and more clear feature extraction. Models developed using random forest classifier have the highest predictive accuracy in terms of Entropy, Haralick full angle, Haralick 30°, inverse gap full angle, inverse gap 45°, inverse gap 0°, and inertia value 45°. Further research is required to develop these models for clinical practice. |
| Publish Date |
2022-09-23 10:13 |
| Citation |
Wei X, Yan XJ, Guo YY, Zhang J, Wang GR, Fayyaz A, Yu J. Machine learning-based gray-level co-occurrence matrix signature for predicting lymph node metastasis in undifferentiated-type early gastric cancer. World J Gastroenterol 2022; 28(36): 5338-5350 |
| URL |
https://www.wjgnet.com/1007-9327/full/v28/i36/5338.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v28.i36.5338 |
