| 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 |
©Author(s) (or their employer(s)) 2026. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
|
| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
|
| 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 Study |
| Article Title |
Computed tomography-based transformer model for non-invasive prediction of portal venous pressure in chronic liver diseases
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Xu Guo, Huan Tong, Wu-Que Cai, Jia-Yi He, Ting-Rui Han, Shuai-Jie Qian, Xin Quan, Ying Li, Bo Wei, Zhi-Dong Wang, Yang Tai, Da-Qing Guo and Hao Wu |
| ORCID |
|
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| National Natural Science Foundation of China |
82270649 |
| Chengdu Science and Technology Program |
2024-YF05-00564-SN |
|
| Corresponding Author |
Hao Wu, PhD, Professor, Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, No. 37 Guo Xue Xiang, Chengdu 610041, Sichuan Province, China. hxxhwh@163.com |
| Key Words |
Portal hypertension; Hepatic venous pressure gradient; Transformer network; Computed tomography; Non-invasive prediction |
| Core Tip |
This retrospective study developed and validated a non-invasive regression machine learning model to predict the hepatic venous pressure gradient values in chronic liver diseases. Utilizing the transformer network combined with transfer learning based on contrast-enhanced computed tomography images from four pivotal sections, our model efficiently predicted the entire spectrum of portal venous pressure (from 1 to 37 mmHg) and showed good diagnostic performance for clinically significant hepatic venous pressure gradient thresholds including 5, 10, 12, 16 and 20 mmHg, outperforming conventional non-invasive methods. Moreover, the time-saving approach in this study minimized region of interest delineation workload, demonstrating potential for routine clinical application. |
| Publish Date |
2026-10-27 06:30 |
| Citation |
Guo X, Tong H, Cai WQ, He JY, Han TR, Qian SJ, Quan X, Li Y, Wei B, Wang ZD, Tai Y, Guo DQ, Wu H. Computed tomography-based transformer model for non-invasive prediction of portal venous pressure in chronic liver diseases. World J Gastroenterol 2026; 32(47): 121435
|
| URL |
https://www.wjgnet.com/1007-9327/full/v32/i47/121435.htm |
| DOI |
https://doi.org/10.3748/wjg.121435 |
