| Category |
Computer Science, Artificial Intelligence |
| Manuscript Type |
Retrospective Study |
| Article Title |
Evidence-based radiologist-supervised automated Liver Imaging Reporting and Data System categorization for the diagnosis of hepatocellular carcinoma
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Xue-Qin Xia, Ruo-Fan Sheng, Ren-Cheng Zheng, Yu-Xiang Dai, Li Yang, Ying-Hua Chu, Hui Zhang, Xin-Ran Wu, Nan-Nan Shi, Cheng-Yan Wang, Meng-Su Zeng and He Wang |
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Shanghai Municipal Science and Technology Explorer Project |
23TS1400500 |
| National Natural Science Foundation of China |
82271956 |
|
| Corresponding Author |
He Wang, PhD, Professor, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, No. 825 Zhangheng Road, Shanghai 201203, China. hewang@fudan.edu.cn |
| Key Words |
Liver Imaging Reporting and Data System; Hepatocellular carcinoma; Evidence-based radiologist-supervised automated Liver Imaging Reporting and Data System categorization; Quantitative feature characterization; Dynamic contrast-enhanced magnetic resonance imaging |
| Core Tip |
This study developed transparent classifiers for three of the major Liver Imaging Reporting and Data System (LI-RADS) features: Arterial phase hyper-enhancement, washout, and capsule. Then, LI-RADS categories were assigned in accordance with the LI-RADS v2018 guidelines. By following LI-RADS guidelines and emulating the decision-making process of radiologists, the model achieved radiologist-supervised automated LI-RADS categorization, through specialized feature characterization algorithms that provide explicit evidence for feature classification, thereby improving transparency for radiologists and patients. Categorization among LI-RADS grade 3, 4 and 5 achieved accuracies of 80.6%, 74.1%, and 77.9% for the internal testing set and two external testing sets, respectively. |
| Citation |
Xia XQ, Sheng RF, Zheng RC, Dai YX, Yang L, Chu YH, Zhang H, Wu XR, Shi NN, Wang CY, Zeng MS, Wang H. Evidence-based radiologist-supervised automated Liver Imaging Reporting and Data System categorization for the diagnosis of hepatocellular carcinoma. World J Gastroenterol 2026; In press |
| 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) 2026. Published by Baishideng Publishing Group Inc. All rights reserved. |
| 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 |
