| 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: https://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2025. 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 |
Radiology, Nuclear Medicine & Medical Imaging |
| Manuscript Type |
Retrospective Study |
| Article Title |
Computed tomography-based deep learning and multi-instance learning for predicting microvascular invasion and prognosis in hepatocellular carcinoma
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Yong-Yi Cen, Hai-Yang Nong, Xiao-Xiao Huang, Xiu-Xian Lu, Chang-Hong Pu, Li-Hong Huang, Xiao-Jun Zheng, Zhao-Lin Pan, Yin Huang, Ke Ding and De-You Huang |
| ORCID |
|
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| National Natural Science Foundation of China |
No. 81560278 |
| The “Summit Plan (New Departure)” Project for the Development of Doctoral Degree Authorization Points and Professional Disciplines at the Affiliated Hospital of Youjiang Medical University for Nationalities |
No. DF20244433 |
| Self-funded Research Project by the Guangxi Health and Wellness Committee |
No. Z-L20240824 |
| The Project to Enhance the Research Foundations of Young and Mid-career Faculty in Guangxi Universities |
No. 2024KY0562 |
| Self-funded Research Project by the Guangxi Health and Wellness Committee |
No. Z-L20240834 |
| The Project to Enhance the Research Foundations of Young and Mid-career Faculty in Guangxi Universities |
No. 2024KY0559 |
|
| Corresponding Author |
De-You Huang, Department of Radiology, Affiliated Hospital of Youjiang Medical University for Nationalities, No. 2 Zhongshan Road, Baise 533000, Guangxi Zhuang Autonomous Region, China. fzxyh2012@126.com |
| Key Words |
Hepatocellular carcinoma; Deep learning; Multi-instance learning; Microvascular invasion; Prognosis |
| Core Tip |
This study developed a 2.5-dimensional deep learning-based multi-instance learning (MIL) model (MIL signature) to predict microvascular invasion (MVI) in hepatocellular carcinoma (HCC) using computed tomography arterial phase images. The model outperformed traditional radiomics and clinical models, offering accurate MVI prediction and prognostic stratification for surgical resection and transcatheter arterial chemoembolization cohorts, supporting personalized HCC treatment. |
| Publish Date |
2025-08-07 06:39 |
| Citation |
<p>Cen YY, Nong HY, Huang XX, Lu XX, Pu CH, Huang LH, Zheng XJ, Pan ZL, Huang Y, Ding K, Huang DY. Computed tomography-based deep learning and multi-instance learning for predicting microvascular invasion and prognosis in hepatocellular carcinoma. <i>World J Gastroenterol</i> 2025; 31(30): 109186</p> |
| URL |
https://www.wjgnet.com/1007-9327/full/v31/i30/109186.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v31.i30.109186 |
