Integrating CEUS Imaging Features and LI-RADS Classification for Postoperative Early Recurrence Prediction in Solitary Hepatocellular Carcinoma: A Machine Learning-Based Prognostic Approach
The GBM-based ML model integrating CEUS imaging features and LI-RADS classification demonstrates potential for predicting early postoperative recurrence of HCC, which may assist in guiding follow-up strategies.
Authors
- Yuquan Wu (ORCID: https://orcid.org/0000-0002-6934-7921)
- Jinshu Pang (ORCID: https://orcid.org/0000-0002-1919-4859)
- Bulin Zhang (ORCID: https://orcid.org/0000-0003-4238-2927)
- Yun He (ORCID: https://orcid.org/0000-0002-2818-1953)
- Rong Wen (ORCID: https://orcid.org/0000-0003-3837-9223)
- Hong Yang (ORCID: https://orcid.org/0000-0002-9873-1631)
- Xiu-Mei Bai (ORCID: https://orcid.org/0000-0001-9581-6851)
- Qiao Que
- Liang Li (ORCID: https://orcid.org/0000-0002-1577-408X)
- Wei Zhang (ORCID: https://orcid.org/0000-0001-6496-849X)
- Jingzhe Peng (ORCID: https://orcid.org/0000-0002-8293-694X)
- Ruizhi Gao (ORCID: https://orcid.org/0000-0001-9847-3081)
Institutions
- The People's Hospital of Guangxi Zhuang Autonomous Region (CN)
- First Affiliated Hospital of GuangXi Medical University (CN)
Publication Details
- Journal
- Journal of Hepatocellular Carcinoma
- Published
- 2025-07-01
- DOI
- https://doi.org/10.2147/jhc.s530848
- Citations
- 1
- Primary Topic
- Hepatocellular Carcinoma Treatment and Prognosis
- Type
- article
- Field-Weighted Citation Impact
- 1.32
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Guangxi Province
- Guangxi Medical University
- Division of Graduate Education