Deep learning radiomics based on contrast enhanced MRI for preoperatively predicting early recurrence in hepatocellular carcinoma after curative resection
Both radiomics and DL models derived from CEMRI can predict HCC recurrence, and DL and radiomics-based integrated approach can provide a more effective tool for the precise prediction of ER for HCC patients undergoing resection.
Authors
- Hongsen Ji
- Qingwei Song (ORCID: https://orcid.org/0000-0002-7035-7211)
- Yuhui Liu (ORCID: https://orcid.org/0000-0003-0867-3632)
- Jun Li (ORCID: https://orcid.org/0000-0002-1171-7141)
- Ying Zhao (ORCID: https://orcid.org/0000-0002-7093-1121)
- Jinghong Liu (ORCID: https://orcid.org/0009-0006-7215-7471)
- Yu Yao (ORCID: https://orcid.org/0000-0003-4752-0102)
- Qinhe Zhang (ORCID: https://orcid.org/0000-0003-0808-4096)
- Ailian Liu (ORCID: https://orcid.org/0000-0002-7288-8227)
- W. J. Su
- Sen Wang (ORCID: https://orcid.org/0000-0002-0593-2902)
- Yue Wang (ORCID: https://orcid.org/0000-0002-4501-8699)
Institutions
- Dalian Medical University (CN)
- Chinese Academy of Sciences (CN)
- First Affiliated Hospital of Dalian Medical University (CN)
- University of Chinese Academy of Sciences (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2024-11-08
- DOI
- https://doi.org/10.3389/fonc.2024.1446386
- Citations
- 10
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 4.04