The value of using deep learning to predict proliferative hepatocellular carcinoma based on multiphasic magnetic resonance imaging
The hybrid model showed potential for predicting PHCC, which may assist clinicians in making personalized treatment decisions.
Authors
- Kaiyi Yuan
- Xiaoming Li (ORCID: https://orcid.org/0000-0002-7229-5915)
- X. Li (ORCID: https://orcid.org/0009-0009-6118-6498)
- Xu Yang (ORCID: https://orcid.org/0000-0002-8276-2679)
- Jie Cheng (ORCID: https://orcid.org/0000-0003-2580-1878)
- Fengxi Chen (ORCID: https://orcid.org/0000-0002-8951-5339)
- Huarong Zhang (ORCID: https://orcid.org/0000-0001-9994-0112)
- Wei Chen (ORCID: https://orcid.org/0000-0002-1919-594X)
- Yuxin He (ORCID: https://orcid.org/0009-0004-2869-4523)
- Yiman Li (ORCID: https://orcid.org/0009-0000-7243-6784)
- Lizhen Liu
- Ping Cai (ORCID: https://orcid.org/0000-0002-2728-5531)
Institutions
- Northeastern University (US)
- Army Medical University (CN)
- Chongqing University (CN)
- Dalian Medical University (CN)
- Second Affiliated Hospital of Chongqing Medical University (CN)
- Southwest Hospital (CN)
- Chongqing Medical University (CN)
Publication Details
- Journal
- Quantitative Imaging in Medicine and Surgery
- Published
- 2025-09-24
- DOI
- https://doi.org/10.21037/qims-2024-2656
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00