ViT‐based quantification of intratumoral heterogeneity for predicting the early recurrence in HCC following multiple ablation
The multimodal-based model, incorporating clinic-radiologic factors and ITH features, demonstrated superior performance in predicting ER among early-stage HCC patients undergoing different ablation modalities.
Authors
- Jinhua Pan (ORCID: https://orcid.org/0000-0002-6691-8282)
- Tianan Jiang (ORCID: https://orcid.org/0000-0002-7672-8394)
- Jionghui Gu (ORCID: https://orcid.org/0000-0001-6386-0065)
- Haoyan Zhang (ORCID: https://orcid.org/0000-0003-3067-0710)
- Ke Zhang (ORCID: https://orcid.org/0000-0001-8259-9747)
- Kun Wang (ORCID: https://orcid.org/0000-0003-2513-768X)
- Jie Tian (ORCID: https://orcid.org/0000-0003-0498-0432)
- Wei Yang (ORCID: https://orcid.org/0000-0003-0198-1632)
- Lei Mu (ORCID: https://orcid.org/0000-0002-7922-2743)
- Min Xu (ORCID: https://orcid.org/0000-0001-9455-9494)
- Jinyu Ru
- Wenbo Wang (ORCID: https://orcid.org/0009-0005-8353-014X)
Institutions
- Chinese Academy of Sciences (CN)
- Peking University (CN)
- Peking University Cancer Hospital (CN)
- Shandong Institute of Automation (CN)
- Beijing Academy of Artificial Intelligence (CN)
- University of Chinese Academy of Sciences (CN)
- First Affiliated Hospital Zhejiang University (CN)
- Beihang University (CN)
Publication Details
- Journal
- Liver International
- Published
- 2024-11-11
- DOI
- https://doi.org/10.1111/liv.16051
- Citations
- 5
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.02