Transfer learning radiomic model predicts intratumoral tertiary lymphoid structures in hepatocellular carcinoma: a multicenter study
The TLR model has indicated accurate prediction of iTLS status, which may assist in the risk stratification for patients with HCC in clinical practice.
Authors
- Ganmian Dai
- Shichao Long
- Yi Feng (ORCID: https://orcid.org/0000-0002-3952-1351)
- Kai Fu (ORCID: https://orcid.org/0000-0001-8571-5352)
- Bocheng Zou
- Deng Pan (ORCID: https://orcid.org/0000-0002-6475-0463)
- Wenzheng Li
- Linhui Zhong
- Wenguang Liu (ORCID: https://orcid.org/0000-0002-4216-2070)
- Yue Ruan (ORCID: https://orcid.org/0000-0003-3392-072X)
- Mengsi Li (ORCID: https://orcid.org/0000-0002-7486-2442)
- Juan Chen (ORCID: https://orcid.org/0000-0003-1332-2431)
- Xiong Chen (ORCID: https://orcid.org/0009-0005-3311-2783)
Institutions
- Fujian Medical University (CN)
- Central South University (CN)
- Mengchao Hepatobiliary Hospital (CN)
- Hainan Medical College Hospital (CN)
- Xiangya Hospital Central South University (CN)
- Hainan Medical University (CN)
Publication Details
- Journal
- Journal for ImmunoTherapy of Cancer
- Published
- 2025-03-01
- DOI
- https://doi.org/10.1136/jitc-2024-011126
- Citations
- 20
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 27.20
Funders
- National Natural Science Foundation of China
- China Postdoctoral Science Foundation
- Natural Science Foundation of Hunan Province