Augmenting conventional criteria: a CT-based deep learning radiomics nomogram for early recurrence risk stratification in hepatocellular carcinoma after liver transplantation
A CT-based DLRN for predicting early HCC recurrence post-transplant was developed. The DLRN predicted recurrence with high accuracy (AUC: 0.829) and 16.370-fold increased recurrence risk. Combining DLRN with Metro-Ticket 2.0 criteria achieved optimal prediction (AUC: 0.863).
Authors
- Ke Ren (ORCID: https://orcid.org/0000-0002-5735-055X)
- Jiawen Luo
- Jie Ding (ORCID: https://orcid.org/0000-0002-5637-228X)
- Siyu Ouyang
- Danyang Liu (ORCID: https://orcid.org/0000-0002-9082-2491)
- Guo Qiu
- Ziqian Wu
- Jidong Gao
- Jingyi Hu (ORCID: https://orcid.org/0000-0001-7726-6753)
Institutions
- Sun Yat-sen University (CN)
- Xiamen University (CN)
- Dalian Medical University (CN)
- First Affiliated Hospital of Xiamen University (CN)
- Second Affiliated Hospital of Dalian Medical University (CN)
- Third Affiliated Hospital of Sun Yat-sen University (CN)
Publication Details
- Journal
- Insights into Imaging
- Published
- 2025-09-17
- DOI
- https://doi.org/10.1186/s13244-025-02082-7
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.72