Survival path model outperforms conventional static machine learning models in long-term dynamic prognosis prediction for patients with intermediate stage hepatocellular carcinoma
Authors
- Lujun Shen (ORCID: https://orcid.org/0000-0002-7936-0206)
- Jian Xu (ORCID: https://orcid.org/0000-0002-4133-7297)
- Weijun Fan (ORCID: https://orcid.org/0000-0002-8757-647X)
- Peihong Wu (ORCID: https://orcid.org/0000-0001-8008-1872)
- Gulijiayina Nuerhashi
- Li Wang (ORCID: https://orcid.org/0000-0001-7692-5067)
- Chen Li (ORCID: https://orcid.org/0000-0001-9845-6791)
- Fei Cao (ORCID: https://orcid.org/0000-0003-0083-1528)
- Qifeng Chen (ORCID: https://orcid.org/0000-0001-8998-3450)
- Yiquan Jiang
- Jia‐Tao Zhang (ORCID: https://orcid.org/0000-0003-4302-9332)
Institutions
- Sun Yat-sen University (CN)
- Nanfang Hospital (CN)
- Sun Yat-sen University Cancer Center (CN)
- Southern Medical University (CN)
Publication Details
- Journal
- Bioinformatics Advances
- Published
- 2024-12-26
- DOI
- https://doi.org/10.1093/bioadv/vbaf027
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.40