Transformer-based skeletal muscle deep-learning model for survival prediction in gastric cancer patients after curative resection
The Transformer-based SMDL model could accurately predict survival of GC and identify patients at high risk of recurrence or death, thereby assisting clinical decision-making.
Authors
- Wai Haung Yu (ORCID: https://orcid.org/0000-0001-7186-6731)
- Zhe Jin (ORCID: https://orcid.org/0000-0002-4717-1558)
- Jingjing You
- Hua Xiao (ORCID: https://orcid.org/0000-0002-4201-7842)
- Xiaoping Yu (ORCID: https://orcid.org/0000-0003-4502-1027)
- Lian Jian (ORCID: https://orcid.org/0000-0002-8726-875X)
- Bin Zhang (ORCID: https://orcid.org/0000-0001-7313-9300)
- Bin Zhang (ORCID: https://orcid.org/0000-0002-6286-6227)
- Qiuying Chen (ORCID: https://orcid.org/0000-0001-5909-3959)
- Xuewei Wu (ORCID: https://orcid.org/0000-0003-0447-0221)
- Bo Shiun Lai
Institutions
- Central South University (CN)
- Jinan University (CN)
- Hunan Cancer Hospital (CN)
- Third Xiangya Hospital (CN)
- First Affiliated Hospital of Jinan University (CN)
Publication Details
- Journal
- Gastric Cancer
- Published
- 2025-04-15
- DOI
- https://doi.org/10.1007/s10120-025-01614-w
- Citations
- 3
- Primary Topic
- Nutrition and Health in Aging
- Type
- article
- Field-Weighted Citation Impact
- 3.49
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China
- Fundamental Research Funds for the Central Universities