Predicting response to patients with gastric cancer via a dynamic-aware model with longitudinal liquid biopsy data
These findings represent a pioneering effort in applying AI technology to interpret longitudinal liquid biopsy data and employ visual analytics in GC. This approach provides a promising pathway toward precise response prediction and personalized treatment strategies for patients with GC.
Authors
- Zifan Chen (ORCID: https://orcid.org/0000-0002-1928-3755)
- Bin Dong (ORCID: https://orcid.org/0000-0003-1295-3362)
- Yilin Li (ORCID: https://orcid.org/0000-0003-2610-7730)
- Xinyu Nan
- Jie Zhao (ORCID: https://orcid.org/0000-0002-6086-9387)
- Xujiao Feng
- Yanyan Li (ORCID: https://orcid.org/0000-0002-1241-5268)
- Yang Chen
- Li Zhang (ORCID: https://orcid.org/0000-0001-6674-692X)
- Hui Li (ORCID: https://orcid.org/0000-0003-4759-1779)
- Lin Shen (ORCID: https://orcid.org/0000-0003-4635-1108)
Institutions
- Changsha University (CN)
- Peking University (CN)
- Peking University Cancer Hospital (CN)
- Peking University International Hospital (CN)
- Beijing Haidian Hospital (CN)
- Guangzhou Medical University (CN)
Publication Details
- Journal
- Gastric Cancer
- Published
- 2025-06-17
- DOI
- https://doi.org/10.1007/s10120-025-01628-4
- Citations
- 2
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 1.61
Funders
- National Natural Science Foundation of China