Dynamic Prognostic Models for Colorectal Cancer With Liver Metastases
In this retrospective prognostic study, the dynamic models, along with the web-based tool for personalized prediction, demonstrated improved performance by incorporating multiple longitudinal makers.
Authors
- Xinyu Bi (ORCID: https://orcid.org/0000-0002-4011-0939)
- K. Li (ORCID: https://orcid.org/0000-0002-6704-3991)
- Yuan Li (ORCID: https://orcid.org/0000-0002-3171-8053)
- Kun Wang (ORCID: https://orcid.org/0000-0001-9962-9906)
- Yiqiao Deng (ORCID: https://orcid.org/0009-0003-1118-9427)
- Qichen Chen (ORCID: https://orcid.org/0000-0003-3194-7822)
- Hong Zhao
Institutions
- Merck & Co., Inc., Rahway, NJ, USA (United States) (US)
- Sun Yat-sen University (CN)
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Peking University (CN)
- Peking University Cancer Hospital (CN)
- Sun Yat-sen University Cancer Center (CN)
Publication Details
- Journal
- JAMA Network Open
- Published
- 2025-08-27
- DOI
- https://doi.org/10.1001/jamanetworkopen.2025.29093
- Citations
- 3
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 4.08
Funders
- National Natural Science Foundation of China
- Sun Yat-sen University
- China Postdoctoral Science Foundation
- Chinese Academy of Medical Sciences
- National Science and Technology Major Project
- Sanming Project of Medicine in Shenzhen
- National Key Research and Development Program of China
- Sun Yat-sen University Cancer Center