RPF-Net: A multimodal model for the postoperative UISS risk stratification of non-metastatic ccRCC based on CT and whole-slide images
The RPF-Net models the diagnostic process of multimodal data and shows strong generalization and excellent performance. This model may be a potential tool to facilitate clinical risk stratification and management for postoperative nccRCC patients.
Authors
- Chongzhe Yan
- Wuchao Li (ORCID: https://orcid.org/0000-0003-3691-5694)
- Zhangxin Hong (ORCID: https://orcid.org/0009-0001-9245-3685)
- Pinhao Li
- Qi Tang (ORCID: https://orcid.org/0000-0001-7198-9766)
- Bangkang Fu (ORCID: https://orcid.org/0000-0001-7647-3371)
- Zhuxue Zhang (ORCID: https://orcid.org/0000-0001-6899-7095)
- Jianguo G. Zhu (ORCID: https://orcid.org/0000-0002-9763-4047)
- Zhen Liu (ORCID: https://orcid.org/0000-0001-9360-269X)
- Yan Zhang (ORCID: https://orcid.org/0000-0001-8400-8859)
- Junjie He (ORCID: https://orcid.org/0000-0002-8037-2829)
- Rongpin Wang (ORCID: https://orcid.org/0000-0001-7587-4181)
- Xinhuan Sun
- Jiahui Mao (ORCID: https://orcid.org/0009-0005-6402-2641)
Institutions
- University of Shanghai for Science and Technology (CN)
- Guiyang Medical University (CN)
- Guizhou University (CN)
- Chinese Academy of Sciences (CN)
- Suzhou Institute of Biomedical Engineering and Technology (CN)
- Beijing Institute of Big Data Research (CN)
- Affiliated Hospital of Guizhou Medical University (CN)
- Guizhou Provincial People's Hospital (CN)
Publication Details
- Journal
- Computer Methods and Programs in Biomedicine
- Published
- 2025-05-06
- DOI
- https://doi.org/10.1016/j.cmpb.2025.108836
- Citations
- 3
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 4.08