Artificial intelligence-based multimodal prediction for nuclear grading status and prognosis of clear cell renal cell carcinoma: a multicenter cohort study
Our novel MPS model demonstrated high accuracy in ISUP grading for ccRCC patients. With further validation across multiple centers, the MPS model could be used for precise detection of nuclear grading in ccRCC, serving as an effective tool for assisting clinical decision-making.
Authors
- Jiejun Wu (ORCID: https://orcid.org/0009-0008-6094-1423)
- Zhiyuan Chen (ORCID: https://orcid.org/0009-0002-4241-7763)
- Qingyuan Zheng (ORCID: https://orcid.org/0000-0003-4682-3857)
- Xinmiao Ni
- Rui Yang (ORCID: https://orcid.org/0000-0001-9126-4957)
- Hailiang Mei (ORCID: https://orcid.org/0000-0003-1781-5508)
- Xiuheng Liu (ORCID: https://orcid.org/0000-0003-3882-2715)
- Panpan Jiao
- Jingping Yuan (ORCID: https://orcid.org/0000-0001-7470-6899)
- Xiaodong Weng (ORCID: https://orcid.org/0000-0001-9774-8493)
- Junjie Fan
- Xiangxiang Yang
Institutions
- Chinese Academy of Sciences (CN)
- Wuhan University (CN)
- Institute of Software (CN)
- Renmin Hospital of Wuhan University (CN)
- University of Chinese Academy of Sciences (CN)
Publication Details
- Journal
- International Journal of Surgery
- Published
- 2025-03-28
- DOI
- https://doi.org/10.1097/js9.0000000000002368
- Citations
- 9
- Primary Topic
- Renal cell carcinoma treatment
- Type
- article
- Field-Weighted Citation Impact
- 13.67