CT-based AI framework leveraging multi-scale features for predicting pathological grade and Ki67 index in clear cell renal cell carcinoma: a multicenter study
Non-invasively determining pathological grade and Ki67 index in ccRCC could guide treatment decisions. The AI framework integrates segmentation, classification, and model interpretation, enabling fully automated analysis. The AI framework enables non-invasive preoperative detection of high-risk tumors, assisting clinical decision-making.
Authors
- Haoyang Zeng (ORCID: https://orcid.org/0000-0003-1057-2865)
- Zhaochen Ye
- Zexin Huang (ORCID: https://orcid.org/0000-0003-3835-7163)
- Hanlin Liu (ORCID: https://orcid.org/0000-0002-2673-5289)
- Kai Wu (ORCID: https://orcid.org/0000-0002-4437-9468)
- Fan Li (ORCID: https://orcid.org/0000-0002-7464-3098)
- Xiang Ye (ORCID: https://orcid.org/0000-0002-8694-8710)
- Yueyue Zhang (ORCID: https://orcid.org/0000-0003-4403-6534)
- Weihao Liu (ORCID: https://orcid.org/0000-0001-5084-5624)
- Yangguang Yuan
- Huancheng Yang
- Hong Yuan (ORCID: https://orcid.org/0000-0002-9452-2189)
Institutions
- Sun Yat-sen University (CN)
- Shenzhen University (CN)
- Shantou University (CN)
- Soochow University (CN)
- Mianyang Third People's Hospital (CN)
- Sixth Affiliated Hospital of Sun Yat-sen University (CN)
- Shantou University Medical College (CN)
- Second Affiliated Hospital of Soochow University (CN)
- Shenzhen Luohu People's Hospital (CN)
Publication Details
- Journal
- Insights into Imaging
- Published
- 2025-05-14
- DOI
- https://doi.org/10.1186/s13244-025-01980-0
- Citations
- 3
- Primary Topic
- Renal cell carcinoma treatment
- Type
- article
- Field-Weighted Citation Impact
- 4.56
Funders
- Soochow University
- Second Affiliated Hospital of Soochow University
- Basic and Applied Basic Research Foundation of Guangdong Province