Combining clinical characteristics with CT radiomics to predict Ki67 expression level of small renal mass based on artificial intelligence algorithms
An artificial intelligence-based predictive model for non-invasively assessing the Ki67 expression level of SRMs was developed, thus providing valuable reference for clinical decision-making in these patients.
Authors
- Mingli Luo
- Yangming Ou (ORCID: https://orcid.org/0000-0002-7726-6208)
- Xiangqian Jiang (ORCID: https://orcid.org/0000-0001-7949-8507)
- Guohua Zeng (ORCID: https://orcid.org/0000-0002-2411-8164)
- Shuai-hong CEN
- Junyi Lin (ORCID: https://orcid.org/0000-0002-7499-6723)
Institutions
- Sun Yat-sen University (CN)
- Sun Yat-sen Memorial Hospital (CN)
- First Affiliated Hospital of Guangzhou Medical University (CN)
- Guangzhou Medical University (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-02-21
- DOI
- https://doi.org/10.3389/fonc.2025.1541143
- Citations
- 3
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 4.08