CT radiomics-based explainable machine learning model for accurate differentiation of malignant and benign endometrial tumors: a two-center study
CT radiomics-based explainable ML model achieved high diagnostic performance, which could be used as an intelligent auxiliary tool for the diagnosis of endometrial cancer.
Authors
- Chang Liu (ORCID: https://orcid.org/0000-0002-7353-0251)
- Zekun Jiang (ORCID: https://orcid.org/0000-0002-3178-7761)
- Tingrui Zhang (ORCID: https://orcid.org/0000-0002-4090-3605)
- J. Cao (ORCID: https://orcid.org/0000-0002-9590-244X)
- Hongyuhang Ni (ORCID: https://orcid.org/0000-0002-8019-7813)
- Hao Wu (ORCID: https://orcid.org/0000-0003-1269-7354)
- Yingying Wang (ORCID: https://orcid.org/0009-0004-9713-3180)
- Xiaoli Sun (ORCID: https://orcid.org/0000-0002-7286-0435)
- Rui Ye (ORCID: https://orcid.org/0000-0001-6992-8737)
- Rong Shao (ORCID: https://orcid.org/0000-0002-9854-4545)
- Xiaorong Wei (ORCID: https://orcid.org/0000-0002-0359-0339)
- Yingchun Sun
Institutions
- Qingdao University (CN)
- Sichuan University (CN)
- Sichuan Agricultural University (CN)
- Affiliated Hospital of Qingdao University (CN)
- Maternal and Child Health Hospital of Sichuan Province (CN)
- Jimo District Qingdao Hospital of Traditional Chinese Medicine (CN)
- Qingdao Women and Children's Hospital (CN)
Publication Details
- Journal
- BioMedical Engineering OnLine
- Published
- 2025-11-04
- DOI
- https://doi.org/10.1186/s12938-025-01462-w
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.72