Prediction of tumor grade in endometrioid carcinoma using a deep learning radiomics model from ultrasound images: a multicenter study
The DLR model based on TVUS images shows potential value for the non-invasive differentiation of EEC tumor grading and provides a useful supplement for non-invasive clinical staging of endometrial carcinoma prior to surgery.
Authors
- Sijia Chen (ORCID: https://orcid.org/0000-0003-0541-1012)
- Xiaomin Hu
- Mengyao Xiao
- Weihan Xiao
- Xiachuan Qin (ORCID: https://orcid.org/0000-0002-8669-3308)
- Fanding He
- Xiaoling Liu
- Hong Luo
- Jianfeng Li
- Jing Qiao
- Wenhao Li
- Qi Luo
- Lin Li
- Qinfeng Wang
- Xiang Gao
- Weiwei Yin
- Sihui Deng
- Chaoxue Zhang
Institutions
- Xihua University (CN)
- North Sichuan Medical University (CN)
- University of Electronic Science and Technology of China (CN)
- Anhui Medical University (CN)
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Peking Union Medical College Hospital (CN)
- Suizhou Central Hospital (CN)
- People’s Hospital of Rizhao (CN)
- Wuhu Fourth People Hospital (CN)
- Chengdu Military General Hospital (CN)
- Affiliated Hospital of North Sichuan Medical College (CN)
- First Affiliated Hospital of Chengdu Medical College (CN)
- Chengdu Second People's Hospital (CN)
- First Affiliated Hospital of Anhui Medical University (CN)
- Xian Yang Central Hospital (CN)
- Community Health Center (US)
- Nanchong Central Hospital (CN)
Publication Details
- Journal
- Quantitative Imaging in Medicine and Surgery
- Published
- 2026-02-13
- DOI
- https://doi.org/10.21037/qims-2025-1932
- Primary Topic
- Endometrial and Cervical Cancer Treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.00