Multiparametric MRI-based radiomics and deep learning for differentiating uterine serous carcinoma from endometrioid carcinoma: a multicenter retrospective study
The integrated model, combining multiparametric MRI-based radiomics, deep learning features, and clinical-radiological characteristics, may be utilized for the preoperative differentiation of USC from EEC.
Authors
- Shaoxian Chen
- Yi Shen (ORCID: https://orcid.org/0000-0002-3198-1927)
- Xiaohui Duan
- Zhuozhi Dai (ORCID: https://orcid.org/0000-0001-8971-8681)
- Xiaohua Ban (ORCID: https://orcid.org/0009-0000-2322-9092)
- Daiying Lin (ORCID: https://orcid.org/0000-0002-7792-9753)
- Liping Liu (ORCID: https://orcid.org/0009-0002-2086-1978)
- Shuang Ma (ORCID: https://orcid.org/0000-0002-9870-5492)
- Kainan Huang
- Shaofan Lin
Institutions
- Sun Yat-sen University (CN)
- Shantou University (CN)
- Cancer Hospital of Shantou University Medical College (CN)
- Sun Yat-sen Memorial Hospital (CN)
- Shantou Central Hospital (CN)
- Sun Yat-sen University Cancer Center (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-10-08
- DOI
- https://doi.org/10.3389/fonc.2025.1655384
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Shantou University
- Sun Yat-sen University
- Guangdong Medical Research Foundation
- Shantou University Medical College
- Sun Yat-sen University Cancer Center