Prediction of BRAF and TERT status in PTCs by machine learning-based ultrasound radiomics methods: A multicenter study
The machine learning-based US radiomics methods, integrated with clinical characteristics, demonstrated effectiveness in predicting the BRAF V600E and TERT promoter mutations in PTCs.
Authors
- Hui Shi (ORCID: https://orcid.org/0000-0003-1524-4553)
- Yaojun Zhang (ORCID: https://orcid.org/0000-0002-9752-4729)
- Li Fan Wang
- Chong Zhao (ORCID: https://orcid.org/0000-0002-9655-6454)
- Jialin Zheng (ORCID: https://orcid.org/0000-0003-2286-0151)
- Liping Sun (ORCID: https://orcid.org/0000-0003-2089-610X)
- Ting Wu (ORCID: https://orcid.org/0000-0003-2073-758X)
- Xiuyan Yang (ORCID: https://orcid.org/0000-0002-0270-3346)
- Ke Ding (ORCID: https://orcid.org/0000-0002-0476-2710)
- Hui Xu (ORCID: https://orcid.org/0000-0003-0322-183X)
- Boyang Zhou
Institutions
- Tongji University (CN)
- Fudan University (CN)
- Zhongshan Hospital (CN)
- Shanghai Tenth People's Hospital (CN)
- United Imaging Healthcare (China) (CN)
- Shanghai First People's Hospital (CN)
- Bayer (China) (CN)
- First Affiliated Hospital of Zhengzhou University (CN)
Publication Details
- Journal
- Journal of Clinical & Translational Endocrinology
- Published
- 2025-04-02
- DOI
- https://doi.org/10.1016/j.jcte.2025.100390
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36