A combined model integrating deep learning, radiomics, and clinical ultrasound features for predicting BRAF V600E mutation in papillary thyroid carcinoma with Hashimoto’s thyroiditis
The combined model integrating radiomics, deep learning features, and clinical as well as ultrasound characteristics exhibits excellent diagnostic performance in predicting <i>BRAF V600E</i> mutations in patients with PTC coexisting with HT, highlighting its strong potential for clinical application.
Authors
- Pengfei Zhu (ORCID: https://orcid.org/0000-0002-4310-9140)
- Pu Zhou (ORCID: https://orcid.org/0000-0002-7277-0798)
- Hao Wang (ORCID: https://orcid.org/0000-0002-5438-1211)
- Ying He
- Xin‐Wu Cui (ORCID: https://orcid.org/0000-0003-3890-6660)
- Shu‐E Zeng
- Xiaofeng Zhang (ORCID: https://orcid.org/0000-0003-2738-3286)
- Jiangyuan Ben
Institutions
- Nantong Tumor Hospital (CN)
- First Affiliated Hospital of Xinxiang Medical University (CN)
- Tongji Hospital (CN)
- Hubei Cancer Hospital (CN)
- Huazhong University of Science and Technology (CN)
- Henan Medical University (CN)
Publication Details
- Journal
- Frontiers in Endocrinology
- Published
- 2025-08-18
- DOI
- https://doi.org/10.3389/fendo.2025.1641037
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Science and Technology Project of Nantong City