Intra-tumor and peritumoral radiomics and deep learning based on ultrasound for differentiating fibroadenoma and phyllodes tumor: a multicenter study
Integrating intra-tumoral, peritumoral (8mm), and clinical US radiomics features enables highly accurate non-invasive differentiation of FA and PT and stratification of PT subtypes. This approach reduces diagnostic ambiguity in Bi-RADS 4 lesions and decreases unnecessary biopsies, demonstrating significant clinical utility for precision diagnosis of breast fibroepithelial tumors.
Authors
- Ronghui Tian
- Wei Yang (ORCID: https://orcid.org/0000-0001-7245-4579)
- Jingjing Liang (ORCID: https://orcid.org/0000-0002-4991-6517)
- Guoxiu Lu (ORCID: https://orcid.org/0000-0001-5085-6163)
- Guoxu Zhang (ORCID: https://orcid.org/0009-0005-1156-165X)
- Dongmei Liu (ORCID: https://orcid.org/0000-0003-1645-3998)
- Wenjing Chen (ORCID: https://orcid.org/0009-0004-0167-125X)
- Peng Qi (ORCID: https://orcid.org/0000-0002-5048-9604)
- Shanhu Hao
Institutions
- Shenyang University of Technology (CN)
- General Hospital of Shenyang Military Region (CN)
- United Imaging Healthcare (China) (CN)
- Beijing Shijitan Hospital (CN)
- Liaoning Cancer Hospital & Institute (CN)
- China Medical University (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-10-23
- DOI
- https://doi.org/10.3389/fonc.2025.1668793
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- China Railway