Development and validation of an interpretable ensemble model for predicting androgen receptor status in triple-negative breast cancer: a multi-center study
An interpretable ensemble model integrating radiomics and multiparametric MRI achieved robust and generalizable performance for AR status prediction in TNBC. This noninvasive approach may assist in patient stratification for AR-targeted therapy and support personalized treatment strategies.
Authors
- Yongliang Liu (ORCID: https://orcid.org/0000-0002-1492-8374)
- Chang Shao
- Lixiu Cao
- Yanna Shan
- Zhi Li
- Mei Ruan
- Wen Xu
Institutions
- Westlake University (CN)
- Tangshan People's Hospital (CN)
- Affiliated Hangzhou First People's Hospital, Westlake University, School of Medicine (CN)
- First Affiliated Hospital Zhejiang University (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2026-03-11
- DOI
- https://doi.org/10.3389/fonc.2026.1743315
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Medical Science and Technology Project of Zhejiang Province
- Science and Technology Program of Zhejiang Province