Attention-based multimodal fusion transformer for predicting the efficacy of neoadjuvant therapy in breast cancer: a cross-institutional retrospective study
By fusing pathology and radiology features, MuFi improves decision reliability and identifies critical multimodal predictors. This integration framework better captures patient heterogeneity, supporting personalized NAC decision-making through improved accuracy and generalizability.
Authors
- Xun‐Xi Lu (ORCID: https://orcid.org/0000-0002-5118-0377)
- Yuhao Yi (ORCID: https://orcid.org/0000-0001-6036-0276)
- Wenchuan Zhang (ORCID: https://orcid.org/0000-0001-9632-6424)
- Hong Bu
- Shuwan Zhang
- Xiaoyan Wu (ORCID: https://orcid.org/0000-0002-8054-4109)
- Qingjie Lv (ORCID: https://orcid.org/0000-0002-6135-0368)
- Jae‐Seek You (ORCID: https://orcid.org/0000-0001-7638-9583)
- Juan Huang (ORCID: https://orcid.org/0000-0003-4080-7928)
- Fengling Li (ORCID: https://orcid.org/0009-0005-6934-7365)
Institutions
- Sichuan University (CN)
- Guangzhou University (CN)
- West China Hospital of Sichuan University (CN)
- University of Hong Kong (HK)
- China Medical University (CN)
Publication Details
- Journal
- Breast Cancer Research
- Published
- 2025-12-06
- DOI
- https://doi.org/10.1186/s13058-025-02181-9
- Primary Topic
- Breast Cancer Treatment Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00