Predicting breast cancer response to neoadjuvant therapy by integrating radiomic and deep-learning features from early-and-peak phases of DCE-MRI
Integrating traditional radiomics and 3D deep learning features from different phases of DCE-MRI can accurately predict pCR to NAT in breast cancer accurately. Multi-phase imaging and diverse features are important to improve predictive accuracy and the constructed model may help guide personalized treatment strategies.
Authors
- Shouliang Qi
- Yabin Zhang (ORCID: https://orcid.org/0000-0003-3380-2870)
- Chunxiao Cui (ORCID: https://orcid.org/0000-0001-5900-7256)
- Dan Zhao (ORCID: https://orcid.org/0000-0003-4320-1366)
- Jiumei Cai
Institutions
- Qingdao University (CN)
- Affiliated Hospital of Qingdao University (CN)
- Liaoning Cancer Hospital & Institute (CN)
- China Medical University (CN)
- Northeastern University (CN)
Publication Details
- Journal
- BMC Cancer
- Published
- 2025-11-11
- DOI
- https://doi.org/10.1186/s12885-025-15095-8
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.72
Funders
- Fundamental Research Funds for the Central Universities