Deep learning-based risk stratification of ductal carcinoma in situ using mammography and abbreviated breast magnetic resonance imaging
Deep Learning-driven multimodal models enable precise ductal carcinoma <i>in situ</i> risk stratification, addressing overtreatment challenges. abbreviated Magnetic Resonance Imaging achieves diagnostic parity with full diagnostic protocol, positioning Magnetic Resonance Imaging as a viable ductal carcinoma <i>in situ</i> screening modality.
Authors
- Jintao Hu (ORCID: https://orcid.org/0000-0003-3577-6944)
- Tingfeng Zhang (ORCID: https://orcid.org/0000-0003-4578-4729)
- Zhenjie Cao (ORCID: https://orcid.org/0000-0002-9250-4040)
- Jie Ma (ORCID: https://orcid.org/0000-0003-3676-5139)
- Tingting Cui
Institutions
- Chinese University of Hong Kong (HK)
- University Town of Shenzhen (CN)
- Prince of Wales Hospital (CN)
- South China University of Technology (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-06-24
- DOI
- https://doi.org/10.3389/fonc.2025.1587882
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Natural Science Foundation of Guangdong Province