Binary and Ternary Classification Prediction for Breast Cancer and Breast Sclerosing Adenosis With Interpretable Artificial Intelligence From Clinical and Imaging Features: A Retrospective, Diagnostic Accuracy Cohort Study
Given the substantial risk of coexisting of SA with DCIS or IBC, and the potential for CNB to underestimate disease due to limited sampling, lesions diagnosed as SA on CNB should be evaluated with additional modalities before determining the need for surgical excision. The proposed interpretable AI model enhances discrimination between SA-only and SA with concomitant breast cancer (SA + BC), thereby supporting more informed clinical decision-making in breast disease management.
Authors
- Yi Qu (ORCID: https://orcid.org/0000-0002-9993-8810)
- Wen Xu (ORCID: https://orcid.org/0000-0002-1308-9732)
- Tianli Liu
- Futian Weng (ORCID: https://orcid.org/0000-0002-7982-8729)
- L. Kong
- Jiahui Zhang (ORCID: https://orcid.org/0009-0008-4087-8674)
- Bo Pan (ORCID: https://orcid.org/0000-0002-5792-9158)
- Jing Qin (ORCID: https://orcid.org/0000-0002-6029-7673)
- Jie Lian (ORCID: https://orcid.org/0000-0002-7237-7347)
- X Ren
- Zhe Wang
- Xu Yang
- Wenbo Li
- Qiang Sun
- Yidong Zhou
- Yan Xu
- Ying Xu
- Ming Wang
Institutions
- Xiamen University (CN)
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Peking Union Medical College Hospital (CN)
- Xiamen National Accounting Institute (CN)
- Ocean University of China (CN)
- Xiamen University of Technology (CN)
Publication Details
- Journal
- Cancer Innovation
- Published
- 2026-02-01
- DOI
- https://doi.org/10.1002/cai2.70049
- Primary Topic
- Breast Lesions and Carcinomas
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Chinese Academy of Medical Sciences
- Peking Union Medical College Hospital