Exploratory multi-cohort, multi-reader study on the clinical utility of a deep learning model for transforming cryosectioned to formalin-fixed, paraffin-embedded (FFPE) images in breast lesion diagnosis
The AI-FFPE model improved perceived image quality and diagnostic confidence among pathologists in breast lesion evaluations. While diagnostic concordance remained comparable, the enhanced interpretability of AI-FFPE images may support more confident intraoperative decision-making.
Authors
- Peng Sun (ORCID: https://orcid.org/0000-0003-3811-0371)
- Yu Wu (ORCID: https://orcid.org/0000-0002-9964-4898)
- Lijuan Song (ORCID: https://orcid.org/0000-0002-7816-2902)
- Chengyou Zheng
- Mei Li (ORCID: https://orcid.org/0000-0001-8634-2252)
- Xiaoqin Li (ORCID: https://orcid.org/0000-0002-7761-5834)
- Chao Xue (ORCID: https://orcid.org/0009-0005-2400-3972)
- Wen Feng
- Xi Cai (ORCID: https://orcid.org/0000-0001-9563-6703)
- Shuoyu Xu
- Jiehua He
- Rongzhen Luo
Institutions
- Sun Yat-sen University (CN)
- Gansu Provincial Hospital (CN)
- Suzhou Research Institute (CN)
- Sun Yat-sen University Cancer Center (CN)
- State Key Laboratory of Oncology in South China
Publication Details
- Journal
- Breast Cancer Research
- Published
- 2025-06-17
- DOI
- https://doi.org/10.1186/s13058-025-02064-z
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Guangdong Province