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

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

Abstract Background Cryosectioned tissues often exhibit artifacts that compromise pathologists’ diagnostic accuracy during intraoperative assessments. These inconsistencies, compounded by variations in frozen section (FS) production across laboratories, highlight the need for improved diagnostic too...

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Bibliographic Details
Main Authors: Xue Chao, Yu Wu, Xi Cai, Jiehua He, Chengyou Zheng, Mei Li, Rongzhen Luo, Lijuan Song, Xiaoqin Li, Wentai Feng, Shuoyu Xu, Peng Sun
Format: Article
Language:English
Published: BMC 2025-06-01
Series:Breast Cancer Research
Subjects:
Frozen section
Formalin-fixed
Paraffin-embedded
Deep learning
Breast lesions
Online Access:https://doi.org/10.1186/s13058-025-02064-z
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https://doi.org/10.1186/s13058-025-02064-z

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