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.

Journal: Breast cancer research : BCR
Published Date: Jun 17, 2025

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 tools. This study aims to develop and validate a deep-learning model that transforms cryosectioned images into formalin-fixed paraffin-embedded (FFPE) images to enhance diagnostic performance in breast lesions.

Authors

  • Xue Chao
    State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, P. R. China.
  • Yu Wu
    Key Laboratory of Pesticide and Chemical Biology of Ministry of Education, International Joint Research Center for Intelligent Biosensing Technology and Health, College of Chemistry, Central China Normal University, Wuhan, 430079, People's Republic of China.
  • Xi Cai
    State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, P. R. China.
  • Jiehua He
    State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, P. R. China.
  • Chengyou Zheng
    State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, P. R. China.
  • Mei Li
    Department of Laboratory Medicine, Med+X Center for Manufacturing, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China.
  • Rongzhen Luo
    Department of Pathology, Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, 510060, Guangzhou, China.
  • Lijuan Song
    Department of Cardiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325000, P.R. China.
  • Xiaoqin Li
    Department of Pulmonary and Critical Care Medicine, Fujian Provincial Hospital, Fuzhou, China.
  • Wentai Feng
    Bio-totem Pte Ltd, Suzhou, P. R. China.
  • Shuoyu Xu
  • Peng Sun
    Department of Microelectronics, Nankai University, Tianjin, 300350, PR China.

Keywords

External Resources

Popular Topics
Recent Journals