Machine learning prediction of HER2-low expression in breast cancers based on hematoxylin-eosin-stained slides.

Journal: Breast cancer research : BCR
PMID: 40251691

Abstract

BACKGROUND: Treatment with HER2-targeted therapies is recommended for HER2-positive breast cancer patients with HER2 gene amplification or protein overexpression. Interestingly, recent clinical trials of novel HER2-targeted therapies demonstrated promising efficacy in HER2-low breast cancers, raising the prospect of including a HER2-low category (immunohistochemistry, IHC) score of 1 + or 2 + with non-amplified in-situ hybridization for HER2-targeted treatments, which necessitated the accurate detection and evaluation of HER2 expression in tumors. Traditionally, HER2 protein levels are routinely assessed by IHC in clinical practice, which not only requires significant time consumption and financial investment but is also technically challenging for many basic hospitals in developing countries. Therefore, directly predicting HER2 expression by hematoxylin-eosin (HE) staining should be of significant clinical values, and machine learning may be a potent technology to achieve this goal.

Authors

  • Jun Du
    Department of Gastrointestinal Surgery, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu 214062, P.R. China.
  • Jun Shi
    School of Communication and Information Engineering, Shanghai University, Shanghai, China. Electronic address: junshi@staff.shu.edu.cn.
  • Dongdong Sun
    School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, PR China.
  • Yifei Wang
    Department of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Guanfeng Liu
    School of Computing, Macquarie University, Sydney, Australia.
  • Jingru Chen
    First Affiliated Hospital of Chongqing University of Chinese Medicine, Chongqing University of Chinese Medicine, Chongqing 400021, PR China; Department of Rheumatology, Chongqing Hospital of Traditional Chinese Medicine, Chongqing 400021, PR China.
  • Wei Wang
    State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau 999078, China.
  • Wenchao Zhou
    Beijing Computing Center, Beijing, China.
  • Yushan Zheng
    Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, China; Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing 100191, China.
  • Haibo Wu
    Department of Pathology, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230036, Anhui Province, China; Intelligent Pathology Institute, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230036, Anhui Province, China.

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