A deep learning based holistic diagnosis system for immunohistochemistry interpretation and molecular subtyping.

Journal: Neoplasia (New York, N.Y.)
PMID: 38412576

Abstract

BACKGROUND: Breast cancer in different molecular subtypes, which is determined by the overexpression rates of human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), progesterone receptor (PR), and Ki67, exhibit distinct symptom characteristics and sensitivity to different treatment. The immunohistochemical method, one of the most common detecting tools for tumour markers, is heavily relied on artificial judgment and in clinical practice, with an inherent limitation in interpreting stability and operating efficiency. Here, a holistic intelligent breast tumour diagnosis system has been developed for tumour-markeromic analysis, combining the automatic interpretation and clinical suggestion.

Authors

  • Lin Fan
    Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Jiahe Liu
    School of Integrated Circuit Science and Engineering (Industry-Education Integration School), Nanjing University of Posts and Telecommunications, Nanjing 210023, PR China.
  • Baoyang Ju
    School of Integrated Circuit Science and Engineering (Industry-Education Integration School), Nanjing University of Posts and Telecommunications, Nanjing 210023, PR China.
  • Doudou Lou
    Nanjing Institute for Food and Drug Control, Nanjing, Jiangsu 211198, PR China.
  • Yushen Tian
    School of Electrical Engineering, Shenyang University of Technology, Shenyang, Liaoning 110870, PR China. Electronic address: tianyshen@163.com.

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