Artificial intelligence predicts multiclass molecular signatures and subtypes directly from breast cancer histology: a multicenter retrospective study.

Journal: International journal of surgery (London, England)
Published Date: Apr 1, 2025

Abstract

Detection of biomarkers of breast cancer incurs additional costs and tissue burden. We propose a deep learning-based algorithm (BBMIL) to predict classical biomarkers, immunotherapy-associated gene signatures, and prognosis-associated subtypes directly from hematoxylin and eosin stained histopathology images. BBMIL showed the best performance among comparative algorithms on the prediction of classical biomarkers, immunotherapy related gene signatures, and subtypes.

Authors

  • Xiangyang Zhang
    School of Pharmaceutical Science and Technology, Tianjin University, Tianjin, China.
  • Yang Chen
    Orthopedics Department of the First Affiliated Hospital of Tsinghua University, Beijing, China.
  • Changjing Cai
    Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China.
  • Yifeng Wang
    School of Electrical Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
  • Jun Tan
    School of Mathematics, Sun Yat-Sen University, Guangzhou, Guangdong, China.
  • Zijie Fang
  • Le Wei
    Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, United States.
  • Zhuchen Shao
  • Liwen Wang
    Xiangya School of Medicine, Central South University, Changsha, China.
  • Tiezheng Qi
    Xiangya School of Medicine, Central South University, Changsha, China.
  • Yihan Liu
    Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM) Nanjing Tech University (NanjingTech) 30 South Puzhu Road Nanjing 211816 P. R. China.
  • Zhaohui Jiang
    Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China.
  • Yin Li
    Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, People's Republic of China.
  • Ying Han
    Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China.
  • Tibera Kagemulo Rugambwa
    Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China.
  • Shan Zeng
    School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430024, China.
  • Haoqian Wang
  • Hong Shen
  • Yongbing Zhang
    Tsinghua Univ. Shenzhen International Graduate School, China.

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