Next-generation sequencing based deep learning model for prediction of HER2 status and response to HER2-targeted neoadjuvant chemotherapy.

Journal: Journal of cancer research and clinical oncology
PMID: 39923208

Abstract

INTRODUCTION: For patients with breast cancer, the amplification of Human Epidermal Growth Factor 2 (HER2) is closely related to their prognosis and treatment decisions. This study aimed to further improve the accuracy and efficiency of HER2 amplification status detection with a deep learning model, and apply the model to predict the efficacy of neoadjuvant therapy.

Authors

  • Jia Wang
    Institute of Special Animal and Plant Sciences, Chinese Academy of Agricultural Sciences, Changchun, Jilin, China.
  • Ge Gao
    School of Computer Science and Artificial Intelligence, Aliyun School of Big Data, School of Software, Changzhou University, Changzhou 213000, China.
  • Cong Tian
    School of Computer Science and Technology, Xidian University, No. 2 South Taibai Road, Xi'an, 710071, PR China. Electronic address: ctian@mail.xidian.edu.cn.
  • Jiao Zhang
  • De-Chuang Jiao
    Department of Breast Disease, Henan Breast Cancer Center, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, 127 Dongming Road, Zhengzhou, 450008, China.
  • Zhen-Zhen Liu
    Department of Breast Disease, Henan Breast Cancer Center, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, 127 Dongming Road, Zhengzhou, 450008, China. zlyyliuzhenzhen0800@zzu.edu.cn.

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