Early prediction of neoadjuvant therapy response in breast cancer using MRI-based neural networks: data from the ACRIN 6698 trial and a prospective Chinese cohort.

Journal: Breast cancer research : BCR
PMID: 40181457

Abstract

BACKGROUND: Early prediction of treatment response to neoadjuvant therapy (NAT) in breast cancer patients can facilitate timely adjustment of treatment regimens. We aimed to develop and validate a MRI-based enhanced self-attention network (MESN) for predicting pathological complete response (pCR) based on longitudinal images at the early stage of NAT.

Authors

  • Siyao Du
    Department of Radiology, The First Hospital of China Medical University, Shenyang, 110001, Liaoning Province, China.
  • Wanfang Xie
    School of Engineering Medicine, Beihang University, Beijing, 100191, China.
  • Si Gao
    Department of Radiology, The First Hospital of China Medical University, Shenyang, 110001, Liaoning Province, China.
  • Ruimeng Zhao
    Department of Radiology, The First Hospital of China Medical University, Shenyang, 110001, Liaoning Province, China.
  • Huidong Wang
    College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, P. R. China.
  • Jie Tian
    CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
  • Jiangang Liu
    School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China.
  • Zhenyu Liu
    School of Electronic Information, Hangzhou Dianzi University, Hangzhou 310018, China.
  • Lina Zhang
    Intensive Care Unit, XiangYa Hospital, Central South University, Changsha, China.

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