Noninvasive identification of HER2-low-positive status by MRI-based deep learning radiomics predicts the disease-free survival of patients with breast cancer.

Journal: European radiology
Published Date: Aug 19, 2023

Abstract

OBJECTIVE: This study aimed to establish a MRI-based deep learning radiomics (DLR) signature to predict the human epidermal growth factor receptor 2 (HER2)-low-positive status and further verified the difference in prognosis by the DLR model.

Authors

  • Yuan Guo
    Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.
  • Xiaotong Xie
    School of Life Science, South China Normal University, Guangzhou, 510631, China.
  • Wenjie Tang
    Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, 510180, China.
  • Siyi Chen
    Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, 510180, China.
  • Mingyu Wang
  • Yaheng Fan
    From the Departments of Radiology (M. Li, C.L., A.L., L.Z., Jinhui Zhou, D.Z., H.C., Y.X., J.W.) and Pathology (Jing Zhou), The Third Affiliated Hospital, Sun Yat-Sen University, No. 600 Tianhe Rd, Guangzhou, Guangdong, 510630, People's Republic of China; Medical AI Laboratory, School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen, People's Republic of China (Y.F., B.H.); Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, People's Republic of China (H.Y.); Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, People's Republic of China (M. Luo); and Department of Clinical Science, Philips Healthcare China, Shanghai, People's Republic of China (X.Y., W.D., Z.Z.).
  • Chuxuan Lin
    Medical AI Lab, School of Biomedical Engineering, Health Science Centre, Shenzhen University, Shenzhen, China.
  • Wenke Hu
    Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, 510180, China.
  • Jing Yang
    Beijing Novartis Pharma Co. Ltd., Beijing, China.
  • Jialin Xiang
    Department of Radiology, Guangdong Women and Children Hospital, Guangzhou, Guangdong, 510010, China.
  • Kuiming Jiang
    Department of Radiology, Guangdong Women and Children Hospital, Guangzhou, Guangdong, 510010, China. kmjiang65@sina.com.
  • Xinhua Wei
    Department of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou 510180, China.
  • Bingsheng Huang
    School of Biomedical Engineering, Shenzhen University Health Sciences Center, Shenzhen, Guangdong 518060, P.R.China.
  • Xinqing Jiang
    Department of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou 510180, China.

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