Deep learning-based prediction of HER2 status and trastuzumab treatment efficacy of gastric adenocarcinoma based on morphological features.

Journal: Journal of translational medicine
PMID: 39762854

Abstract

BACKGROUND: First-line treatment for advanced gastric adenocarcinoma (GAC) with human epidermal growth factor receptor 2 (HER2) is trastuzumab combined with chemotherapy. In clinical practice, HER2 positivity is identified through immunohistochemistry (IHC) or fluorescence in situ hybridization (FISH), whereas deep learning (DL) can predict HER2 status based on tumor histopathological features. However, it remains uncertain whether these deep learning-derived features can predict the efficacy of anti-HER2 therapy.

Authors

  • Zhida Wu
  • Tao Wang
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Junlin Lan
    College of Physics and Information Engineering, Fuzhou University, Xueyuan Road No.2, Fuzhou, Fujian, 350108, China.
  • Jianchao Wang
    Division of Radiation Research, Department of Radiology, New Jersey Medical School, Rutgers University, Newark, New Jersey.
  • Gang Chen
    Department of Orthopedics, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
  • Tong Tong
    CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
  • Hejun Zhang
    Department of Pathology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, China. 38617347@qq.com.

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