Quantitative Radiological Features and Deep Learning for the Non-Invasive Evaluation of Programmed Death Ligand 1 Expression Levels in Gastric Cancer Patients: A Digital Biopsy Study.

Journal: Academic radiology
Published Date: Jul 1, 2023

Abstract

RATIONALE AND OBJECTIVES: Programmed Death-Ligand 1 (PD-L1) is an important biomarker for patient selection of immunotherapy in gastric cancer (GC). This study aimed to construct and validate a non-invasive virtual biopsy system based on radiological features and clinical factors to predict the PD-L1 expression level in GC.

Authors

  • Wentao Xie
    Department of Biochemistry and Medical Genetics, University of Manitoba, Room 308-Basic Medical Sciences Building, 745 Bannatyne Avenue, Winnipeg, Manitoba R3E 0J9, Canada.
  • Zinian Jiang
    Qingdao Medical Colledge, Qingdao University, 266071, Qingdao, Shandong, China.
  • Xiaoming Zhou
    a Department of Pathology, Guangzhou Medical University, Guangzhou, Guangdong Province, China.
  • Xianxiang Zhang
    Department of General Surgery, Affiliated Hospital of Qingdao University, Qingdao, China.
  • Maoshen Zhang
    Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, 266000, Qingdao, Shandong, China.
  • Ruiqing Liu
    Department of Otorhinolaryngology, Kunming City Women and Children Hospital, Kunming, China.
  • Longbo Zheng
    Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, 266000, Qingdao, Shandong, China.
  • Fangjie Xin
    Department of Pathology, The Affiliated Hospital of Qingdao University, 266000, Qingdao, Shandong, China.
  • Yun Lu
    Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou Province, China.
  • Dongsheng Wang

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