Non-Invasive Measurement Using Deep Learning Algorithm Based on Multi-Source Features Fusion to Predict PD-L1 Expression and Survival in NSCLC.

Journal: Frontiers in immunology
Published Date: Apr 7, 2022

Abstract

BACKGROUND: Programmed death-ligand 1 (PD-L1) assessment of lung cancer in immunohistochemical assays was only approved diagnostic biomarker for immunotherapy. But the tumor proportion score (TPS) of PD-L1 was challenging owing to invasive sampling and intertumoral heterogeneity. There was a strong demand for the development of an artificial intelligence (AI) system to measure PD-L1 expression signature (ES) non-invasively.

Authors

  • Chengdi Wang
    Department of Pulmonary and Critical Care Medicine, Med-X Center for Manufacturing, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.
  • Jiechao Ma
    InferVision, Beijing, 100020, China.
  • Jun Shao
    Department of Pulmonary and Critical Care Medicine, Med-X Center for Manufacturing, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.
  • Shu Zhang
    State University of New York, Department of Radiology, Stony Brook, New York, United States.
  • Jingwei Li
    Department of Pulmonary and Critical Care Medicine, Med-X Center for Manufacturing, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.
  • Junpeng Yan
    AI Lab, Deepwise Healthcare, Beijing, China.
  • Zhehao Zhao
    West China Hospital, West China School of Medicine, Sichuan University, Chengdu, China.
  • Congchen Bai
    Department of Medical Informatics, West China Hospital, Sichuan University, Chengdu, China.
  • Yizhou Yu
    Department of Computer Science, The University of Hong Kong, Pok Fu Lam, Hong Kong.
  • Weimin Li
    Department of Psychiatry, Sleep Medicine Center, Nanfang Hospital, Southern Medical University, Guangzhou, China.

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