Rapid visualization of PD-L1 expression level in glioblastoma immune microenvironment via machine learning cascade-based Raman histopathology.

Journal: Journal of advanced research
Published Date: Dec 10, 2023

Abstract

INTRODUCTION: Combination immunotherapy holds promise for improving survival in responsive glioblastoma (GBM) patients. Programmed death-ligand 1 (PD-L1) expression in immune microenvironment (IME) is the most important predictive biomarker for immunotherapy. Due to the heterogeneous distribution of PD-L1, post-operative histopathology fails to accurately capture its expression in residual tumors, making intra-operative diagnosis crucial for GBM treatment strategies. However, the current methods for evaluating the expression of PD-L1 are still time-consuming.

Authors

  • Qing-Qing Zhou
    Department of Radiology, Jinling Hospital, Affiliated Nanjing Medical University, Nanjing, China; Department of Radiology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing, China.
  • Jingxing Guo
    School of Chemistry, Chemical Engineering and Life Sciences, Wuhan University of Technology, Wuhan, China. Electronic address: jxguo@whut.edu.cn.
  • Ziyang Wang
  • Jianrui Li
    Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, China.
  • Meng Chen
    Institute of Industrial and Consumer Product Safety, China Academy of Inspection and Quarantine, Beijing, China.
  • Qiang Xu
    University of Huddersfield, Queensgate, Huddersfield, United Kingdom . Electronic address: Q.Xu2@hud.ac.uk.
  • Lijun Zhu
    State Key Laboratory of Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, 100083, Beijing, China.
  • Qing Xu
    Department of Reproductive Medicine, Zigong Hospital of Women and Children Health Care, Zigong, China.
  • Qiang Wang
    Ningbo Konfoong Bioinformation Tech Co., Ltd, Ningbo, China.
  • Hao Pan
    School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430000, China.
  • Jing Pan
    College of Veterinary Medicine, Gansu Agricultural University, Lanzhou, China.
  • Yong Zhu
    Jiangsu Key Laboratory of Regional Specific Resource Pharmaceutical Transformation, Huaiyin Institute of Technology, Huai'an 223003, Jiangsu, P. R. China.
  • Ming Song
    National Laboratory of Pattern Recognition and Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China.
  • Xiaoxue Liu
    Yunnan Agricultural University, Kunming, China.
  • Jiandong Wang
    Department of Computer Science and Engineering,University of South Carolina, Columbia, 29208, SC, USA.
  • Zhiqiang Zhang
    Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.
  • Longjiang Zhang
    Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, No.305, Zhongshan East Road, Nanjing, 210002, China.
  • Yiqing Wang
    Department of Statistical Science, Southern Methodist University, Dallas, TX 75275, United States. Electronic address: lucy@mail.smu.edu.
  • Huiming Cai
    Department of Biomedical Engineering, College of Engineering and Applied Sciences, State Key Laboratory of Analytical Chemistry for Life Science, Nanjing University, Nanjing, China; Nanjing Nuoyuan Medical Devices Co. Ltd, Nanjing, China.
  • Xiaoyuan Chen
    Laboratory of Molecular Imaging and Nanomedicine, National Institute of Biomedical Imaging and Bioengineering (NIBIB), National Institutes of Health (NIH), Bethesda, MD, 20892, USA.
  • Guangming Lu
    Department of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China.

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