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Instant diagnosis of gastroscopic biopsy via deep-learned single-shot femtosecond stimulated Raman histology

Author

Listed:
  • Zhijie Liu

    (Fudan University)

  • Wei Su

    (Zhongshan Hospital, Fudan University)

  • Jianpeng Ao

    (Fudan University)

  • Min Wang

    (Shanghai Jiaotong University)

  • Qiuli Jiang

    (Fudan University)

  • Jie He

    (Fudan University)

  • Hua Gao

    (Fudan University)

  • Shu Lei

    (Wuhan No. 1 Hospital)

  • Jinshan Nie

    (Soochow University)

  • Xuefeng Yan

    (Shangrao Municipal Hospital)

  • Xiaojing Guo

    (Naval Medical University)

  • Pinghong Zhou

    (Zhongshan Hospital, Fudan University)

  • Hao Hu

    (Zhongshan Hospital, Fudan University
    People’s Hospital of Shigatse)

  • Minbiao Ji

    (Fudan University)

Abstract

Gastroscopic biopsy provides the only effective method for gastric cancer diagnosis, but the gold standard histopathology is time-consuming and incompatible with gastroscopy. Conventional stimulated Raman scattering (SRS) microscopy has shown promise in label-free diagnosis on human tissues, yet it requires the tuning of picosecond lasers to achieve chemical specificity at the cost of time and complexity. Here, we demonstrate that single-shot femtosecond SRS (femto-SRS) reaches the maximum speed and sensitivity with preserved chemical resolution by integrating with U-Net. Fresh gastroscopic biopsy is imaged in 96%. We further demonstrate semantic segmentation of intratumor heterogeneity and evaluation of resection margins of endoscopic submucosal dissection (ESD) tissues to simulate rapid and automated intraoperative diagnosis. Our method holds potential for synchronizing gastroscopy and histopathological diagnosis.

Suggested Citation

  • Zhijie Liu & Wei Su & Jianpeng Ao & Min Wang & Qiuli Jiang & Jie He & Hua Gao & Shu Lei & Jinshan Nie & Xuefeng Yan & Xiaojing Guo & Pinghong Zhou & Hao Hu & Minbiao Ji, 2022. "Instant diagnosis of gastroscopic biopsy via deep-learned single-shot femtosecond stimulated Raman histology," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-31339-8
    DOI: 10.1038/s41467-022-31339-8
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    References listed on IDEAS

    as
    1. Zhigang Song & Shuangmei Zou & Weixun Zhou & Yong Huang & Liwei Shao & Jing Yuan & Xiangnan Gou & Wei Jin & Zhanbo Wang & Xin Chen & Xiaohui Ding & Jinhong Liu & Chunkai Yu & Calvin Ku & Cancheng Liu , 2020. "Clinically applicable histopathological diagnosis system for gastric cancer detection using deep learning," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    2. Feng Tian & Wenlong Yang & Daniel A. Mordes & Jin-Yuan Wang & Johnny S. Salameh & Joanie Mok & Jeannie Chew & Aarti Sharma & Ester Leno-Duran & Satomi Suzuki-Uematsu & Naoki Suzuki & Steve S. Han & Fa, 2016. "Monitoring peripheral nerve degeneration in ALS by label-free stimulated Raman scattering imaging," Nature Communications, Nature, vol. 7(1), pages 1-15, December.
    3. Jianpeng Ao & Xiaofeng Fang & Xianchong Miao & Jiwei Ling & Hyunchul Kang & Sungnam Park & Changfeng Wu & Minbiao Ji, 2021. "Switchable stimulated Raman scattering microscopy with photochromic vibrational probes," Nature Communications, Nature, vol. 12(1), pages 1-8, December.
    4. Sebastian Karpf & Matthias Eibl & Wolfgang Wieser & Thomas Klein & Robert Huber, 2015. "A Time-Encoded Technique for fibre-based hyperspectral broadband stimulated Raman microscopy," Nature Communications, Nature, vol. 6(1), pages 1-6, November.
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