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

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 () and Minbiao Ji ()
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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
Minbiao Ji: Fudan University

Nature Communications, 2022, vol. 13, issue 1, 1-12

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-31339-8

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