Rapid, label-free histopathological diagnosis of liver cancer based on Raman spectroscopy and deep learning

Liping Huang, Hongwei Sun, Liangbin Sun, Keqing Shi, Yuzhe Chen, Xueqian Ren, Yuancai Ge, Danfeng Jiang, Xiaohu Liu, Wolfgang Knoll, Qingwen Zhang () and Yi Wang ()
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Liping Huang: Wenzhou Medical University
Hongwei Sun: The First Affiliated Hospital of Wenzhou Medical University
Liangbin Sun: Wenzhou Medical University
Keqing Shi: The First Affiliated Hospital of Wenzhou Medical University
Yuzhe Chen: Wenzhou Medical University
Xueqian Ren: Wenzhou Medical University
Yuancai Ge: Wenzhou Medical University
Danfeng Jiang: University of Chinese Academy of Sciences
Xiaohu Liu: Wenzhou Medical University
Wolfgang Knoll: Austrian Institute of Technology
Qingwen Zhang: University of Chinese Academy of Sciences
Yi Wang: Wenzhou Medical University

Nature Communications, 2023, vol. 14, issue 1, 1-14

Abstract: Abstract Biopsy is the recommended standard for pathological diagnosis of liver carcinoma. However, this method usually requires sectioning and staining, and well-trained pathologists to interpret tissue images. Here, we utilize Raman spectroscopy to study human hepatic tissue samples, developing and validating a workflow for in vitro and intraoperative pathological diagnosis of liver cancer. We distinguish carcinoma tissues from adjacent non-tumour tissues in a rapid, non-disruptive, and label-free manner by using Raman spectroscopy combined with deep learning, which is validated by tissue metabolomics. This technique allows for detailed pathological identification of the cancer tissues, including subtype, differentiation grade, and tumour stage. 2D/3D Raman images of unprocessed human tissue slices with submicrometric resolution are also acquired based on visualization of molecular composition, which could assist in tumour boundary recognition and clinicopathologic diagnosis. Lastly, the potential for a portable handheld Raman system is illustrated during surgery for real-time intraoperative human liver cancer diagnosis.

Date: 2023
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DOI: 10.1038/s41467-022-35696-2

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