Journal
NATURE COMMUNICATIONS
Volume 14, Issue 1, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41467-022-35696-2
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This study utilizes Raman spectroscopy combined with deep learning to develop a rapid, non-disruptive, and label-free method for pathological diagnosis of liver cancer. The technique allows for detailed pathological identification of cancer tissues and provides high-resolution tissue images for tumor boundary recognition and clinicopathological diagnosis.
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 aworkflow for in vitro and intraoperative pathological diagnosis of liver cancer. We distinguish carcinoma tissues fromadjacent 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.
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