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

• Published in: Nature communications 2023-01, Vol.14 (1), p.48-14, Article 48
• Main Authors: Huang, Liping, Sun, Hongwei, Sun, Liangbin, Shi, Keqing, Chen, Yuzhe, Ren, Xueqian, Ge, Yuancai, Jiang, Danfeng, Liu, Xiaohu, Knoll, Wolfgang, Zhang, Qingwen, Wang, Yi
• Format: Article
• Language: English
• Subjects: 140/133; 631/1647/527/1821; 692/308/575; 692/699/67/1504/1610/4029; Biopsy; Carcinoma, Hepatocellular; Chemical composition; Deep Learning; Diagnosis; Human tissues; Humanities and Social Sciences; Humans; Image acquisition; Liver cancer; Liver Neoplasms - diagnosis; Medical imaging; Metabolomics; multidisciplinary; Portability; Raman spectroscopy; Real time; Science; Science (multidisciplinary); Sectioning; Spectroscopy; Spectrum analysis; Spectrum Analysis, Raman - methods; Staining; Tissues; Tumors; Workflow
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-022-35696-2

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. Biopsy is recommended for definitive diagnosis of liver carcinoma, however, this process requires staining and expert pathologists to confirm diagnosis. Here, the authors employ a portable Raman spectroscopy system combined with deep learning to detect carcinoma from normal tissue in real time.