IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-022-35696-2.html
   My bibliography  Save this article

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

Author

Listed:
  • Liping Huang

    (Wenzhou Medical University
    University of Chinese Academy of Sciences)

  • 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
    University of Chinese Academy of Sciences)

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.

Suggested Citation

  • Liping Huang & Hongwei Sun & Liangbin Sun & Keqing Shi & Yuzhe Chen & Xueqian Ren & Yuancai Ge & Danfeng Jiang & Xiaohu Liu & Wolfgang Knoll & Qingwen Zhang & Yi Wang, 2023. "Rapid, label-free histopathological diagnosis of liver cancer based on Raman spectroscopy and deep learning," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-022-35696-2
    DOI: 10.1038/s41467-022-35696-2
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-022-35696-2
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-022-35696-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Chi-Sing Ho & Neal Jean & Catherine A. Hogan & Lena Blackmon & Stefanie S. Jeffrey & Mark Holodniy & Niaz Banaei & Amr A. E. Saleh & Stefano Ermon & Jennifer Dionne, 2019. "Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning," Nature Communications, Nature, vol. 10(1), pages 1-8, December.
    2. Laura Poillet-Perez & Xiaoqi Xie & Le Zhan & Yang Yang & Daniel W. Sharp & Zhixian Sherrie Hu & Xiaoyang Su & Anurag Maganti & Cherry Jiang & Wenyun Lu & Haiyan Zheng & Marcus W. Bosenberg & Janice M., 2018. "Autophagy maintains tumour growth through circulating arginine," Nature, Nature, vol. 563(7732), pages 569-573, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Da Chen & Zhaoming Xia & Zhixiong Guo & Wangyan Gou & Junlong Zhao & Xuemei Zhou & Xiaohe Tan & Wenbin Li & Shoujie Zhao & Zhimin Tian & Yongquan Qu, 2023. "Bioinspired porous three-coordinated single-atom Fe nanozyme with oxidase-like activity for tumor visual identification via glutathione," Nature Communications, Nature, vol. 14(1), pages 1-13, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chenglong Sun & Anqiang Wang & Yanhe Zhou & Panpan Chen & Xiangyi Wang & Jianpeng Huang & Jiamin Gao & Xiao Wang & Liebo Shu & Jiawei Lu & Wentao Dai & Zhaode Bu & Jiafu Ji & Jiuming He, 2023. "Spatially resolved multi-omics highlights cell-specific metabolic remodeling and interactions in gastric cancer," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    2. Lucia Taraborrelli & Yasin Şenbabaoğlu & Lifen Wang & Junghyun Lim & Kerrigan Blake & Noelyn Kljavin & Sarah Gierke & Alexis Scherl & James Ziai & Erin McNamara & Mark Owyong & Shilpa Rao & Aslihan Ka, 2023. "Tumor-intrinsic expression of the autophagy gene Atg16l1 suppresses anti-tumor immunity in colorectal cancer," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    3. Alexandre Girard & Anneli Cooper & Samuel Mabbott & Barbara Bradley & Steven Asiala & Lauren Jamieson & Caroline Clucas & Paul Capewell & Francesco Marchesi & Matthew P Gibbins & Franziska Hentzschel , 2021. "Raman spectroscopic analysis of skin as a diagnostic tool for Human African Trypanosomiasis," PLOS Pathogens, Public Library of Science, vol. 17(11), pages 1-28, November.
    4. Alberto Signoroni & Alessandro Ferrari & Stefano Lombardi & Mattia Savardi & Stefania Fontana & Karissa Culbreath, 2023. "Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    5. Hao He & Maofeng Cao & Yun Gao & Peng Zheng & Sen Yan & Jin-Hui Zhong & Lei Wang & Dayong Jin & Bin Ren, 2024. "Noise learning of instruments for high-contrast, high-resolution and fast hyperspectral microscopy and nanoscopy," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    6. Oleksii Ilchenko & Yurii Pilhun & Andrii Kutsyk & Denys Slobodianiuk & Yaman Goksel & Elodie Dumont & Lukas Vaut & Chiara Mazzoni & Lidia Morelli & Sofus Boisen & Konstantinos Stergiou & Yaroslav Auli, 2024. "Optics miniaturization strategy for demanding Raman spectroscopy applications," Nature Communications, Nature, vol. 15(1), pages 1-14, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-022-35696-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.