IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v16y2025i1d10.1038_s41467-025-58883-3.html
   My bibliography  Save this article

Deep learning enabled liquid-based cytology model for cervical precancer and cancer detection

Author

Listed:
  • Peng Xue

    (Chinese Academy of Medical Sciences and Peking Union Medical College
    Chinese Academy of Medical Sciences and Peking Union Medical College)

  • Le Dang

    (Chinese Academy of Medical Sciences and Peking Union Medical College)

  • Ling-Hua Kong

    (Chinese Academy of Medical Sciences and Peking Union Medical College)

  • Hong-Ping Tang

    (Shenzhen Maternity and Child Healthcare Hospital)

  • Hai-Miao Xu

    (Zhejiang Cancer Center)

  • Hai-Yan Weng

    (University of Science and Technology of China)

  • Zhe Wang

    (Fourth Military Medical University)

  • Rong-Gan Wei

    (Guangxi Zhuang Autonomous Region People’s Hospital)

  • Lian Xu

    (Sichuan University)

  • Hong-Xia Li

    (The Seventh Medical Center of Chinese PLA General Hospital)

  • Hai-Yan Niu

    (Hainan Medical University)

  • Ming-Juan Wang

    (Northwest Women’s and Children’s Hospital)

  • Zi-Chen Ye

    (Chinese Academy of Medical Sciences and Peking Union Medical College)

  • Zhi-Fang Li

    (Changzhi Medical College)

  • Wen Chen

    (Chinese Academy of Medical Sciences and Peking Union Medical College)

  • Qin-Jing Pan

    (Chinese Academy of Medical Sciences and Peking Union Medical College)

  • Xun Zhang

    (Chinese Academy of Medical Sciences and Peking Union Medical College)

  • Remila Rezhake

    (The Affiliated Cancer Hospital of Xinjiang Medical University)

  • Li Zhang

    (Chinese Academy of Medical Sciences and Peking Union Medical College)

  • Yu Jiang

    (Chinese Academy of Medical Sciences and Peking Union Medical College)

  • You-Lin Qiao

    (Chinese Academy of Medical Sciences and Peking Union Medical College
    Chinese Academy of Medical Sciences and Peking Union Medical College)

  • Lan Zhu

    (Chinese Academy of Medical Sciences and Peking Union Medical College)

  • Fang-Hui Zhao

    (Chinese Academy of Medical Sciences and Peking Union Medical College)

Abstract

Deep learning (DL) enabled liquid-based cytology has potential for cervical cancer screening or triage. Here, we develop a DL model using whole cytology slides from 17,397 women and test it on 10,826 additional cases through a three-stage process. The DL model achieves robust performance across nine hospitals. In a multi-reader, multi-case study, it outperforms cytopathologists’ sensitivity by 9%. Reading time significantly decreases with DL assistance (218s vs 30s; p 0.999), yet it has reduced specificity (0.831 vs 0.901; p

Suggested Citation

  • Peng Xue & Le Dang & Ling-Hua Kong & Hong-Ping Tang & Hai-Miao Xu & Hai-Yan Weng & Zhe Wang & Rong-Gan Wei & Lian Xu & Hong-Xia Li & Hai-Yan Niu & Ming-Juan Wang & Zi-Chen Ye & Zhi-Fang Li & Wen Chen , 2025. "Deep learning enabled liquid-based cytology model for cervical precancer and cancer detection," Nature Communications, Nature, vol. 16(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-58883-3
    DOI: 10.1038/s41467-025-58883-3
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-025-58883-3
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-025-58883-3?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
    ---><---

    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:16:y:2025:i:1:d:10.1038_s41467-025-58883-3. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.