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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
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    References listed on IDEAS

    as
    1. Shenghua Cheng & Sibo Liu & Jingya Yu & Gong Rao & Yuwei Xiao & Wei Han & Wenjie Zhu & Xiaohua Lv & Ning Li & Jing Cai & Zehua Wang & Xi Feng & Fei Yang & Xiebo Geng & Jiabo Ma & Xu Li & Ziquan Wei & , 2021. "Robust whole slide image analysis for cervical cancer screening using deep learning," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    2. Xiaohui Zhu & Xiaoming Li & Kokhaur Ong & Wenli Zhang & Wencai Li & Longjie Li & David Young & Yongjian Su & Bin Shang & Linggan Peng & Wei Xiong & Yunke Liu & Wenting Liao & Jingjing Xu & Feifei Wang, 2021. "Hybrid AI-assistive diagnostic model permits rapid TBS classification of cervical liquid-based thin-layer cell smears," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
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