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

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, Qin-Jing Pan, Xun Zhang, Remila Rezhake, Li Zhang, Yu Jiang, You-Lin Qiao, Lan Zhu () and Fang-Hui Zhao ()
Additional contact information
Peng Xue: 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
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

Nature Communications, 2025, vol. 16, issue 1, 1-10

Abstract: 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

Date: 2025
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DOI: 10.1038/s41467-025-58883-3

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