Development of a machine learning-based model for predicting positive margins in high-grade squamous intraepithelial lesion (HSIL) treatment by Cold Knife Conization(CKC): a single-center retrospective study.

Journal: BMC women's health
PMID: 38849836

Abstract

OBJECTIVES: This study aims to analyze factors associated with positive surgical margins following cold knife conization (CKC) in patients with cervical high-grade squamous intraepithelial lesion (HSIL) and to develop a machine-learning-based risk prediction model.

Authors

  • Lin Zhang
    Laboratory of Molecular Translational Medicine, Centre for Translational Medicine, Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, Clinical Research Center for Birth Defects of Sichuan Province, West China Second Hospital, Sichuan University, Chengdu, Sichuan, 610041, China. Electronic address: zhanglin@scu.edu.cn.
  • Yahong Zheng
    Department of Obstetrics and Gynecology, The First Affiliated Hospital of Yangtze University, Shashi District, 8 Hangkong Road, Jingzhou, Hubei, China.
  • Lingyu Lei
    Department of Obstetrics and Gynecology, The First Affiliated Hospital of Yangtze University, Shashi District, 8 Hangkong Road, Jingzhou, Hubei, China.
  • Xufeng Zhang
    Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China.
  • Jing Yang
    Beijing Novartis Pharma Co. Ltd., Beijing, China.
  • Yong Zeng
    a College of Pharmacy , Chengdu University of Traditional Chinese Medicine , Chengdu , P.R. China.
  • Keming Chen
    Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, 519000, Guangdong Province, China.

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