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Establishment and validation of a risk prediction model for high-grade cervical lesions

  • Binyue Sheng
    Affiliations
    Department of Gynaecology, Maternal and Child Health Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology, Hongshan, Wuhan, Hubei 430070, PR China
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  • Dongmei Yao
    Correspondence
    Corresponding author.
    Affiliations
    Department of Gynaecology, Maternal and Child Health Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology, Hongshan, Wuhan, Hubei 430070, PR China
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  • Xin Du
    Affiliations
    Department of Gynaecology, Maternal and Child Health Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology, Hongshan, Wuhan, Hubei 430070, PR China
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  • Dejun Chen
    Affiliations
    Department of Gynaecology, Maternal and Child Health Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology, Hongshan, Wuhan, Hubei 430070, PR China
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  • Limin Zhou
    Affiliations
    Department of Gynaecology, Maternal and Child Health Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology, Hongshan, Wuhan, Hubei 430070, PR China
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Published:December 06, 2022DOI:https://doi.org/10.1016/j.ejogrb.2022.12.005

      Abstract

      Objective

      To establish and validate a risk prediction model for cervical high-grade squamous intraepithelial lesions (HSIL).

      Methods

      This retrospective study included patients who underwent cervical biopsies at the Cervical Disease Centre of Maternal and Child Hospital of Hubei Province between January 2021 and December 2021.

      Results

      A total of 1630 patients were divided into the HSIL + cervical lesion group (n = 186) and the ≤ LSIL cervical lesions group (n = 1444). LSIL, ASC-H, HSIL and SCC, high-risk HPV, HPV16, HPV18/45, multiple HPV strains, acetowhite epithelium, atypical vessels, and mosaicity were independently associated with HSIL + lesions. These factors were used to establish a risk prediction model with a demonstrated area under the curve (AUC) of 0.851 and a C-index of 0.829. Calibration curve analysis showed that the model performed well, with a mean absolute error (MAE) of 0.005. The decision curve showed that the model created by combining the risk factors was more specific and sensitive than each predictive variable.

      Conclusion

      The model for predicting HSIL demonstrated promising predictive capability and might help identify patients requiring biopsy and treatment.

      Abbreviations:

      CIN (cervical intraepithelial neoplasias), HPV (human papilloma virus), TCT (ThinPrep cytological test), NILM (negative for intraepithelial lesion or malignancy), ASC-US (atypical cytology of undetermined significance), LSIL (low-grade squamous intraepithelial lesion), HSIL (high-grade squamous intraepithelial lesion), ASC-H (atypical squamous cells cannot exclude high-grade), SCC (squamous cell carcinoma), TCT (Thinprep cytologic test), SCJ (squamocolumnar junction), TBS (the Bethesda System), AGC (abnormal blood glandular cells), AIS (situ adenocarcinoma), ACC (adenocarcinoma), ROC (receiver operating characteristic), AUC (area under the curve), MAE (mean absolute error)

      Keywords

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