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已发表论文
基于机器学习的宫颈上皮内瘤变 II 级及以上预测模型的构建与验证：一项横断面人群研究
Authors He J, Chen KJ , Fang YX , He YF, Xiang L, Wang XM, Zhou ML, Zhou SG , Hu JJ
Received 17 July 2025
Accepted for publication 21 November 2025
Published 5 December 2025 Volume 2025:17 Pages 5195—5208
DOI https://doi.org/10.2147/IJWH.S554552
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Matteo Frigerio
Juan He,1,2,* Kang-Jia Chen,1,3,* Ya-Xing Fang,1,2 Yu-Feng He,1,2 Lan Xiang,1,2 Xue-Mei Wang,4 Ming-Li Zhou,4 Shu-Guang Zhou,1,3 Jing-Jing Hu5
1Department of Gynecology, Maternal and Child Medical Center of Anhui Medical University, Hefei, Anhui, 230032, People’s Republic of China; 2Department of Gynecology, Hefei Maternal and Child Health Hospital, Hefei, Anhui, 230001, People’s Republic of China; 3Department of Gynecology, Anhui Provincial Maternity and Child Healthcare Hospital, Hefei, Anhui, 230051, People’s Republic of China; 4Department of Gynecology, Linquan Maternity and Child Healthcare Hospital, Fuyang, 236400, People’s Republic of China; 5Department of Reproduction, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230032, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Shu-Guang Zhou, Email zhoushuguang@ahmu.edu.cn Jing-Jing Hu, Email hujingjing@ahmu.edu.cn
Background: Cervical cancer, as the leading malignant tumor among women globally, underscores the critical need for early screening; however, effective models for predicting cervical lesions remain lacking.
Objective: To construct a predictive model for cervical intraepithelial neoplasia II+(CINII+), and to compare the predictive performance of machine learning models integrating thinprep cytologic test (TCT) + human papillomavirus (HPV) testing with clinical data versus TCT combined with traditional clinical data for CIN II+.
Methods: Clinical data from women undergoing cervical cancer screening at Linquan Maternity and Child Healthcare Hospital (2020– 2024) were collected, including TCT results, HPV status, cervical pathology, age, sexual history and other clinical data. Ten machine learning algorithms were applied to develop two predictive models: Model 1(TCT+HPV+clinical data) and Model 2(TCT+traditional clinical data). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves and decision curve analysis (DCA).
Results: Multivariate logistic regression analysis showed that HPV positivity, TCT indicates High-Grade Squamous Intraepithelial Lesion(HSIL), colposcopy result indicates a high-grade lesion and the first age of pregnancy as predictors of CINII+. Model 1 (TCT+HPV+clinical data) demonstrated significantly higher predictive efficacy than Model 2(TCT+clinical data), the difference in AUC is statistically significant. (P=0.006 in training set; P=0.035 in testing set).
Conclusion: The TCT+HPV-integrated model outperformed the TCT-only model in predicting CIN II+, supporting the incorporation of HPV testing into routine screening to enhance early diagnostic accuracy.
Keywords: cervical lesions, thinprep cytologic test, TCT, human papillomavirus, HPV, predictive model, machine learning
1Department of Gynecology, Maternal and Child Medical Center of Anhui Medical University, Hefei, Anhui, 230032, People’s Republic of China; 2Department of Gynecology, Hefei Maternal and Child Health Hospital, Hefei, Anhui, 230001, People’s Republic of China; 3Department of Gynecology, Anhui Provincial Maternity and Child Healthcare Hospital, Hefei, Anhui, 230051, People’s Republic of China; 4Department of Gynecology, Linquan Maternity and Child Healthcare Hospital, Fuyang, 236400, People’s Republic of China; 5Department of Reproduction, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230032, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Shu-Guang Zhou, Email zhoushuguang@ahmu.edu.cn Jing-Jing Hu, Email hujingjing@ahmu.edu.cn
Background: Cervical cancer, as the leading malignant tumor among women globally, underscores the critical need for early screening; however, effective models for predicting cervical lesions remain lacking.
Objective: To construct a predictive model for cervical intraepithelial neoplasia II+(CINII+), and to compare the predictive performance of machine learning models integrating thinprep cytologic test (TCT) + human papillomavirus (HPV) testing with clinical data versus TCT combined with traditional clinical data for CIN II+.
Methods: Clinical data from women undergoing cervical cancer screening at Linquan Maternity and Child Healthcare Hospital (2020– 2024) were collected, including TCT results, HPV status, cervical pathology, age, sexual history and other clinical data. Ten machine learning algorithms were applied to develop two predictive models: Model 1(TCT+HPV+clinical data) and Model 2(TCT+traditional clinical data). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves and decision curve analysis (DCA).
Results: Multivariate logistic regression analysis showed that HPV positivity, TCT indicates High-Grade Squamous Intraepithelial Lesion(HSIL), colposcopy result indicates a high-grade lesion and the first age of pregnancy as predictors of CINII+. Model 1 (TCT+HPV+clinical data) demonstrated significantly higher predictive efficacy than Model 2(TCT+clinical data), the difference in AUC is statistically significant. (P=0.006 in training set; P=0.035 in testing set).
Conclusion: The TCT+HPV-integrated model outperformed the TCT-only model in predicting CIN II+, supporting the incorporation of HPV testing into routine screening to enhance early diagnostic accuracy.
Keywords: cervical lesions, thinprep cytologic test, TCT, human papillomavirus, HPV, predictive model, machine learning