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Updated: Mar 27, 2026

Chromogenic In Situ Hybridization as a Tool for HPV-Related Head and Neck Cancer Diagnosis
Published on: June 14, 2019
Development of a Nomogram for Predicting 6-Month HPV Persistence After Cold Knife Conization in Chinese Women with
Qian Zhang1, Xiaomei Tai1, Lihua Jia1
1Department of Obstetrics and Gynecology, Maternal and Child Health Hospital of Tongzhou, Beijing, People's Republic of China.
Purpose:
This study aimed to develop and validate a nomogram to predict the risk of persistent human papillomavirus (HPV) infection after cold knife conization (CKC) in Chinese women with high-grade squamous intraepithelial lesions (HSIL).
Methods:
A single-center retrospective study included 476 HPV-positive patients with HSIL who underwent CKC between January 1 2020 and December 31 2023. Patients were randomly divided into a training cohort (n=333) and a validation cohort (n=143) in a 7:3 ratio. Univariate and multivariate logistic regression analyses were used to identify predictors of HPV persistence after CKC, which was defined as the presence of the same HPV genotype both before and 6 months after surgical intervention. A nomogram was constructed using the independent risk factors. The validation of the prognostic model was evaluated by concordance index (C-index), calibration curves, and decision curve analyses (DCAs).
Results:
HPV persistence was detected in 120 patients (25.21%) at 6 months post-CKC. Multivariate analysis revealed that the factors associated with HPV persistence after CKC were age, menopausal status, and number of types of HPV infections. The nomogram was constructed based on the above 3 factors. The nomogram demonstrated moderate and acceptable discrimination (AUC=0.700; 95% CI: 0.606-0.794, Youden index = 0.30, sensitivity = 0.636, specificity = 0.664), good calibration, and positive net benefit in the validation cohort.
Conclusion:
We successfully developed the first nomogram to predict the risk of HPV persistence at 6 months after CKC in Chinese women with HSIL using risk factors of age, menopausal status, and number of types of HPV infections, with HPV multiplicity being a novel and key predictor.
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