A novel bivariate nomogram for predicting sequela vaginal lesions after surgery in patients with HPV-associated cervical cancer.

ABSTRACT:

Objective

To study and predict the risk of further vaginal lesions after surgery in patients with HPV-associated cervical cancer.

Methods

Medical records of women who underwent surgery for cervical cancer between January 2018 and December 2022 at the Obstetrics and Gynecology Hospital of Fudan University were analyzed. Incidence and genotype of persistent HPV infection were recorded and patients with further vaginal lesions were analyzed for clinicopathological risk factors. Of the patients, 70% were randomly grouped into a training cohort, and predictive prognostic models for vaginal lesions were constructed through machine learning. The model with the highest area under the receiver operator curve (AUC) was screened out in the testing cohort. The nomogram and its calibrati

SUBMITTER: Liu Y 

PROVIDER: S-EPMC12510852 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature