ORIGINAL RESEARCH article
Front. Oncol.
Sec. Cancer Epidemiology and Prevention
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1587520
This article is part of the Research TopicAdvanced Machine Learning Techniques in Cancer Prognosis and ScreeningView all 4 articles
A novel bivariate nomogram for predicting sequela vaginal lesions after surgery in patients with HPV-associated cervical cancer
Provisionally accepted
- 1Fudan University, Shanghai, Shanghai Municipality, China
- 2Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
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Objective: To study and predict the risk of further vaginal lesion 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 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. 70% of the patients 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 receiver operator curve (AUC) was screened out in the testing cohort. The nomogram and its calibration curve presented the risk of sequela vaginal lesion. R 4.2.0 software was used for all data processing. Results: Within five years after surgery, 29.94% patients remained persistently infected with HPV, with annual rate fluctuating around 22%. Besides, 10.2% patients were diagnosed with vaginal intraepithelial neoplasia (VaIN), and 320 cases (78.35%) were low-grade squamous intraepithelial lesions (LSIL). The annual incidence of vaginal lesion decreased gradually from 6.97% at first year (Y1) to 2.96% at year 5 (Y5). Ovarian preservation (OP) during hysterectomy and histology as adenocarcinoma were found to be protective from further vaginal lesions, while elder age, FIGO stage II, and positive vaginal incision margin were significant risk factors. As for persistent HPV infection, both single genotype and multiple genotype remarkably increased the risk of vaginal lesion, and α-9 HPV infection (OR=18.20) brought higher risk than non α-9 HPV (OR=11.76). Then we built three predictive models and multiple logistic regression was optional with its AUC at 0.7955 in ROC curve. Conclusion: The predictive model constructed in our study could identify high-risk population of vaginal lesions and precisely guide our clinical interventions.
Keywords: Vaginal intraepithelial neoplasia, Vaginal carcinoma, Persistent HPV infection, cervical cancer, predictive models
Received: 04 Mar 2025; Accepted: 29 Jul 2025.
Copyright: © 2025 Liu, Shi, Yunqiang, Hua and Ding. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Zhang Yunqiang, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
Keqin Hua, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
Jingxin Ding, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
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