ORIGINAL RESEARCH article
Front. Public Health
Sec. Radiation and Health
Volume 13 - 2025 | doi: 10.3389/fpubh.2025.1585481
This article is part of the Research TopicAdvances in Radiation Research and Applications: Biology, Environment and MedicineView all 10 articles
Study on the Relationship Between Vaginal Dose and Radiation-Induced Vaginal Injury Following Cervical Cancer Radiotherapy, and Model Development
Provisionally accepted
- 1Gansu Provincial Maternal and Child Health Hospital, Lanzhou, China
- 2Cloud Gansu Technology Co., Ltd., Lanzhou, Gansu Province, China
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Objective: This study investigates the relationship between vaginal radiation dose and radiation-induced vaginal injury in cervical cancer patients, with the aim of developing a risk prediction model to support personalized treatment strategies. Methods: A retrospective analysis was performed on the clinical data of 66 cervical cancer patients treated between December 2022 and December 2023. The Synthetic Minority Over-sampling Technique (SMOTE) was employed for data augmentation. Univariate and multivariate analyses were conducted to identify key factors influencing radiation-induced vaginal injury, and five distinct algorithms were applied to develop predictive models. The AUC/ROC metric was used to assess the performance of the models. Results: Univariate analysis revealed significant associations between the posteriorinferior border of the symphysis (PIBS) point dose and brachytherapy dose with radiation-induced vaginal injury (P < 0.05). Multivariate analysis confirmed PIBS point dose, brachytherapy dose, age, external beam radiation dose, and vaginal involvement as significant factors. A neural network algorithm was chosen to construct the radiation-induced vaginal injury model, which was subsequently developed into an online tool.The developed predictive model can assess the risk of radiation-induced vaginal injury, thereby facilitating the development of individualized radiotherapy plans.
Keywords: cervical cancer, Radiotherapy, Vaginal dose, Radiation-Induced Vaginal Injury, deep learning
Received: 28 Feb 2025; Accepted: 28 Apr 2025.
Copyright: © 2025 Kong, Yan, Gao, Long, Wang and Zheng. 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: Zhangcai Zheng, Gansu Provincial Maternal and Child Health Hospital, Lanzhou, China
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