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ORIGINAL RESEARCH article

Front. Oncol.
Sec. Gynecological Oncology
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1523567
This article is part of the Research Topic Optimizing Radiotherapy for Cervical Cancer Efficacy Toxicity and Brachytherapy Integration View all articles

Delta-radiomics analysis based on magnetic resonance imaging to identify radiation proctitis in patients with cervical cancer after radiotherapy

Provisionally accepted
Jing Xue Jing Xue 1Menghan Wu Menghan Wu 1Jing Zhang Jing Zhang 2Jiayang Yang Jiayang Yang 3Guannan Lv Guannan Lv 4Baojun Qu Baojun Qu 4Yanping Zhang Yanping Zhang 4Xia Yan Xia Yan 3*Jianbo Song Jianbo Song 1,3*
  • 1 Third Hospital of Shanxi Medical College, Taiyuan, Shanxi Province, China
  • 2 China Institute for Radiation Protection, Tauyuan, China
  • 3 Shanxi Bethune Hospital, Shanxi Medical University, Taiyuan, Shanxi Province, China
  • 4 Datong Second People's Hospital, Datong, Shanxi Province, China

The final, formatted version of the article will be published soon.

    Objectives: To develop a magnetic resonance imaging (MRI)-based radiomics model for predicting the severity of radiation proctitis (RP) in cervical cancer patients' postradiotherapy.: We retrospectively analyzed clinical data and MRI images from 126 cervical squamous cell carcinoma patients treated with concurrent chemoradiotherapy. Logistic regression (LR), Pearson correlation coefficient, and least absolute shrinkage and selection operator (LASSO) methods were utilized to select optimal imaging features, leading to a combined prediction model developed using a random forest (RF) algorithm. Model performance was assessed using the area under the curve (AUC), DeLong test, calibration curve, and decision curve analysis (DCA), with Shapley Additive exPlanations (SHAP) values for interpretation. Results: The samples were split into training (70%) and validation (30%) sets. The delta-radiomics model, comprising 10 delta features, showed strong predictive performance (AUC: 0.92 for training and 0.90 for validation sets). A comprehensive model combining delta-radiomics with clinical features outperformed this, achieving

    Keywords: cervical cancer, radiation proctitis, Prediction model, Radiomics, deltaradiomics

    Received: 06 Nov 2024; Accepted: 09 Jan 2025.

    Copyright: © 2025 Xue, Wu, Zhang, Yang, Lv, Qu, Zhang, Yan and Song. 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:
    Xia Yan, Shanxi Bethune Hospital, Shanxi Medical University, Taiyuan, 030032, Shanxi Province, China
    Jianbo Song, Shanxi Bethune Hospital, Shanxi Medical University, Taiyuan, 030032, Shanxi Province, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.