Application of a nomogram model based on cervical cancer radiomics and clinical features in the treatment of chronic radiation enteritis
10.3760/cma.j.cn112271-20240913-000349
- VernacularTitle:基于宫颈癌影像组学和临床特征的列线图模型在慢性放射性肠炎中的应用
- Author:
Liyang ZHU
1
;
Zhengting REN
;
Shuhao PAN
;
Ping LI
;
Xiangxun CHEN
;
Yin LYU
Author Information
1. 安徽医科大学第一附属医院放疗科，合肥 230022
- Publication Type:Journal Article
- Keywords:
Cervical cancer;
Intensity-modulated radiotherapy;
Radiation enteritis;
Radiomics;
Radscore
- From:
Chinese Journal of Radiological Medicine and Protection
2025;45(8):803-809
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To predict the occurrence of chronic radiation enteritis (CRE) in cervical cancer patients by developing a prediction model based on the combination of radiomic features derived from magnetic resonance imaging (MRI) scans and clinical parameters, in order to provide a reference for clinicians to determine the prognosis of these patients and offer them individualized diagnosis and treatment.Methods:A retrospective analysis was conducted on 111 cervical cancer patients who received radical radiotherapy at the First Affiliated Hospital of Anhui Medical University. Radiological features were extracted from the T1-weighted MRI images of local lesions of cervical cancer obtained before the radiotherapy. Features were selected using the least absolute shrinkage and selection operator (LASSO) to obtain the radiomics score. The radiomics scores and clinical parameters were assessed using univariate and multivariate logistic regression analyses, followed by the establishment of nomograms. The ability of radiomics to achieve CRE prediction was assessed using the area under the curve (AUC) and the calibration and decision curves.Results:Multivariate logistic regression analysis result revealed that the independent risk factors for identifying CRE in patients included radiomics score ( HR: 17.457, 95% CI: 5.540-55.009, P<0.001), tumor volume ( HR: 3.617, 95% CI: 1.293-10.115, P=0.014), and pelvic lymph node metastasis ( HR: 3.559, 95% CI: 1.013-12.501, P=0.048). The model combining radiomics and clinical data demonstrated high performance, with its AUCs of the training and validation groups (0.888 and 0.870, respectively) higher than those of the radiomics model (0.842 and 0.804, respectively) and the clinical data model (0.721 and 0.704, respectively). The analyses of calibration and decision curves confirmed the application value of clinical radiomic nomograms. Conclusions:The model combining radiomics and clinical data allows for accurate CRE prediction. Therefore, radiomic features have the potential to serve as a promising imaging biomarker for CRE.
