Non-contrast MRI Radiomics Nomogram for Predicting Recurrence and Progression-free Survival in Cervical Cancer following Concurrent Chemoradiotherapy
6 affiliations
- 1Department of Gynecologic Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Hai Dian District, Beijing 100142, China
- 2Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Hai Dian District, Beijing 100142, China
- 3Graduate Group in Applied Mathematics and Computational Science, University of Pennsylvania, Philadelphia, PA 19104, United States
- 4Department of Psychology, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, 100875, Beijing, China
- 5School of Artificial Intelligence, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, 100875, Beijing, China
- 6Department of Radiotherapy Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Hai Dian District, Beijing 100142, China
Abstract
Background: Accurate prognostic stratification following concurrent chemoradiotherapy (CCRT) for cervical cancer remains challenging. Existing approaches often rely on contrast-enhanced magnetic resonance imaging (MRI), which limits applicability in patients with renal dysfunction and in resource-limited settings. We aimed to develop and validate a non-contrast MRI-based prognosis prediction model combined with clinical predictors to estimate recurrence risk and progression-free survival (PFS).
Materials and Methods: A total of 145 cervical cancer patients from two centers were retrospectively analyzed. The model-building cohort (n=114) was used to extract radiomics features from pre-treatment MRI, and an extreme gradient boosting classifier was developed. Clinical predictors were identified through univariate Cox and multivariate logistic regression analyses. A nomogram integrating radiomics and clinical variables was then constructed. External validation was performed in an independent cohort (n=31). Model performance was assessed by discrimination, calibration, and its ability to stratify patients into low- and high-risk groups for predicting 5-year PFS.
Results: The proposed method categorized patients into well-defined risk groups. Survival results varied greatly among groups. Kaplan–Meier analysis indicated that patients with high risk had a worse 5-year PFS compared to those with low risk. This result was observed in both the construction cohort (P < 0.001) and the external validation cohort (P = 0.012). The prognostic performance was comparable to that of contrast-enhanced MRI–based models. A significant point to note is that no gadolinium administration was necessary.
Discussion: This study develops a radiomics nomogram that relies on non-contrast MRI. The model offers strong and tailored prognostic information. Imaging features and clinical predictors combined, which improved predictive accuracy. The approach increases accessibility, particularly for patients contraindicated for contrast or in low-resource settings.
Conclusion: We developed and validated a non-contrast MRI radiomics nomogram that accurately predicts recurrence and 5-year PFS following CCRT in cervical cancer. The model offers a clinically applicable, cost-effective tool for personalized risk stratification.
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BibTeX
@article{wang2026noncontrast,
title = {{Non-contrast MRI Radiomics Nomogram for Predicting Recurrence and Progression-free Survival in Cervical Cancer following Concurrent Chemoradiotherapy}},
author = {Wei Wang and Xiaoting Li and Yueqi Jiang and Yiheng Lu and Min Gao and Yunong Gao and Yun Tian and Jing You and Xiaofan Li and Kun Cao and Hong Zheng},
journal = {Current Medical Imaging},
year = {2026},
month = jul,
doi = {10.2174/0115734056449836260717192214},
url = {https://doi.org/10.2174/0115734056449836260717192214},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Non-contrast MRI Radiomics Nomogram for Predicting Recurrence and Progression-free Survival in Cervical Cancer following Concurrent Chemoradiotherapy
AU - Wei Wang
AU - Xiaoting Li
AU - Yueqi Jiang
AU - Yiheng Lu
AU - Min Gao
AU - Yunong Gao
AU - Yun Tian
AU - Jing You
AU - Xiaofan Li
AU - Kun Cao
AU - Hong Zheng
PY - 2026
DA - 2026/07/23
JO - Current Medical Imaging
DO - 10.2174/0115734056449836260717192214
UR - https://doi.org/10.2174/0115734056449836260717192214
AB - Background: Accurate prognostic stratification following concurrent chemoradiotherapy (CCRT) for cervical cancer remains challenging. Existing approaches often rely on contrast-enhanced magnetic resonance imaging (MRI), which limits applicability in patients with renal dysfunction and in resource-limited settings. We aimed to develop and validate a non-contrast MRI-based prognosis prediction model combined with clinical predictors to estimate recurrence risk and progression-free survival (PFS). Materials and Methods: A total of 145 cervical cancer patients from two centers were retrospectively analyzed. The model-building cohort (n=114) was used to extract radiomics features from pre-treatment MRI, and an extreme gradient boosting classifier was developed. Clinical predictors were identified through univariate Cox and multivariate logistic regression analyses. A nomogram integrating radiomics and clinical variables was then constructed. External validation was performed in an independent cohort (n=31). Model performance was assessed by discrimination, calibration, and its ability to stratify patients into low- and high-risk groups for predicting 5-year PFS. Results: The proposed method categorized patients into well-defined risk groups. Survival results varied greatly among groups. Kaplan–Meier analysis indicated that patients with high risk had a worse 5-year PFS compared to those with low risk. This result was observed in both the construction cohort (P < 0.001) and the external validation cohort (P = 0.012). The prognostic performance was comparable to that of contrast-enhanced MRI–based models. A significant point to note is that no gadolinium administration was necessary. Discussion: This study develops a radiomics nomogram that relies on non-contrast MRI. The model offers strong and tailored prognostic information. Imaging features and clinical predictors combined, which improved predictive accuracy. The approach increases accessibility, particularly for patients contraindicated for contrast or in low-resource settings. Conclusion: We developed and validated a non-contrast MRI radiomics nomogram that accurately predicts recurrence and 5-year PFS following CCRT in cervical cancer. The model offers a clinically applicable, cost-effective tool for personalized risk stratification.
SN - 1573-4056
ER -