Non-contrast MRI Radiomics Nomogram for Predicting Recurrence and Progression-Free Survival in Cervical Cancer following Concurrent Chemoradiotherapy.
TL;DR
A non-contrast MRI radiomics nomogram that accurately predicts recurrence and 5-year PFS following CCRT in cervical cancer is developed and validated and offers a clinically applicable, cost-effective tool for personalized risk stratification.
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 resourcelimited 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.