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Updated: Aug 2, 2025
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Published on: July 5, 2021
A nomogram for preoperative risk stratification based on MRI morphological parameters in patients with endometrioid
Bin Yan1, Zhihao Li2, Ying Deng3
1Department of Medical Imaging, First Affiliated Hospital of Xi'an Jiaotong University, No.277, West Yanta Road, 710061, People's Republic of China; Department of Radiology, Shaanxi Provincial Tumor Hospital, Xi'an Jiaotong University, No.309, West Yanta Road, 710061, People's Republic of China.
Purpose:
To develop and validate a nomogram based on MRI morphological parameters to preoperatively discriminate between low-risk and non-low-risk patients with endometrioid endometrial carcinoma (EEC).
Methods:
Two hundred eighty-one women with histologically confirmed EEC were divided into training (1.5-T MRI, n = 182) and validation cohorts (3.0-T MRI, n = 99). According to the European Society of Medical Oncology guidelines, the patients were divided into four risk groups: low, intermediate, high-intermediate, and high. Binary classification models were developed (low-risk vs. non-low-risk). Univariate logistic regression (LR) analyses were used to determine which variables to select to build the predictive models. Five classification models were constructed, and the best model was selected. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the performance of the prediction model and nomogram. P < 0.05 indicated a statistically significant difference.
Results:
Age and four morphological parameters (tumor size, tumor volume, maximum anteroposterior tumor diameter on sagittal T2-weighted images (APsag), and tumor area ratio (TAR)) were selected, and the LR model was used to construct an MRI morphological nomogram. The AUCs for the nomogram in predicting a non-low-risk of EEC among patients in the training and validation cohorts were 0.856 (sensitivity = 75.0%, specificity = 83.1%) and 0.849 (sensitivity = 74.6%, specificity = 85.0%), respectively.
Conclusion:
An MRI morphological nomogram was developed and achieved high diagnostic performance for classifying low-risk and non-low-risk EEC preoperatively, which could provide support for therapeutic decision-making. Furthermore, our findings indicate that this nomogram is robust in the clinical application of various field strength data.
