Development and Validation of a Nomogram Based on Multiparametric MRI for Predicting Lymph Node Metastasis in Endometrial Cancer: A Retrospective Cohort Study

Rationale and Objectives: To develop a radiomics nomogram based on clinical and magnetic resonance features to predict lymph node metastasis (LNM) in endometrial cancer (EC). Materials and Methods: We retrospectively collected 308 patients with endometrial cancer (EC) from two centers. These patients were divided into a training set (n = 155), a test set (n = 67), and an external validation set (n = 86). Based on T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) arterial phase and equilibrium phase images, radiomics features were extracted. Clinical characteristics were determined using multivariate logistic regression analysis. Subsequently, eight machine learning classification algorithms were employed to construct the radiomics model and clinical models, from which the best algorithm was selected. Ultimately, the radiomics and clinical features were combined to establish the radiomics nomogram. The efficacy of each model was appraised through receiver operating characteristic (ROC), calibration curve, and decision curve analysis (DCA). Results: The LR algorithm demonstrated superior predictive accuracy, with areas under the curve (AUCs) of 0.903 and 0.824 in the test and validation sets, respectively. Radiomics nomograms showed better predictive performance compared to clinical models or radiomics models, the AUCs in the test and external validation set were 0.900 (95% confidence interval [CI]: 0.784-1.000) and 0.858 (95%CI: 0.750-0.966), respectively. The calibration curve and DCA indicated that the nomogram had excellent predictive performance. Conclusion: The nomogram based on radiomics features and clinical parameters could effectively predict LNM in patients with EC, thus providing a basis for clinicians to develop individualized treatment plans preoperatively. 2024 The Association of Academic Radiology. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.