BackgroundTumor grade is associated with the treatment and prognosis of endometrial cancer (EC). The accurate preoperative prediction of the tumor grade is essential for EC risk stratification. Herein, we aimed to assess the performance of a multiparametric magnetic resonance imaging (MRI)-based radiomics nomogram for predicting high-grade EC.MethodsOne hundred and forty-three patients with EC who had undergone preoperative pelvic MRI were retrospectively enrolled and divided into a training set (n =100) and a validation set (n =43). Radiomic features were extracted based on T2-weighted, diffusion-weighted, and dynamic contrast-enhanced T1-weighted images. The minimum absolute contraction selection operator (LASSO) was implemented to obtain optimal radiomics features and build the rad-score. Multivariate logistic regression analysis was used to determine the clinical MRI features and build a clinical model. We developed a radiomics nomogram by combining important clinical MRI features and rad-score. A receiver operating characteristic (ROC) curve was used to evaluate the performance of the three models. The clinical net benefit of the nomogram was assessed using decision curve analysis (DCA), net reclassification index (NRI), and integrated discrimination index (IDI).ResultsIn total, 35/143 patients had high-grade EC and 108 had low-grade EC. The areas under the ROC curves of the clinical model, rad-score, and radiomics nomogram were 0.837 (95% confidence interval [CI]: 0.754–0.920), 0.875 (95% CI: 0.797–0.952), and 0.923 (95% CI: 0.869–0.977) for the training set; 0.857 (95% CI: 0.741–0.973), 0.785 (95% CI: 0.592–0.979), and 0.914 (95% CI: 0.827–0.996) for the validation set, respectively. The radiomics nomogram showed a good net benefit according to the DCA. NRIs were 0.637 (0.214–1.061) and 0.657 (0.079–1.394), and IDIs were 0.115 (0.077–0.306) and 0.053 (0.027–0.357) in the training set and validation set, respectively.ConclusionThe radiomics nomogram based on multiparametric MRI can predict the tumor grade of EC before surgery and yield a higher performance than that of dilation and curettage.

Background: It is important for biopsy formal endometrial cancer patients, especially young patients of childbearing age to determine the preservation of fertility and predict pathological escalation. Purpose: This study's goal was to determine the viability and effectiveness of a non-invasive quantitative imaging evaluation model built using the Diffusion Weighted Image (DWI) technique and based on Radiomics signatures and clinical parameters Analysis to evaluate Endometrial Cancer (EC) with Biopsy-Proven Pathologic Upgrading. Method: From January 2018 to December 2021, a total of 76 patients with endometrial cancer who had undergone surgery for the disease were retrospectively recruited (training cohort, n = 53; validation cohort, n = 23). The diffusion-weighted image (DWI) served as the source for the Radiomics features. All images were imported into 3D-slicer for whole tumor Segmentation and were used for radiomics feature extraction. Radiomic features were selected in target tumor volumes to build Radscore using the least absolute shrinkage and selection operator (LASSO) and Cox regression analysis Logistic regression, Next building a combined model incorporating rad-scores and clinical risk factors, compared with Radscore model, the clinical model. The models were evaluated by the receiver operating characteristic curve, and calibration curve as well as verified the model in the verification group. Results: AUC for identifying non-pathologic upgrading and pathologic upgrading in the training cohort was 0.606 and in the validation cohort was 0.708, Three of the 107 texture feature were retrieved and 3 parameters were preserved to create the Radscore. With the incorporation of clinical risk factors, the nomogram's AUC for the training and validation cohorts were 0.870 and 0.808, respectively. Both values were significantly higher than the AUC of the clinical model in these cohorts (0.830 and 0.815). The nomogram's training cohort and validation cohort's sensitivity and specificity were 0.938, 0.730, 0.900, and 0.769, respectively. The calibration curves for the nomogram had a good agreement. Conclusions: The Nomogram based on the Radiomics-clinical model in predicting Pathologic Upgrading in Biopsy-Proven Endometrial Cancer with high discriminatory ability.