Abstract: Objective To explore the clinical value of a multimodal predictive model based on multiparametric magnetic resonance imaging (MRI) radiomics combined with deep learning (DL) features for the preoperative noninvasive assessment of mismatch repair-deficient (MMRd) status in endometrial cancer (EC). Methods Patients diagnosed with EC at Peking Union Medical College Hospital from January 2015 to December 2021 were retrospectively enrolled and randomly divided into a training set and a validation set at a ratio of 8:2. Relevant clinical data were collected,and radiomics features and DL features were extracted from preoperative contrast-enhanced T1-weighted imaging (CE-T1WI),fat-suppressed T2-weighted imaging (fs-T2WI),and diffusion-weighted imaging (DWI) sequences. High-dimensional feature selection and dimensionality reduction were performed sequentially using the recursive feature elimination (RFE) algorithm to generate a radiomics score (Rad-score) and a deep learning score (DL-score),respectively. Multivariate logistic regression was utilized to construct a clinical model,a pure radiomics model,a clinical-radiomics model,and an integrated multimodal model incorporating clinical indicators,Rad-score,and DL-score. Model performance was assessed and compared using area under receiver operating characteristic curve (AUC) and DeLong test. Results A total of 509 patients were enrolled in this study,comprising 413 in the training cohort and 96 in the validation cohort. Independent predictors:Multivariate analysis indicated that preoperative fasting blood glucose level,histological grade,lymph node metastasis status,Rad-score,and DL-score were all independent significant predictors of MMRd status in EC patients. The integrated multimodal model demonstrated optimal predictive performance with an AUC of 0.699 (95% CI:0.635-0.763) in the training set,which was superior to the clinical model (AUC=0.629,95% CI:0.561-0.697) and the pure radiomics model (AUC=0.641,95% CI:0.575-0.706). In the validation set,the integrated model maintained good generalizability,achieving an AUC of 0.655 (95% CI:0.535-0.775),and its diagnostic efficacy was higher than that of the clinical model (AUC=0.578,95% CI:0.450-0.705) and the pure radiomics model (AUC=0.611,95% CI:0.488-0.734). According to the DeLong test,the incorporation of DL features resulted in the clinicalra-diomicsdeep learning model performing better than both the clinicalonly model (P=0.027) and the radiomicsonly model (P=0.044) in the training cohort. Conclusions The initially developed clinical-radiomics-deep learning model exhibits a certain predictive potential for the MMRd status in patients with EC. The inclusion of DL features may help complement the limitations of traditional evaluations,offering a preliminary radiological reference for preoperative non-invasive screening. However,given the current diagnostic performance,its overall accuracy and clinical generalizability warrant further validation in multi-center,large-sample external cohort studies.