Predicting Microsatellite Instability in Endometrial Cancer by Multimodal Magnetic Resonance Radiomics Combined with Clinical Factors
To develop a nomogram integrating clinical and multimodal MRI features for non-invasive prediction of microsatellite instability (MSI) in endometrial cancer (EC), and to evaluate its diagnostic performance. This retrospective multicenter study included 216 EC patients (mean age, 54.68 ± 8.72 years) from two institutions (2017-2023). Patients were classified as MSI (n=59) or microsatellite stable (MSS, n=157) based on immunohistochemistry. Institution A data were randomly split into training (n=132) and testing (n=33) sets (8:2 ratio), while Institution B data (n=51) served as external validation. Eight machine learning algorithms were used to construct models. A nomogram combining radiomics score and clinical predictors was developed. Performance was evaluated via receiver operating characteristic (ROC) curves, calibration, and decision curve analysis (DCA). The T2-weighted imaging (T2WI) radiomics model showed the highest area under the receiver operating characteristic curve (AUC) among single sequences (training set:0.908; test set:0.838). The combined-sequence radiomics model achieved superior performance (AUC: training set=0.983, test set=0.862). The support vector machine (SVM) outperformed other algorithms. The nomogram integrating rad-score and clinical features demonstrated higher predictive efficacy than the clinical model (test set: AUC=0.904 vs. 0.654; p < 0.05) and comparable to the multimodal radiomics model. DCA indicated significant clinical utility for both nomogram and radiomics models. The clinical-radiomics nomogram effectively predicts MSI status in EC, offering a non-invasive tool for guiding immunotherapy decisions.