A radiomics model based on diffusion-weighted imaging developed using machine learning enables prediction of microsatellite instability in endometrial cancer

The objective of this research was to develop and validate a machine learning-based prediction model integrating clinical data, apparent diffusion coefficient (ADC) value, and diffusion- weighted imaging (DWI)-based radiomic features, aimed at assessing microsatellite instability (MSI) status in endometrial cancer (EC) patients. In total, 292 EC patients who underwent pelvic MRI scans participated in this study and were allocated into three distinct groups: an external validation cohort (n = 70), a testing cohort (n = 68), and a training cohort (n = 154). Preoperative clinical indicators, ADC metrics, and radiomic parameters extracted from DWI images were comprehensively evaluated. Feature selection was conducted using least absolute shrinkage and selection operator (LASSO) regression combined with Mann-Whitney U testing. Following feature selection, three distinct machine learning classifiers—support vector machine (SVM), random forest (RF), and logistic regression (LR)—were employed to develop predictive models. The performance and clinical utility of these models were subsequently examined through receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Among the evaluated methods, the RF model incorporating two clinical indicators, six radiomic parameters from DWI, and ADC values exhibited superior predictive ability. The areas under the ROC curves (AUC) reached 0.980 (95% CI: 0.944–0.996) in the training cohort, 0.852 (95% CI: 0.745–0.927) in the test cohort, and 0.938 (95% CI: 0.853–0.982) in the external validation cohort, respectively. These AUC values reflected better accuracy compared to separate predictive models employing only clinical factors, DWI radiomics, or ADC values (training cohort: AUC = 0.682, 0.925, and 0.851; test cohort: AUC = 0.663, 0.788, and 0.731; external validation cohort: AUC = 0.605, 0.872, and 0.828, respectively). Calibration curves indicated robust concordance, and DCA analysis confirmed that the model had substantial clinical applicability. A prediction model combining clinical factors, DWI radiomics features, and ADC values with machine learning algorithms can noninvasively assess MSI status in EC.