Prediction of molecular subtypes of endometrial cancer patients on the basis of intratumoral and peritumoral radiomic features from multiparametric MR images

Objectives: The purpose of this study was to assess the performance of multiparametric MRI-based radiomic models in predicting the molecular subtypes of endometrial cancer (EC) patients. Methods: A total of 310 patients with pathologically confirmed EC who underwent preoperative MRI were enrolled this retrospective study and randomly divided into training (n = 217) and testing (n = 93) cohorts. We extracted 22,640 radiomic features from intratumoral and 3-mm peritumoral regions of interest (ROIs) on MR images. Feature selection was performed using the Mann-Whitney U test, Max-Relevance and Min-Redundancy (mRMR) and the least absolute shrinkage and selection operator (LASSO). Twelve radiomic signatures (RSs) were constructed using logistic regression to predict four molecular subtypes (POLEmut, MMRd, NSMP, and p53abn). The performance of these RSs was assessed using receiving operating characteristic (ROC) curve analysis, and the area under the curve (AUC), sensitivity, specificity, and accuracy were calculated. Results: In the testing cohort, the RSs based on intratumoral features for predicting the POLEmut, MMRd, NSMP and p53abn subtypes yielded AUCs of 0.764, 0.812, 0.893 and 0.731, respectively, whereas those based on peritumoral features yielded AUCs of 0.847, 0.836, 0.871 and 0.804, respectively. The RSs constructed by combining intratumoral and peritumoral features for predicting the POLEmut, MMRd, NSMP and p53abn subtypes had the AUCs of 0.844, 0.880, 0.943 and 0.801, respectively. Conclusion: The combination of intratumoral and peritumoral radiomic features from multiparametric MRI enables effective and noninvasive prediction of EC molecular subtypes.