Predicting myometrial invasion in endometrial cancer based on whole-uterine magnetic resonance radiomics.

Objective: The objective of this study was to evaluate whether whole-uterine magnetic resonance imaging (MRI) radiomic features can predict myometrial invasion (MI) depth in endometrial cancer (EC). Materials and Methods: The preoperative 3.0 T magnetic resonance examinations of EC patients were retrospectively reviewed. Whole-uterus segmentation was performed, and features were extracted based on sagittal T2-weighted imaging (T2WI) and axial diffusion-weighted imaging (DWI). The logistic regression (LR) classifier algorithm was used to establish the radiomic model, which was verified by ten times five-fold cross-validation. The areas under the receiver operating characteristic (ROC) curves (AUCs) were assessed by the DeLong test to compare differences among the models based on different sequences. The LR model was compared with the subjective diagnosis results by the Chi-square test. Results: Of the 163 EC patients included, 44 had deep myometrial invasion (DMI). The feature consistency of the whole uterus was higher than that of the lesion (P 0.05). The single-sequence LR models had lower specificity and accuracy than the corresponding subjective diagnostic results (P 0.05). The combined model included 24 radiomic features, and the accuracy, sensitivity, and specificity were 0.83, 0.77, and 0.85 for DMI, respectively. There was no significant difference compared with subjective diagnosis (P > 0.05). Conclusion: Whole-uterine MRI radiomic features based on sagittal T2WI and axial DWI show potential in predicting MI in EC.