Aim: To develop and validate a radiomics-based combined model (ModelRC) to predict the pathological grade of endometrial cancer. Methods: A total of 403 endometrial cancer patients from two independent centers were enrolled as training, internal validation and external validation sets. Radiomic features were extracted from T2-weighted images, apparent diffusion coefficient map and contrast-enhanced 3D volumetric interpolated breath-hold examination images. Results: Compared with the clinical model and radiomics model, ModelRC showed superior performance; the areas under the receiver operating characteristic curves were 0.920 (95% CI: 0.864-0.962), 0.882 (95% CI: 0.779-0.955) and 0.881 (95% CI: 0.815-0.939) for the training, internal validation and external validation sets, respectively. Conclusion: ModelRC, which incorporated clinical and radiomic features, exhibited excellent performance in the prediction of high-grade endometrial cancer.
Keywords: MRI; endometrial cancer; radiomics; random forest.
Accurate preoperative evaluation of the pathological grade of endometrial carcinoma is very important for the selection of treatment and prognosis. This study tried to develop a simple combined model based on radiomic features from endometrial carcinoma MRI and clinical features of patients. Compared with the clinical model and the radiomic model, the combined model showed superior performance. Therefore, this combined model would help patients and clinicians to make more rational decisions when choosing treatment strategies.