Deep Learning Radiomics Model Based on Ultrasound Images Predicts Myometrial Infiltration of Endometrial Cancer

ABSTRACT Objective This study aims to develop and validate a deep learning radiomics (DLR) model based on ultrasound images for non‐invasively distinguishing the myometrial infiltration (MI) of patients with endometrial cancer (EC). Methods We retrospectively collected 310 patients with EC who underwent surgical resection from center 1 from September 2017 to January 2025, forming a training group and a validation group. An external testing group was comprised of 80 patients from center 2. We extracted deep learning (DL) features and radiomics features from ultrasound images, establishing a DLR model after dimensionality reduction. The receiver operating characteristic analysis was used to evaluate the practicality of the proposed model. The utility of the proposed model was evaluated using receiver operating characteristic, calibration, and decision curve analysis. Results A total of 390 EC patients were included in the study. In the validation set, the AUC of the radiomics model was 0.874 (0.774–0.949), and in the testing group, the AUC of the DL model was 0.844 (0.699–0.957). The DLR model was superior to radiomics models and DL models in interpreting images, and also outperformed the diagnostic performance of advanced ultrasound physicians. Conclusion The DLR model based on ultrasound images can accurately and non‐invasively distinguish the MI depth of EC patients, assisting doctors in formulating more favorable treatment plans for patients.