Abstract
BACKGROUND: To investigate the optimal periendometrial zone (PEZ) in ultrasound (US) images and assess the performance of ultrasound radiomics in predicting the outcome of frozen embryo transfer (FET). METHODS: This prospective study had 422 female participants (training set: n = 358, external validation set: n = 64). We delineated the region of interest (ROI) of the endometrium (EN) from ultrasound images of the median sagittal surface of the uteri of patients. We determined the ROIs of PEZ on US images by automatically expanding 2.0, 4.0, 6.0, and 8.0 mm radii surrounding the EN. We determined the radiomics characteristics based on the ROIs of the endometrium and PEZ, then compared the different sizes of PEZ to determine the optimal PEZ. We constructed models of the EN and optimal PEZ using six machine learning algorithms. We developed a combined model using the radiomics characteristics of EN and the optimal PEZ. We evaluated the performance of the three models using the area under the curve (AUC). RESULTS: The optimal PEZ was 4.0 mm with a maximum AUC of 0.715 (95% confidence interval (CI): 0.581 - 0.833) in the external validation set. The combined radiomics model (endometrium and PEZ(4.0 mm)) yielded the best predictive performance with AUC = 0.853 (95% CI: 0.811 - 0.890) for the training set and AUC = 0.809 (95% CI: 0.696 - 0.909) for the external validation set. CONCLUSIONS: PEZ(4.0 mm) could be the optimal area for predicting clinical pregnancy after FET. An US-based radiomics model that combines EN and PEZ(4.0 mm) demonstrated strong potential in helping clinicians predict FET outcomes more accurately, thereby supporting informed decision-making before treatment.