Background: Ovarian endometriomas damage the ovarian structure, alter ovarian inflammation, and impair ovarian reserve.Given the conflicting results, determining an optimal reproductive strategy for women with endometriomas-whether expectant management, medication, surgery, or assisted reproductive technology (ART)-remains challenging.Objective: This study aims to preliminarily develop and validate clinically applicable decision-support tools by training, testing, and validating an automated machine learning (ML) model to predict the likelihood of live birth following ART in women with endometriomas.Methods: The derivation and testing cohort included 1705 women, and the external validation cohort included 1475 women with ovarian endometriomas following ART retrospectively.Two ML models were developed and validated to predict the probability of live birth.Model performance was evaluated using the area under the curve (AUC), accuracy, sensitivity, specificity, F1 score, and Brier score.The SHapley Additive exPlanations (SHAP) method was employed to interpret feature importance.Results: Comparing seven ML algorithms, the Extreme Gradient Boosting (XGBoost) demonstrated superior predictive performance both in model-1 and model-2, achieving an AUC of 0.90 95% confidence interval (CI): 0.88-0.92and 0.88 (95% CI: 0.86-0.89) in test-datasets and 0.80 (95% CI: 0.76-0.83)and 0.69 (95% CI: 0.65-0.73) in external validation cohort.The SHAP analysis revealed that the age and features associated with ovarian reserve had strong predictive power and the ovarian endometriomas had limited predictive power.Conclusion: Model-2, which uses only pre-ART variables, can support reproductive strategy selection prior to ART initiation.Conversely, Model-1 is designed to support embryo transfer strategy option after oocyte retrieval, incorporating post-ART data.Although both models show promise as decision-support tools for personalizing infertility treatment in women with endometriomas, their clinical implementation awaits confirmation from prospective, multicenter validation.
Sun et al. (Sun,) studied this question.