Work

Machine Learning-Based Prediction of Intervention-Free Survival After Surgery for Adenomyosis: Development and Internal Validation of a Prognostic Model

Year: 2026

Type: article

Abstract: Objective: To develop and validate a machine learning–based model for predicting long-term intervention-free survival (IFS) in patients with adenomyosis (AM) after surgery. Methods: This retrospective cohort study enrolled patients with AM who underwent surgical treatment at our institution between March 2016 and December 2020. Data were retrospectively collected from medical records, while follow-up outcomes were prospectively gathered. Seven machine learning algorithms, including decision trees (DT), random forest (RF), eXtreme Gradient Boosting (XGBoost), support vector machine (SVM), multilayer perceptron (MLP), light gradient boosting machine (LightGBM), and k-nearest neighbors (KNN), were evaluated and compared using regression prediction performance (assessed by root mean squared error, RMSE) and time-dependent ROC analysis. The algorithm demonstrating the highest accuracy was selected as the final model, and the variables it identified were subsequently used to construct the prognostic model. A nomogram was developed using multivariate Cox regression to predict 3-year, 4-year, and 5-year IFS. Its performance was validated through receiver operating characteristic (ROC) curve, calibration curves, and decision curve analysis (DCA). Results: A total of 541 patients were included, with a median follow-up of 77 months. The SVM model demonstrated the optimal performance, yielding the lowest RMSE among the candidate models and achieving the highest time-dependent AUC (0.671). SHAP analysis identified seven stable predictors: age, CA125 level, number of pregnancies, number of abortions, VAS score, hysterectomy, and GnRH-a course. Subsequently, these significant variables were integrated to formulate a nomogram for predicting 3-year, 4-year, and 5-year IFS. The resulting nomogram demonstrated good discrimination, with AUCs of 0.72– 0.80 for 3- to 5-year IFS in both cohorts. Conclusion: This study developed and internally validated a machine learning–guided nomogram for predicting IFS in AM patients after surgery. The model may support individualized risk stratification and postoperative management. However, its clinical utility requires prospective validation before implementation. Keywords: adenomyosis, intervention-free survival, machine learning, cox proportional hazards models, SHAP analysis, nomogram

Source: International Journal of Women s Health

DOI: 10.2147/ijwh.s646974