A Machine Learning–Based Prediction Model for Deep Infiltrating Endometriosis

Guoquan Sun, Ling LiuDepartment of Obstetrics and Gynecology, Hangzhou Red Cross Hospital, Hangzhou, 310005, People’s Republic of ChinaCorrespondence: Ling Liu, Department of Obstetrics and Gynecology, Hangzhou Red Cross Hospital, No. 208, Huancheng East Road, Gongshu District, Hangzhou, 310005, People’s Republic of China, Email [email protected]: Deep infiltrating endometriosis (DIE) is a severe endometriosis phenotype. This study aimed to develop and internally validate a machine learning-based predictive model for DIE using retrospective clinical data from a single center to improve diagnostic accuracy.Materials and Methods: Clinical, imaging, and laboratory data were retrospectively collected from 250 surgically confirmed DIE patients and non-DIE endometriosis controls (2020– 2025). Samples were randomly split 7:3 into training and internal validation sets. Three machine learning algorithms-Random Forest (RF), Gradient Boosting Machine (GBM), and Support Vector Machine (SVM)-were developed and compared with conventional logistic regression. Model performance was assessed using AUC, sensitivity, specificity, calibration, and decision curves. The model was designed to identify DIE among patients with confirmed or suspected endometriosis.Results: Univariate analysis identified seven independent predictors (age, BMI, CA125, CA199, neutrophil-to-lymphocyte ratio, albumin, ovarian endometrioma), confirmed by multivariate logistic regression. In the validation set, AUCs were 0.787 (RF), 0.740 (GBM), 0.685 (SVM), and 0.709 (logistic regression). RF achieved 0.714 accuracy and 0.821 specificity, alongside favorable calibration and a positive net clinical benefit.Conclusion: In this retrospective single-center exploratory study, the RF model demonstrated moderate non-invasive predictive performance for DIE. However, this performance level does not yet support clinical readiness, and external multicenter validation is needed to confirm generalizability.Keywords: deep infiltrating endometriosis, machine learning, predictive model, internal validation