Prediction Models of Microinvasive Cervical Cancer in High-Grade Squamous Intraepithelial Lesion Treatment by Loop Electrosurgical Excision Procedure.

Objective: The implementation of comprehensive microinvasive cervical cancer (MIC) risk assessment in high-grade squamous intraepithelial lesion (HSIL) patients undergoing loop electrosurgical excision procedure (LEEP) is critical to optimize treatment strategies and improve patient outcomes. Methods: From March 2017 to January 2024, a total of 3066 eligible patients with HSIL were retrospectively enrolled from two hospitals and assigned into one training cohort (n = 2084), one internal validation cohort (579) and one external testing cohort (n = 403). Four feature selection methods (Random Forest, Lasso regression, Boruta algorithm, and Extreme Gradient Boosting) were employed to identify key predictive factors from the training cohort. Then, four machine learning models were developed and evaluated using comprehensive metrics. The optimal model was visualized through interpretable techniques and operationalized as a web-based clinical decision support system for real-world implementation. Results: Six clinical predictive variables were identified, including surgical margins, endocervical curettage (ECC), TCT status, HPV status, Transformation Zone (TZ) type and Age. The optimal model demonstrated good predictive performance, achieving an area under the receiver operating characteristic curve (AUC) of 0.822 (95% CI: 0.793-0.852) in the internal validation cohort and 0.802 (95% CI: 0.730-0.874) in the external validation cohort. Conclusion: The machine learning-based model can accurately assess the risk of MIC during the treatment of HSIL with LEEP, potentially aiding in the selection of appropriate treatment and surveillance strategies in clinical practice.