Practical Model for Residual/Recurrent Cervical Intraepithelial Lesions in Patients with Negative Margins after Cold-Knife Conization.

Objective: This study aimed to identify reliable risk factors for residual/recurrent cervical intraepithelial lesions in patients with negative margins after cold-knife conization. Methods: A total of 2352 women with HSILs (high-grade squamous intraepithelial lesions) with negative margins who underwent cold-knife conization between January 2014 and December 2020 were included; in total, 1411 women were assigned to the development cohort, and 941 women were assigned to the validation cohort. Multivariate logistic regression was used to build four predictive models based on the different combinations of follow-up data (Model A: preoperative factors; Model B: first-follow-up data; Model C: second-follow-up data; Model D: data from both follow-ups). The accuracy, sensitivity, specificity, false-positive rate (FPR), false-negative rate (FNR), and area under the receiver operating characteristic curve (AUC) were evaluated on the validation cohort. The predictive power of risk factors was further validated using six machine learning algorithms. Results: Model D demonstrated the highest AUC of 0.91 (95% CI, 0.87 to 0.96) in the validation cohort, whereas Models A, B, and C achieved AUCs of 0.69 (95% CI, 0.59 to 0.78), 0.88 (95% CI, 0.80 to 0.95), and 0.89 (95% CI, 0.81 to 0.97) respectively. The six machine learning methods achieved consistent results. Kaplan-Meier (KM) survival curves demonstrated that our models could effectively stratify patients with all models (p < 0.05 for all models). Conclusion: Our model, which is based on preoperative and follow-up factors, can serve as a complementary screening procedure for the early detection or prediction of recurrence after cold-knife conization in HSIL patients.