In Geburtshilfe und Frauenheilkunde
Objective This study aimed to identify predictors for the presence of cervical dysplasia in diagnostic LEEPs (Loop Electrical Excision Procedure) of the cervix. Materials/Methods The study was designed as a retrospective single-institution cohort analysis of all patients who underwent LEEP without prior proof of high-grade intraepithelial lesion (diagnostic LEEP) between 2015 and 2020 in the Department of Obstetrics and Gynecology of University Hospital Aachen. In order to identify the most meaningful predictive variables for CIN status (CIN2+ or non-CIN2+), multivariate logistic regression was performed and a machine-learning method was used. Results A total of 849 patients with an indication for loop excision of the cervix were assessed for eligibility. Finally, 125 patients without prior proof of CIN2+ were included into the study. Based on the final multivariate logistic regression model, multiple high-risk HPV infections (p = 0.001), the presence of a T2 transformation zone (p = 0.003) and major lesion changes (p = 0.015) as a result of the colposcopy examination were found to be statistically significant for CIN status based on the diagnostic LEEP. Subsequent ROC analysis showed a high predictive value for the model of 88.35% (AUC). The machine-learning technique (recursive partitioning) identified similar variables as important for CIN status with an accuracy of 75%. Conclusion For clinical decision-making, the result of the colposcopy examination (T2, major change) as well as the results of HPV testing (multiple high-risk HPV infections) are stronger indicators for clinicians to perform diagnostic excisional procedures of the cervix than the presence of high-grade cytological abnormalities.
Wittenborn Julia, Kupec Tomas, Iborra Severine, Stickeler Elmar, Najjari Laila, Kennes Lieven N
2022-Dec
cervical dysplasia, colposcopy, machine learning, recursive partitioning
