Page 100 - Strategies for non-invasive managementof high-grade cervical intraepithelial neoplasia - prognostic biomarkers and immunotherapy Margot Maria Koeneman
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Chapter 5
Abstract
This study aims to develop a prediction model for spontaneous regression of cervical intraepithelial neoplasia grade 2 (CIN 2) lesions based on simple clinical and pathological parameters. The study was conducted at Maastricht University Medical Center, the Netherlands. The prediction model was developed in a retrospective cohort of 129 women with a histological diagnosis of CIN 2 who were managed by watchful waiting for 6–24 months. Five potential predictors for spontaneous regression were selected based on the literature and expert opinion and were analyzed in a multivariable logistic regression model, followed by backward stepwise deletion based on the Wald test. The prediction model was internally validated by the bootstrapping method. Discriminative capacity and accuracy were tested by assessing the area under the receiver operating characteristic curve (AUC) and a calibration plot. Disease regression within 24 months was seen in 91 out of 129 (71%) patients. A prediction model was developed including the following variables: smoking, PAP smear outcome prior to the CIN 2 diagnosis, concomitant CIN 1 diagnosis in the same biopsy, and more than one biopsy containing CIN 2. Not smoking, < PAP3, concomitant CIN 1 and no more than one biopsy containing CIN 2 were predictive of disease regression. The AUC was 69.2% [95% confidence interval (CI), 58.5–79.9%], indicating a moderate discriminative ability of the model. The calibration plot indicated good calibration of the predicted probabilities. This prediction model for spontaneous regression of CIN 2 may aid physicians in the personalized management of these lesions.
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