The development and validation of a column chart for predicting the regression risk of CIN2.

Background: A predictive nomogram model was established for the prognosis of cervical intraepithelial neoplasia 2 (CIN2).

Methods: This study was based on the research data of CIN2 obtained from the Shanxi CIN cohort study from 2019 to 2022. We conducted a cross-sectional analysis of 512 female patients with CIN2. Subsequently, the least absolute shrinkage and selection operator (LASSO) regression, along with univariate and multivariate regression analyses, were conducted to identify five risk factors associated with CIN2 prognosis. These factors include age at first sexual activity, ThinPrep cytologic test (TCT) results, Human papillomavirus (HPV) infection type, lesion area detected by colposcopy, and acetowhitening thickness. A predictive model was constructed employing R software. Receiver operating characteristic (ROC) curve and resampling methods were employed to evaluate the predictive model in terms of accuracy and calibration. Decision curve analysis (DCA) was performed to assess its clinical application value.

Results: Women with CIN2 (n = 512) aged 19-65 were included in the study; after 6 months of follow-up, 185 showed lesion regression, and 336 showed lesion persistence or progression. The factors for the predictive model included age of sexual activity (P = 0.005), multiple sexual partners (P = 0.076), TCT results (P < 0.0001), HPV infection (P = 0.0025), lesion area (P < 0.0001), and the thickness of acetic acid stain (P < 0.0001). Subsequent ROC curve analysis showed the respective sensitivity and specificity of the predictive model to be 0.827 and 0.708. Finally, DCA, used to assess the predictive value of the 5-factor CIN2 regression predictive model, was higher than the combined predictive model of HPV and TCT.

Conclusion: The study could successfully establish a model for predicting the pathological regression status of CIN2 patients.