What question did this study set out to answer?

The study aims to create a predictive model using clinical data to assess the risk of cervical intraepithelial neoplasia (CIN).

February 13, 2026SHILAP Revista de lepidopterología0 citationsOpen Access

CINPred: a risk prediction tool for cervical intraepithelial neoplasia

JGJiaXuan Gu QWQiao Wang ALAili Li

Key Points

The study aims to create a predictive model using clinical data to assess the risk of cervical intraepithelial neoplasia (CIN).
Enrolled female patients with cervical lesions between 2018-2021.
Analyzed various feature variables including age, HPV genotype, and cytological test results.
Utilized machine learning algorithms like CatBoost and GBDT for risk prediction.
Evaluated model performance using AUC and F1 score metrics.
Implemented SHAP values to identify significant risk factors for CIN.
CatBoost and GBDT achieved the highest AUC values of 0.89 and 0.87, respectively.
AdaBoost produced the highest F1 score at 0.81.
Key risk factors affecting CIN risk included TCT, age, and FRD, according to SHAP values.

Abstract

Introduction Cervical intraepithelial neoplasia (CIN) is a group of precancerous lesions associated with invasive carcinoma of the cervix that reflects the continuous progression of cervical cancer (CC). Therefore, early detection and standard treatment can effectively prevent the progression of CIN to CC. The objective of this study is to establish machine learning model using clinical data to predict the risk of CIN in women, and to develop a clinical prediction tool, exploring its broader clinical application significance. Methods Female patients who sought consultation for cervical lesions at a hospital in Jiangsu province between 2018 and 2021 were enrolled in this study. The feature variables considered in the analysis included age, ThinPrep cytological test (TCT), human papillomavirus (HPV) genotype, multiple infection assessment, folate receptor-mediated tumor detection (FRD) and cotton-tipped swab test. Several algorithms were utilized for establishing the model, including adaptive boosting (AdaBoost), gradient boosting decision tree (GBDT), categorical boosting (CatBoost) and others. The performance of models was rigorously evaluated. The SHapley Additive exPlanation (SHAP) values were used to identify risk factors affecting the risk of CIN. Results For predicting CIN events, CatBoost and GBDT had the highest area under the receiver operating characteristic curve (AUC) (0.89, 0.87, respectively). AdaBoost had the highest F1 score (F1 score = 0.81), followed by RF, LR and stochastic gradient descent (SGD). SHAP values suggested that the variables affected the risk of CIN in descending order of magnitude were TCT, age, FRD, cotton-tipped swab, multiple infection and HPV, respectively. Discussion A novel CatBoost-based risk prediction tool for CIN (CINPred) has been developed and it can be accessed through the website at: https://medinfo.hebeu.edu.cn/shiny/CINPred/ . CINPred can be used as a quick screening tool to assess CIN risk, offering significant benefits for the development of personalized treatment plans.

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