ArticleScientific reports2024
Nationwide study on development and validation of a risk prediction model for CIN3+ and cervical cancer in Estonia.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Prediction models for high-grade cervical lesions or worse using machine learning.EClinicalMedicine · 2026Article
- A Machine Learning Model Integrating Preoperative Blood-Based Indices for Early and Noninvasive Detection of Endometrial Cancer.Journal of inflammation research · 2025Article
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Authors and funding
11 authors.
Funding
Abstract
Transitioning to an individualized risk-based approach can significantly enhance cervical cancer screening programs. We aimed to derive and internally validate a prediction model for assessing the risk of cervical intraepithelial neoplasia grade 3 or higher (CIN3+) and cancer in women eligible for screening. This retrospective study utilized data from the Estonian electronic health records, including 517,884 women from the health insurance database and linked health registries. We employed Cox proportional hazard regression, incorporating reproductive and medical history variables (14 covariates), and utilized the least absolute shrinkage and selection operator (LASSO) for variable selection. A 10-fold cross-validation for internal validation of the model was used. The main outcomes were the performance of discrimination and calibration. Over the 8-year follow-up, we identified 1326 women with cervical cancer and 5929 with CIN3+, with absolute risks of 0.3% and 1.1%, respectively. The prediction model for CIN3 + and cervical cancer had good discriminative power and was well calibrated Harrell's C of 0.74 (0.73-0.74) (calibration slope 1.00 (0.97-1.02) and 0.67 (0.66-0.69) (calibration slope 0.92 (0.84-1.00) respectively. A developed model based on nationwide electronic health data showed potential utility for risk stratification to supplement screening efforts. This work was supported through grants number PRG2218 from the Estonian Research Council, and EMP416 from the EEA (European Economic Area) and Norway Grants.
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