ArticleBMC medicine2021
Development of models for cervical cancer screening: construction in a cross-sectional population and validation in two screening cohorts in China.
Article in BMC medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 16 citations in OpenAlex.
- A nomogram model integrating transcriptomics and clinical features for predicting the risk of cervical intraepithelial lesion progression.Frontiers in oncology · 2026Article
- CINPred: a risk prediction tool for cervical intraepithelial neoplasia.Frontiers in oncology · 2026Article
- Machine and Deep Learning for the Diagnosis, Prognosis, and Treatment of Cervical Cancer: A Scoping Review.Diagnostics (Basel, Switzerland) · 2025Review
- Evaluating the Cost-Effectiveness of Cervical Cancer Screening and Treatment in Western Romania.Current oncology (Toronto, Ont.) · 2025Article
- Development, validation, and clinical application of a machine learning model for risk stratification and management of cervical cancer screening based on full-genotyping hrHPV test (SMART-HPV): a modelling study.The Lancet regional health. Western Pacific · 2025Article
- Article
- Tumor initiation and early tumorigenesis: molecular mechanisms and interventional targets.Signal transduction and targeted therapy · 2024Review
- Using online search activity for earlier detection of gynaecological malignancy.BMC public health · 2024Article
- Use of Virus Genotypes in Machine Learning Diagnostic Prediction Models for Cervical Cancer in Women With High-Risk Human Papillomavirus Infection.JAMA network open · 2023Article
- Mathematical Modelling of Cervical Precancerous Lesion Grade Risk Scores: Linear Regression Analysis of Cellular Protein Biomarkers and Human PapillomavirusDiagnostics (Basel, Switzerland) · 2023Article
- Developing a predictive nomogram for colposcopists: a retrospective, multicenter study of cervical precancer identification in China.BMC cancer · 2023Article
- Evaluating the Feasibility of Machine-Learning-Based Predictive Models for Precancerous Cervical Lesions in Patients Referred for Colposcopy.Diagnostics (Basel, Switzerland) · 2022Article
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Authors and funding
15 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCurrent methods for cervical cancer screening result in an increased number of referrals and unnecessary diagnostic procedures. This study aimed to develop and evaluate a more accurate model for cervical cancer screening.
methodsMultiple predictors including age, cytology, high-risk human papillomavirus (hrHPV) DNA/mRNA, E6 oncoprotein, HPV genotyping, and p16/Ki-67 were used for model construction in a cross-sectional population including women with normal cervix (N = 1085), cervical intraepithelial neoplasia (CIN, N = 279), and cervical cancer (N = 551) to predict CIN2+ or CIN3+. A base model using age, cytology, and hrHPV was calculated, and extended versions with additional biomarkers were considered. External validations in two screening cohorts with 3-year follow-up were further conducted (N
resultsThe base model increased the area under the curve (AUC, 0.91, 95% confidence interval [CI] = 0.88-0.93) and reduced colposcopy referral rates (42.76%, 95% CI = 38.67-46.92) compared to hrHPV and cytology co-testing in the cross-sectional population (AUC 0.80, 95% CI = 0.79-0.82, referrals rates 61.62, 95% CI = 59.4-63.8) to predict CIN2+. The AUC further improved when HPV genotyping and/or E6 oncoprotein were included in the base model. External validation in two screening cohorts further demonstrated that our models had better clinical performances than routine screening methods, yielded AUCs of 0.92 (95% CI = 0.91-0.93) and 0.94 (95% CI = 0.91-0.97) to predict CIN2+ and referrals rates of 17.55% (95% CI = 16.24-18.92) and 7.40% (95% CI = 6.50-8.38) in screening cohort I and II, respectively. Similar results were observed for CIN3+ prediction.
conclusionsCompared to routine screening methods, our model using current cervical screening indicators can improve the clinical performance and reduce referral rates.
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