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 (NCohort-I = 3179, NCohort-II = 3082).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.
Highlights
Current methods for cervical cancer screening result in an increased number of referrals and unnecessary diagnostic procedures
Statistical comparisons showed that the logistic regression had slightly higher area under the curve (AUC) compared to support vector machine (SVM) were chosen in further analysis
The logistic regression from the testing set of the crosssectional population showed that the base model had a sensitivity of 92.00% (95% confidence interval [Confidence interval (CI)] = 88.00–95.11%), specificity of 89.08%, and AUC of 0.91
Summary
Current methods for cervical cancer screening result in an increased number of referrals and unnecessary diagnostic procedures. Cancer morbidity and mortality have decreased in developed countries due to the implementation of routine cervical cancer screening [2], and testing for high-risk human papillomavirus (hrHPV) has improved cervical cancer prevention efforts [3]. The decision for modern cervical cancer screening programs is often made based on age, cytology, and hrHPV testing results. For women aged 30 to 65 years, guidelines recommend the use of cytology and hrHPV co-testing due to its high sensitivity. The cytology and hrHPV co-testing would increase the number of referrals, unnecessary diagnostic procedures, and costs of the health care system [5, 6]
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