A risk prediction model to allow personalized screening for cervical cancer

Rothberg, Michael B.; Hu, Bo; Lipold, Laura; Schramm, Sarah; Jin, Xian Wen; Sikon, Andrea; Taksler, Glen B.

doi:10.1007/s10552-018-1013-4

Cited by 17 publications

(24 citation statements)

References 24 publications

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“…Several studies used logistic regression to establish predictors for histologic grade or risk stratification based on the epidemiologic risk factors and the molecular markers. In a large study of around 100,000 women using race, smoking status, insurance, marital status, median income, and previous HPV test result as predictors, their model only obtained an AUC of 0.81 for CIN2+ [ 19 ]. Another study of 1,477 women reported that the most predictive factors were mRNA level, DNA index, parity, and age, and the AUC was 0.99 for HSIL and 0.81 for LSIL [ 20 ].…”

Section: Discussionmentioning

confidence: 99%

Development of models for cervical cancer screening: construction in a cross-sectional population and validation in two screening cohorts in China

Han

et al. 2021

BMC Med

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Background Current 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. Methods Multiple 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). Results The 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. Conclusions Compared to routine screening methods, our model using current cervical screening indicators can improve the clinical performance and reduce referral rates.

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Section: Discussionmentioning

confidence: 99%

Development of models for cervical cancer screening: construction in a cross-sectional population and validation in two screening cohorts in China

Han

et al. 2021

BMC Med

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Section: Discussionmentioning

confidence: 99%

“…This is a potential advantage over other predictive models for CC, which typically rely on a specific set of patient data and risk factors. 16,17 Finally, our model assesses CC risk based on a limited list of risk factors rather than the risk of cervical pre-cancer based on CC screening results, such as the analysis reported by Rothberg et al 18 The meta-model was also able to combine both adjusted and non-adjusted risk factor estimates. However, the use of both univariate and multivariate risk factor estimates could potentially bias predicted risks.…”

Section: Discussionmentioning

confidence: 99%

Risk factors for cervical cancer in women in China: A meta-model

Aballéa

Beck

Cheng³

et al. 2020

Womens Health (Lond Engl)

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Objectives: Cervical cancer is a leading cause of cancer-related mortality in women in China. This analysis is a quantitative evidence synthesis pooling information about each cervical cancer risk factor. Methods: A meta-model was developed to estimate the risk of cervical cancer for a woman aged 18–85 years in Mainland China based on her risk profile at the time of assessment. The meta-model was built using findings of a systematic literature review that identified 21 case–control studies reporting data on 105 groups of cervical cancer risk factors in Chinese women. Extracted risk factors were ranked, and 17 were selected by Chinese clinical experts for inclusion in the meta-model. Risk equations were developed for each selected study. Predicted risks for each study were dependent on the risk profile under consideration and study-specific risks were pooled to an overall risk estimate using a random-effects meta-analysis. Sensitivity analysis was conducted using 100 artificial patient profiles (in the absence of patient data). Results: Predicted risks for the 100 profiles suggested that the model had good face validity and could differentiate between high and non-high cervical cancer risk profiles. Conclusion: This innovative meta-model approach assesses cervical cancer risk in Chinese women from a holistic perspective and could be adapted for other diseases and settings.

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“…Additional risk factors for CC have been identified, but are not currently included in recommendations about screening intervals. These include age, smoking, oral contraceptive use, and sexual history [26]. Evaluating screening strategies that consider these risk factors for a more personalized approach, incl.…”

Section: An Alternative Policy Optionmentioning

confidence: 99%

Cervical Cancer in the Baltic States: Can Intelligent and Personalised Cancer Screening Change the Situation?

Stankūnas

Pärna

Tisler

et al. 2022

AML

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The three Baltic States (Estonia, Latvia, and Lithuania) are among the European Union countries with the highest incidence and mortality rates for cervical cancer. In order to tackle this public health challenge, there is an urgent need to implement more advanced and effective methods in cervical cancer prevention in Baltic countries.Nationwide cervical cancer screening programs in the Baltic States commenced in 2004–2009. While the organized screening programs in these countries differ in some relevant details (target age groups, screening interval), the underlying principles and problems, barriers are universal. However, the outcomes of present screening programs are unsatisfactory. In addition, universal screening programs are extremely costly. There is a potential need for more intelligent and personalized cervical cancer screening program. In 2019 the project “Towards elimination of cervical cancer: intelligent and personalized solutions for cancer screening” (2020–2023) was developed with the main objective – to develop improved and personalized cancer screening methods within a sustainable health care system.It is expected, that more sophisticated cervical cancer screening model will be implemented in Estonia, Latvia, and Lithuania, and will have a positive impact to epidemiology of cervical cancer and public health in general.

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A risk prediction model to allow personalized screening for cervical cancer

Abstract: A multivariable model using data from the electronic health record was able to stratify women across a 50-fold gradient of risk for CIN2+. After further validation, use of a similar model could enable more targeted cervical cancer screening.

Cited by 17 publications

References 24 publications

Development of models for cervical cancer screening: construction in a cross-sectional population and validation in two screening cohorts in China

Development of models for cervical cancer screening: construction in a cross-sectional population and validation in two screening cohorts in China

Risk factors for cervical cancer in women in China: A meta-model

Cervical Cancer in the Baltic States: Can Intelligent and Personalised Cancer Screening Change the Situation?

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