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Abstract Background The World Health Organization has set ambitious goals to eliminate cervical cancer, necessitating evidence on increasing coverage and access to screening and treatment in high-burden areas. We implemented a pilot program to assess the feasibility of obtaining self-collected specimens for high-risk human papillomavirus (hr-HPV) testing in Nzulezo stilt village, a hard-to-reach community in Ghana, and inviting only hr-HPV-positive women to a central location for colposcopy and possible treatment. Subsequently, this study aimed to investigate the prevalence of hr-HPV infection and cervical lesions among the women and to explore factors potentially associated with hr-HPV infection among them. Methods This pilot community-based cross-sectional study utilized data from screening sessions held from 2 to 20 November 2021 with specimens collected by participants using Evalyn brushes. HPV DNA testing was performed using the Sansure MA-6000 platform, while visual inspection utilized the Enhanced Visual Assessment (EVA) mobile colposcope. Univariate and multivariable nominal logistic regression was employed to explore factors associated with hr-HPV positivity. Results Among 100 women screened (mean age, 43.6 ± 14.5 years), the overall hr-HPV prevalence rate was 39.0% (95% CI, 29.4–49.3). The prevalence rates of hr-HPV genotypes were stratified as follows: HPV16–8.0% (95% CI, 3.5–15.2), HPV18–5.0% (95% CI, 1.6–11.2), and other genotype(s) – 31.0% (95% CI, 22.1–41.0). Single-genotype infections with HPV16 and HPV18 were found in 4.0% (95% CI, 1.1–9.9) and 3.0% (95% CI, 0.6–8.5) of women, respectively. Mixed infections were observed in 1.0% (95% CI, 0.0–5.4) for HPV16 + 18, 3.0% (95% CI, 0.6–8.5) for HPV16 + other type(s), and 1.0% (95% CI, 0.0–5.4) for HPV18 + other type(s). The prevalence of cervical lesions among hr-HPV-positive women screened via colposcopy was 11.4% (95% CI, 3.2–26.7). In the multivariable model, reliance on other sources for medical bill payment was associated with hr-HPV infection (aOR, 0.20; 95% CI, 0.04–0.93), whereas age was not (aOR, 1.02; 95% CI, 0.99–1.05). Conclusions A high hr-HPV infection prevalence was recorded among the women. Utilizing technologies such as self-sampling, HPV DNA testing, and mobile colposcopy enables screening and treatment in remote and hard-to-reach communities where access to cervical cancer screening and treatment would otherwise be limited. Further research is warranted to assess the value and scalability of this approach in similar remote areas and its potential implementation in future programs.

Abstract Background Colposcopic examination with biopsy is the standard procedure for referrals with abnormal cervical cancer screening results; however, the decision to biopsy is controvertible. Having a predictive model may help to improve high-grade squamous intraepithelial lesion or worse (HSIL+) predictions which could reduce unnecessary testing and protecting women from unnecessary harm. Methods This retrospective multicenter study involved 5,854 patients identified through colposcopy databases. Cases were randomly assigned to a training set for development or to an internal validation set for performance assessment and comparability testing. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to reduce the number of candidate predictors and select statistically significant factors. Multivariable logistic regression was then used to establish a predictive model which generates risk scores for developing HSIL+. The predictive model is presented as a nomogram and was assessed for discriminability, and with calibration and decision curves. The model was externally validated with 472 consecutive patients and compared to 422 other patients from two additional hospitals. Results The final predictive model included age, cytology results, human papillomavirus status, transformation zone types, colposcopic impressions, and size of lesion area. The model had good overall discrimination when predicting HSIL + risk, which was internally validated (Area Under the Curve [AUC] of 0.92 (95%CI 0.90–0.94)). External validation found an AUC of 0.91 (95%CI 0.88–0.94) across the consecutive sample, and 0.88 (95%CI 0.84–0.93) across the comparative sample. Calibration suggested good coherence between predicted and observed probabilities. Decision curve analysis also suggested this model would be clinically useful. Conclusion We developed and validated a nomogram which incorporates multiple clinically relevant variables to better identify HSIL + cases during colposcopic examination. This model may help clinicians determining next steps and in particular, around the need to refer patients for colposcopy-guided biopsies.

Background: Colposcopic examination with biopsy is the standard procedure for referrals with abnormal cervical cancer screening results; however, the decision to biopsy is controvertible. Having a predictive model may help to improve high-grade squamous intraepithelial lesion or worse (HSIL+) predictions which could reduce unnecessary testing and protecting women from unnecessary harm. Methods: This retrospective multicenter study involved 5,854 patients identified through colposcopy databases. Cases were randomly assigned to a training set for development or to an internal validation set for performance assessment and comparability testing. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to reduce the number of candidate predictors and select statistically significant factors. Multivariable logistic regression was then used to establish a predictive model which generates risk scores for developing HSIL+. The predictive model is presented as a nomogram and was assessed for discriminability, and with calibration and decision curves. The model was externally validated with 472 consecutive patients and compared to 422 other patients from two additional hospitals. Results: The final predictive model included age, cytology results, human papillomavirus status, transformation zone types, colposcopic impressions, and size of lesion area. The model had good overall discrimination when predicting HSIL + risk, which was internally validated (Area Under the Curve [AUC] of 0.92 (95%CI 0.90–0.94)). External validation found an AUC of 0.91 (95%CI 0.88–0.94) across the consecutive sample, and 0.88 (95%CI 0.84–0.93) across the comparative sample. Calibration suggested good coherence between predicted and observed probabilities. Decision curve analysis also suggested this model would be clinically useful. Conclusion: We developed and validated a nomogram which incorporates multiple clinically relevant variables to better identify HSIL + cases during colposcopic examination. This model may help clinicians determining next steps and in particular, around the need to refer patients for colposcopy-guided biopsies. [ABSTRACT FROM AUTHOR]