Bingyi Wang

Characteristics of Human Papillomavirus Prevalence and Infection Patterns Among Women Aged 35–65 in Fujian Province, China: A Nine‐Year Retrospective Observational Study

ABSTRACTThe assessment of human papillomavirus (HPV) genotype distribution could inform targeted cervical cancer prevention strategies. The epidemiology of HPV genotypes in terms of age and cervical lesions in Fujian Province, China has not been well described. This 9‐year retrospective study aimed to delineate the prevalence pattern and trend of HPV genotypes among a large‐scale community‐based population. Deidentified data were retrieved from the national cervical cancer screening program in China. We included eligible women aged 35–65 years who underwent cervical cancer screening between 2014 and 2022 in Fujian Province. The HPV prevalence within distinct subpopulations was calculated, and trends in HPV prevalence over the years and across age groups were examined using the Cochran‐Armitage trend test. A total of 551 604 women (median age 49 years [42, 54]; 0.10% with cervical cancer) were included in this study. The overall HPV prevalence was 11.72% (95% CI: 11.63%–11.80%), with HR‐HPV (high‐risk HPV) and HPV 16/18 prevalence at 10.02% (9.94%–10.10%) and 1.74% (1.71%–1.78%), respectively. HPV‐52, 58, 16, 39, 51, and 68 were the most predominant genotypes in the general population. Nearly all genotypes, except for HPV‐39 and 66, showed a decreasing trend in prevalence over the years, while a relatively high prevalence of HR‐HPV was observed across all age groups. As lesion severity increased, HR‐HPV and 9v‐HPV prevalence also increased. Our study underscores the importance of ongoing surveillance of HPV prevalence in China. While the overall decline in HPV infections over the years is encouraging, the relatively high prevalence of HR‐HPV warrants continued attention. Strengthening public health strategies—including prioritizing and promoting the current 9‐valent vaccination, extending HPV testing and cervical cancer screening to older women where feasible, and developing future vaccines targeting more HR‐HPV genotypes—will be crucial in eliminating cervical cancer and HPV‐related disease in China and beyond.

Identifying Data-Driven Clinical Subgroups for Cervical Cancer Prevention With Machine Learning: Population-Based, External, and Diagnostic Validation Study

Abstract Background Cervical cancer remains a major global health issue. Personalized, data-driven cervical cancer prevention (CCP) strategies tailored to phenotypic profiles may improve prevention and reduce disease burden. Objective This study aimed to identify subgroups with differential cervical precancer or cancer risks using machine learning, validate subgroup predictions across datasets, and propose a computational phenomapping strategy to enhance global CCP efforts. Methods We explored the data-driven CCP subgroups by applying unsupervised machine learning to a deeply phenotyped, population-based discovery cohort. We extracted CCP-specific risks of cervical intraepithelial neoplasia (CIN) and cervical cancer through weighted logistic regression analyses providing odds ratio (OR) estimates and 95% CIs. We trained a supervised machine learning model and developed pathways to classify individuals before evaluating its diagnostic validity and usability on an external cohort. Results This study included 551,934 women (median age, 49 years) in the discovery cohort and 47,130 women (median age, 37 years) in the external cohort. Phenotyping identified 5 CCP subgroups, with CCP4 showing the highest carcinoma prevalence. CCP2–4 had significantly higher risks of CIN2+ (CCP2: OR 2.07 [95% CI: 2.03‐2.12], CCP3: 3.88 [3.78‐3.97], and CCP4: 4.47 [4.33‐4.63]) and CIN3+ (CCP2: 2.10 [2.05‐2.14], CCP3: 3.92 [3.82‐4.02], and CCP4: 4.45 [4.31‐4.61]) compared to CCP1 (P<.001), consistent with the direction of results observed in the external cohort. The proposed triple strategy was validated as clinically relevant, prioritizing high-risk subgroups (CCP3-4) for colposcopies and scaling human papillomavirus screening for CCP1-2. Conclusions This study underscores the potential of leveraging machine learning algorithms and large-scale routine electronic health records to enhance CCP strategies. By identifying key determinants of CIN2+/CIN3+ risk and classifying 5 distinct subgroups, our study provides a robust, data-driven foundation for the proposed triple strategy. This approach prioritizes tailored prevention efforts for subgroups with varying risks, offering a novel and scalable tool to complement existing cervical cancer screening guidelines. Future work should focus on independent external and prospective validation to maximize the global impact of this strategy.