Background/Objectives: Atypical cytological findings in cervical screening, such as ASC-US, ASC-H, and AGC, present a clinical challenge due to their variable risk of underlying high-grade lesions. The precise stratification of this risk is crucial for effective patient management. This study aimed to correlate Bethesda cytology categories with HPV genotyping, including viral load, and histological follow-up to improve risk prediction for cervical intraepithelial neoplasia grade 2 or worse (CIN2+). Materials and Methods: In this retrospective single-center study, we analyzed 407 patients with cytological reports of ASC-US, ASC-H, or AGC. All patients underwent HPV DNA testing with genotyping for 21 types, with viral load quantification for HPV16/18, and subsequent histological verification. Statistical analyses included non-parametric tests, correlation analysis, and multivariate logistic regression to identify independent predictors of CIN2+. Results: The prevalence of CIN2+ differed significantly among the cytological categories: 23.2% in ASC-US, 47.3% in ASC-H, and 19.5% in AGC. ASC-H and a high HPV16 viral load were identified as independent predictors of CIN2+ in the multivariate analysis. An ASC-H result increased the probability of CIN2+ by 2.5 times (aOR = 2.51; 95% CI: 1.28–4.94). For each 1 log10 increase in HPV16 viral load, the risk of CIN2+ increased by 30% (aOR = 1.30; 95% CI: 1.16–1.46). Stratification of ASC-US cases by HPV16 status revealed a dramatically higher positive predictive value (PPV) for CIN2+ in HPV16-positive patients (66%) compared to HPV16-negative patients (12.6%). The AGC category showed the strongest association with glandular pathology, including adenocarcinoma in situ. Conclusions: The combination of cytological findings and HPV16 viral load provides a powerful model for risk stratification. An ASC-H result is a strong independent risk marker, while the clinical significance of ASC-US is fundamentally determined by HPV16 status. These findings advocate for a risk-based management algorithm that integrates liquid-based cytology with extended HPV genotyping and viral load assessments to optimize patient triage and follow-up.
Asaturova et al. (Tue,) studied this question.