A Holistic Framework for Cervical Cancer Screening and Risk Stratification: Integrating Etiology, Morphology,...

Cervical cancer (CC) remains a major global health burden despite being highly preventable through early detection. Current screening strategies, predominantly high-risk human papillomavirus (HR-HPV) testing and liquid-based cytology (LBC), have limited specificity, leading to unnecessary colposcopic referrals and overtreatment. This narrative review proposes a conceptual holistic screening framework-termed HADm-that integrates four complementary pillars into a unified risk stratification system: (1) Etiology (HR-HPV genotyping), (2) Morphology (artificial intelligence-assisted LBC, Ai-LBC), (3) Epigenetics (DNA methylation [DNAm] biomarkers, with PAX1 methylation as an example), and (4) Microecology (genital pathogens and vaginal microbiota). Persistent HR-HPV infection is the primary etiological factor, while disease progression is modulated by host epigenetic alterations and the local microenvironment. DNAm markers may serve as molecular indicators of malignant transformation and have shown high specificity for cervical intraepithelial neoplasia grade 2 or worse (CIN2+) and grade 3 or worse (CIN3+) among human papillomavirus (HPV)-positive women in some studies. Ai-LBC may improve both sensitivity and standardization of morphological interpretation, whereas genital pathogen and microbiota may help explain persistent HPV infection and chronic inflammation. However, evidence for the combined HADm framework remains limited, and prospective multicenter validation is lacking. This framework represents a shift from single-analyte testing toward integrated risk assessment, but its incremental clinical value, cost-effectiveness, and feasibility in low- and middle-income countries require rigorous evaluation. If validated, HADm may support precise risk stratification, individualized follow-up, and more efficient resource allocation, aligning with the World Health Organization (WHO) global goal of accelerating CC elimination by 2030.