Design of the HPV-Automated Visual Evaluation (PAVE) Study: Validating a Novel Cervical Screening Strategy

Area of Science:

• Oncology
• Infectious Diseases
• Medical Technology

Background:

• Cervical cancer remains a significant global health burden, particularly in resource-limited settings where access to effective screening and treatment is often inadequate.
• Current screening methods may lack the sensitivity or specificity required for optimal early detection and prevention in diverse populations.
• Novel strategies integrating advanced diagnostics are needed to enhance cervical cancer prevention efforts worldwide.

Approach:

• The HPV-Automated Visual Evaluation (PAVE) Study employs a multi-centric, international approach to evaluate a novel screen-triage-treat strategy.
• Phase 1 (efficacy) involves HPV testing via self-sampled specimens, followed by triage using extended HPV genotyping and deep-learning-assisted automated visual evaluation (AVE) of the cervix.
• Phase 2 (effectiveness) will assess the real-world implementation, acceptability, feasibility, and cost-effectiveness of the PAVE strategy in clinical practice.

Key Points:

• The PAVE strategy integrates HPV testing with automated visual evaluation (AVE) for improved cervical cancer screening and triage.
• The study utilizes a novel isothermal HPV test for hierarchical genotyping and a deep learning algorithm for cervical image analysis.
• Risk stratification combines HPV genotype and AVE classification to guide timely treatment with thermal ablation or excision.

Conclusions:

• The PAVE study aims to validate an accurate, feasible, and cost-effective cervical cancer prevention protocol for resource-limited settings.
• Successful validation could facilitate the broader implementation of the PAVE strategy to reduce the global burden of cervical cancer.
• The study's findings will inform the development and deployment of innovative cervical cancer prevention tools in underserved regions.