Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, United States; ISGlobal, Barcelona, Spain
Rebecca B Perkins
University Chobanian and Avedisian School of Medicine/Boston Medical Center, Boston, United States
Nicole Campos
Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, United States
Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, United States
Didem Egemen
Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, United States
Brian Befano
Information Management Services Inc, Calverton, United States; Department of Epidemiology, University of Washington School of Public Health, Seattle, United States
Ana Cecilia Rodriguez
Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, United States
Jose Jerónimo
Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, United States
Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, United States; Harvard Graduate Program in Biophysics, Harvard Medical School, Harvard University, Cambridge, United States; Massachusetts Institute of Technology, Cambridge, United States; Geisel School of Medicine at Dartmouth, Dartmouth College, Hanover, United States
Nicolas Wentzensen
Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, United States
Jayashree Kalpathy-Cramer
Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, United States; University of Colorado Anschutz Medical Campus, Aurora, United States
Background: The HPV-automated visual evaluation (PAVE) Study is an extensive, multinational initiative designed to advance cervical cancer prevention in resource-constrained regions. Cervical cancer disproportionally affects regions with limited access to preventive measures. PAVE aims to assess a novel screening-triage-treatment strategy integrating self-sampled HPV testing, deep-learning-based automated visual evaluation (AVE), and targeted therapies. Methods: Phase 1 efficacy involves screening up to 100,000 women aged 25–49 across nine countries, using self-collected vaginal samples for hierarchical HPV evaluation: HPV16, else HPV18/45, else HPV31/33/35/52/58, else HPV39/51/56/59/68 else negative. HPV-positive individuals undergo further evaluation, including pelvic exams, cervical imaging, and biopsies. AVE algorithms analyze images, assigning risk scores for precancer, validated against histologic high-grade precancer. Phase 1, however, does not integrate AVE results into patient management, contrasting them with local standard care. Results: Currently, sites have commenced fieldwork, and conclusive results are pending. Conclusions: The study aspires to validate a screen-triage-treat protocol utilizing innovative biomarkers to deliver an accurate, feasible, and cost-effective strategy for cervical cancer prevention in resource-limited areas. Should the study validate PAVE, its broader implementation could be recommended, potentially expanding cervical cancer prevention worldwide. Funding: The consortial sites are responsible for their own study costs. Research equipment and supplies, and the NCI-affiliated staff are funded by the National Cancer Institute Intramural Research Program including supplemental funding from the Cancer Cures Moonshot Initiative. No commercial support was obtained. Brian Befano was supported by NCI/ NIH under Grant T32CA09168.
