人乳头瘤病毒
基因分型
目视检查
医学
计算机科学
人工智能
病毒学
生物
基因型
内科学
遗传学
基因
作者
Brian Befano,Jayashree Kalpathy–Cramer,Didem Egemen,Federica Inturrisi,José Jerónimo,Ana Cecilia Rodríguez,Nicole G. Campos,Miriam Cremer,Ana Ribeiro,Kayode Olusegun Ajenifuja,Andrew Goldstein,Amna Haider,Karen Yeates,Margaret M. Madeleine,Teresa Norris,Juán Luis Peñaloza Figueroa,Karla Alfaro,Tainá Raiol,Clement A. Adepiti,Judith Norman
出处
期刊:PubMed
日期:2025-03-18
摘要
The HPV-Automated Visual Evaluation (PAVE) Consortium is validating a cervical screening strategy enabling accurate cervical screening in resource-limited settings. A rapid, low-cost HPV assay permits sensitive HPV testing of self-collected vaginal specimens; HPV-negative women are reassured. Triage of positives combines HPV genotyping (four groups in order of cancer risk) and visual inspection assisted by automated cervical visual evaluation (AVE) that classifies cervical appearance as severe, indeterminate, or normal. Together, the combination predicts which women have precancer, permitting targeted management to those most needing treatment. We analyzed CIN3+ yield for each PAVE risk level (HPV genotype crossed by AVE classification) from nine clinical sites (Brazil, Cambodia, Dominican Republic, El Salvador, Eswatini, Honduras, Malawi, Nigeria, and Tanzania). Data from 1832 HPV-positive participants confirmed that HPV genotype and AVE classification each strongly and independently predict risk of histologic CIN3+. The combination of these low-cost tests provided excellent risk stratification, warranting pre-implementation demonstration projects.
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