Befano Brian, Kalpathy-Cramer Jayashree, Egemen Didem, Inturrisi Federica, Jeronimo José, Rodríguez Ana Cecilia, Campos Nicole, Cremer Miriam, Ribeiro Ana, Ajenifuja Kayode Olusegun, Goldstein Andrew, Haider Amna, Yeates Karen, Madeleine Margaret, Norris Teresa, Figueroa Jaqueline, Alfaro Karla, Raiol Tainá, Adepiti Clement, Norman Judith, Chilinda George Kassim, Mchome Bariki, Donastorg Yeycy, Dlamini Xolisili, Conzuelo Gabriel, Banjo Adekunbiola A, Chone Pauline, Mremi Alex, Benitez Arismendi, Rosberger Zeev, Vantha Te, Prieto-Egido Ignacio, Boyd-Morin Jen, Clark Christopher, Kinder Scott, Wentzensen Nicolas, Desai Kanan, Perkins Rebecca, de Sanjosé Silvia, Schiffman Mark
University of Washington, Seattle, WA, United States of America.
Information Management Services, Inc, Calverton, MD, United States of America.
J Natl Cancer Inst. 2025 Mar 18. doi: 10.1093/jnci/djaf054.
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.
人乳头瘤病毒自动视觉评估(PAVE)联盟正在验证一种宫颈筛查策略,该策略能够在资源有限的环境中进行准确的宫颈筛查。一种快速、低成本的人乳头瘤病毒检测方法可以对自行采集的阴道样本进行敏感的人乳头瘤病毒检测;人乳头瘤病毒检测呈阴性的女性可以放心。对阳性结果的分流结合了人乳头瘤病毒基因分型(按癌症风险顺序分为四组)和通过自动宫颈视觉评估(AVE)辅助的视觉检查,AVE将宫颈外观分为严重、不确定或正常。这两者结合起来可以预测哪些女性患有癌前病变,从而对最需要治疗的女性进行有针对性的管理。我们分析了来自九个临床地点(巴西、柬埔寨、多米尼加共和国、萨尔瓦多、斯威士兰、洪都拉斯、马拉维、尼日利亚和坦桑尼亚)的每个PAVE风险水平(人乳头瘤病毒基因型与AVE分类交叉)的CIN3+检出率。来自1832名HPV阳性参与者的数据证实,HPV基因型和AVE分类各自都能强烈且独立地预测组织学CIN3+的风险。这些低成本检测方法的结合提供了出色的风险分层,值得在实施前开展示范项目。